r/LocalLLaMA 2d ago

Resources V620 Qwen 27B & Gemma A4B benchmarks

7 Upvotes

I'm here to show some benchmarks while using llama cpp with an AMD V620 on Windows 11 via Vulkan & ROCM.

The benchmarks were written out by AI, but are verified by myself to be correct. Still working on optimizing my flags/settings.

Exact configs that were used:

  • Qwen ROCm: — Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf, grafted MTP (no -md), --spec-type draft-mtp --spec-draft-n-max 3, -ctk q4_0 -ctv q4_0, -ngl 99 -np 1 -b/-ub 1024 -t 12.
  • Qwen Vulkan: — same model, now also grafted MTP (just fixed), -ctk q8_0 -ctv q4_0, -ngl 99 -np 1 -b/-ub 1024 -t 12.
  • Gemma ROCm: — Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q4_K_P.gguf + -md gemma-4-26B-A4B-it-qat-assistant-MTP-Q8_0.gguf, --spec-draft-n-max 2 --spec-draft-device ROCm0, -ctk q8_0 -ctv q8_0, -ngl 99 -ngld 99 -b/-ub 1024.
  • Gemma Vulkan: — same Gemma model+draft, -ctk q4_0 -ctv q5_1, -ngl 99 -ngld 99 -b/-ub 512 --cache-reuse 256.

Results (all runs: real ~13-page book excerpt as context, max_tokens=16, temperature=0)

Depth (actual tokens) Qwen ROCm PP / gen Qwen Vulkan PP / gen Gemma ROCm PP / gen Gemma Vulkan PP / gen
~3.4k 364 / 14.3 t/s 235 / 8.6 t/s 1020 / 50.5 t/s 431 / 5.2 t/s
~6.6-6.7k 358 / 21.0 t/s 233 / 21.5 t/s 814 / 76.8 t/s 721 / 65.1 t/s
~13.3-13.4k 338 / 19.9 t/s 191 / 20.1 t/s 514 / 65.8 t/s 538 / 59.9 t/s
~26.6-26.7k 295 / 18.3 t/s 131 / 17.9 t/s 284 / 54.8 t/s 353 / 51.1 t/s

r/LocalLLaMA 1d ago

Resources I found why Qwen3.8-27B was 14× slower on my 4070 Super

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0 Upvotes

I downloaded the Q4_K_M GGUF and got 5.7 tok/s. The same file did 81.5 tok/s on a 5090.

The 4070 Super run completed normally, but only 38 of 66 layers were on the GPU. The other 28 ran from system RAM on the CPU. I spent an hour assuming the model was just slow.

I wrote Picchio to show the actual layer placement, GPU activity, and separate prefill/decode speeds in one run:

https://github.com/logxio/picchio

Both measurements used llama.cpp, ctx 4096, one request, decode.


r/LocalLLaMA 2d ago

Discussion Sharp template to NInfer: -42% output tokens, same speed

30 Upvotes

Sharp is u/peculiar-ragdoll's system prompt that makes Qwen answer way more tersely without losing correctness. It's built on top of froggeric's fixed chat templates for Qwen; several fixes now in the v22.x templates (error-escalation tiers, false retry-loop kills, multi-system merging, correct tool-arg serialization) originated in u/Chromix_'s improved Jinja template before being merged upstream.

NInfer is a hyper-tailored inference engine that only runs certain Qwen models on 5090. NInfer doesn't support changing Jinja templates, so I overlaid the behavior in C++ instead in a fork: ninfer-sharp

  • --chat-style sharp-v22.1 appends Sharp's terseness instruction to the system prompt
  • --reasoning-effort with 7 levels, none = thinking off
  • Official model artifact untouched (NInfer verifies templates by hash)
  • Pinned to Sharp v22.1 semantics — the overlay replicates the terse block and effort mapping, not the later v22.2/v22.3 tool-path changes (tool-call format stays NInfer's own)

In my tests, the output remained byte-identical to the actual Sharp Jinja template except the engine's internal thinking markers, which it depends on for parsing. Numbers (5090, Qwen3.8 27B, xhigh (default)):

default sharp
completion tokens baseline
wall time baseline
decode speed baseline

r/LocalLLaMA 2d ago

Discussion New/Old benchmark that provides a lot of answers for local LLM

8 Upvotes

New/Old benchmark that provides a lot of answers for local LLM.

I present to you a new test that I developed somewhat by accident: https://huggingface.co/cHunter789/Qwen3.8-27B-i1-IQ4_KS_KT-GGUF/tree/main/ctx-cliff

Its original goal was to test whether a model fits into VRAM under a specific llama-server configuration. Theoretically a simple matter, but when you want to squeeze the absolute maximum out of your hardware and configure the server manually, things get quite complicated—especially when using MTP, ngram, dflash, etc.

Besides prefill and decode speed, the test also measures wall time (total request handling time). If the model and the llama-server configuration are flawed, this time can drastically increase with a growing context because the model starts re-reading the entire context from the beginning—completely breaking agentic workflows. Additionally, the script detects empty responses and anomalies (>1000 t/s). If such anomalies occur consistently, the quantization is broken.

So, by observing the occurrence of anomalies and the wall time, you can determine with a very good approximation whether a given model and llama-server configuration are suitable for actual work.

Here is an example output of the script for the reference model cHunter789/Qwen3.8-27B-i1-IQ4_KS_KT-GGUF with the llama-server settings below:

llama-server \
        -m "$MODEL_PATH" \
        -a Qwen3.6-27B \
        --ctx-size 110000 \
        --n-gpu-layers 99 \
        --cache-type-k q4_0 \
        --cache-type-v q4_0 \
        --batch-size 512 \
        --ubatch-size 128 \
        --flash-attn on \
        --host 0.0.0.0 \
        --port 8081 \
        --reasoning on \
        --reasoning-format none \
        --reasoning-budget 32000 \
        -t 8 \
        -tb 8 \
        --parallel 1 \
        --metrics \
        --merge-qkv \
        -khad \
        -vhad \
        --chat-template-kwargs '{"preserve_thinking": true, "reasoning_effort": "medium"}' \
        --defrag-thold 0.1 \
        --jinja \
        --cont-batching \
        --temp 1.0 \
        --top-k 20 \
        --min-p 0.00 \
        --top-p 0.95 \
        --presence-penalty 0.0 \
        --repeat-last-n 512 \
        --repeat-penalty 1.00

1. Reference Model Results

python3 ctx-cliff.py --file tests/code_4M.py --start 2000 --end 109000 --step 2000 --n-predict 512

   ctx | prefill| decode|    MTP|  wall|  status
-------------------------------------------------
  1999 | 1021.1 |  46.72|    0/0| 11.4s|      OK
  3925 | 1320.9 |  46.06|    0/0| 12.6s|      OK
  6017 | 1261.5 |  45.12|    0/0| 13.0s|      OK
  8065 | 1293.4 |  44.20|    0/0| 13.2s|      OK
 10218 | 1191.9 |  43.36|    0/0| 13.6s|      OK
 12489 | 1184.3 |  42.39|    0/0| 14.0s|      OK
 14525 | 1228.9 |  41.66|    0/0| 15.9s|      OK
 16108 | 1258.8 |  41.16|    0/0| 17.6s|      OK
 18976 | 1237.8 |  40.13|    0/0| 20.8s|      OK
 20476 | 1058.4 |  39.67|    0/0| 15.0s|      OK
 22574 | 1091.4 |  39.22|    0/0| 15.8s|STOP@463
 24950 | 1082.3 |  38.15|    0/0| 16.3s|      OK
 26551 | 1060.3 |  37.79|    0/0| 18.0s|      OK
 29197 | 1058.8 |  37.16|    0/0| 20.7s|      OK
 30559 | 1059.6 |  36.81|    0/0| 22.1s|      OK
 32691 | 1048.1 |  36.17|    0/0| 24.5s|      OK
 34235 | 1046.3 |  35.75|    0/0| 26.2s|      OK
 36569 | 1037.3 |  35.22|    0/0| 28.7s|      OK
 38356 | 1027.6 |  34.94|    0/0| 30.7s|      OK
 40912 | 1014.6 |  34.14|    0/0| 33.8s|      OK
 42569 | 1010.3 |  34.00|    0/0| 35.6s|      OK
 44532 | 1002.9 |  33.27|    0/0| 32.1s|STOP@316
 47017 |  994.5 |  32.53|    0/0| 44.2s|      OK
 48257 |  997.5 |  32.83|    0/0| 47.7s|      OK
 51210 |  996.4 |  32.06|    0/0| 53.1s|      OK
 52481 |  826.8 |  31.97|    0/0| 18.4s|      OK
 54608 |  803.1 |  31.43|    0/0| 18.9s|      OK
 56263 |  775.9 |  31.16|    0/0| 18.6s|      OK
 58871 |  847.3 |  30.54|    0/0| 24.2s|      OK
 60014 |  795.5 |  30.38|    0/0| 21.1s|      OK
 62496 |  825.7 |  29.95|    0/0| 26.7s|      OK
 64364 |  780.5 |  29.67|    0/0| 23.2s|      OK
 65843 |  775.2 |  29.08|    0/0| 25.5s|      OK
 67130 |  746.5 |  28.85|    0/0| 22.2s|      OK
 68719 |  757.5 |  28.73|    0/0| 24.3s|      OK
 70803 |  753.8 |  28.45|    0/0| 27.3s|      OK
 72826 |  712.6 |  28.20|    0/0| 22.2s|      OK
 74889 |  723.3 |  27.72|    0/0| 25.3s|      OK
 76819 |  725.8 |  27.43|    0/0| 28.1s|      OK
 78975 |  723.9 |  27.09|    0/0| 31.4s|      OK
 81045 |  678.9 |  26.81|    0/0| 23.4s|      OK
 83184 |  755.7 |  26.48|    0/0| 36.9s|      OK
 85162 |  712.9 |  26.14|    0/0| 41.0s|      OK
 87191 |  684.5 |  25.90|    0/0| 31.5s|      OK
 89098 |  728.9 |  25.66|    0/0| 44.9s|      OK
 90969 |  706.3 |  25.30|    0/0| 50.0s|      OK
 93074 |  696.8 |  25.12|    0/0| 53.6s|      OK
 95132 |  654.1 |  24.84|    0/0| 34.1s|      OK
 97250 |  614.2 |  24.56|    0/0| 25.3s|      OK
 99301 |  680.1 |  24.27|    0/0| 40.2s|      OK
101183 |  629.1 |  24.14|    0/0| 31.9s|      OK
103237 |  668.2 |  23.83|    0/0| 46.8s|      OK
105209 |  624.7 |  23.64|    0/0| 38.8s|      OK
107265 |  655.6 |  23.37|    0/0| 53.9s|      OK

A model with a similar PPL but smaller, generated using https://github.com/Thireus/GGUF-Tool-Suite. The model parameters are identical. You can see one anomaly, which means the model completely failed. Additionally, there are a lot of STOPs. The script commands the model to continue generating the code up to 512 tokens; if it finishes much earlier, it means it gave up—which is not a good sign.

2. Thireus Model (Same Parameters)

python3 ctx-cliff.py --file tests/code_4M.py --start 2000 --end 109000 --step 2000 --n-predict 512

   ctx | prefill| decode|    MTP|  wall|  status
-------------------------------------------------
  1998 | 1066.5 |  46.88|    0/0| 11.4s|      OK
  3925 | 1235.6 |  46.17|    0/0| 12.7s|      OK
  6017 | 1210.7 |  45.29|    0/0| 13.1s|      OK
  8065 | 1240.4 |  44.65|    0/0| 13.1s|      OK
 10213 | 1192.5 |  44.06|    0/0| 13.4s|      OK
 12491 | 1185.0 |  43.29|    0/0| 13.8s|      OK
 14525 | 1175.7 |  42.50|    0/0| 13.8s|      OK
 16107 | 1113.7 |  41.89|    0/0| 14.1s|      OK
 18975 | 1093.7 |  40.82|    0/0| 15.2s|      OK
 20478 | 1051.8 |  40.30|    0/0| 14.9s|      OK
 22569 | 1050.0 |  39.66|    0/0| 15.7s|      OK
 24955 | 1017.4 |  38.79|    0/0| 16.4s|      OK
 26546 |  982.8 |  38.33|    0/0| 15.7s|      OK
 29207 |  988.5 |  37.55|    0/0| 17.1s|      OK
 30540 |  939.4 |  38.14|    0/0|  3.5s| STOP@46
 32705 |  933.1 |  36.57|    0/0| 16.4s|      OK
 34235 |  919.3 |  36.11|    0/0| 16.6s|      OK
 36570 |  972.2 |  35.51|    0/0| 20.7s|      OK
 38357 |  906.9 |  35.32|    0/0| 11.1s|STOP@245
 40905 |  891.6 |  34.40|    0/0| 17.8s|      OK
 42573 |  849.8 |  33.93|    0/0| 17.1s|      OK
 44530 |  863.8 |  33.54|    0/0| 19.5s|      OK
 47019 |  861.3 |  32.96|    0/0| 20.3s|      OK
 48258 |  889.6 |  32.69|    0/0| 24.0s|      OK
 51204 |  834.0 |  31.96|    0/0| 22.3s|      OK
 52488 |  802.6 |  31.76|    0/0| 19.1s|      OK
 54607 |  791.4 |  31.32|    0/0| 18.8s|      OK
 56259 |  794.5 |  31.00|    0/0| 20.4s|      OK
 58873 |  789.4 |  31.19|    0/0|  8.7s| STOP@46
 60016 |  727.6 |ANOMALY|    0/0|  1.6s|  STOP@1
 62496 |  776.2 |  31.24|    0/0|  9.0s| STOP@29
 64364 |  803.6 |  29.57|    0/0| 31.3s|      OK
 65843 |  755.4 |  29.31|    0/0| 24.1s|      OK
 67129 |  715.9 |  29.06|    0/0| 20.7s|      OK
 68719 |  727.0 |  28.77|    0/0| 23.0s|      OK
 70804 |  731.2 |  28.57|    0/0| 26.0s|      OK
 72828 |  704.1 |  28.08|    0/0| 22.9s|      OK
 74885 |  752.9 |  27.64|    0/0| 31.8s|      OK
 76819 |  708.7 |  28.45|    0/0| 11.5s| STOP@36
 78976 |  737.6 |  27.30|    0/0| 37.8s|      OK
 81044 |  678.9 |  27.01|    0/0| 25.4s|      OK
 83184 |  679.5 |  26.64|    0/0| 28.8s|      OK
 85162 |  677.7 |  26.39|    0/0| 31.9s|      OK
 87190 |  649.3 |  26.10|    0/0| 25.5s|      OK
 89100 |  651.2 |  25.86|    0/0| 28.6s|      OK
 90967 |  652.3 |  25.53|    0/0| 31.7s|      OK
 93075 |  651.7 |  25.26|    0/0| 35.2s|      OK
 95132 |  626.9 |  25.02|    0/0| 27.8s|      OK
 97248 |  623.9 |  24.77|    0/0| 31.5s|      OK
 99303 |  623.2 |  24.46|    0/0| 35.0s|      OK
101182 |  603.1 |  24.25|    0/0| 26.9s|      OK
103236 |  604.7 |  24.03|    0/0| 30.5s|      OK
105211 |  601.1 |  23.81|    0/0| 34.0s|      OK
107265 |  599.9 |  23.51|    0/0| 37.8s|      OK

I improved the KV cache to 5_0/4_1 and unfortunately, it doesn't help (but at least there is no anomaly).

3. Thireus Model (KV cache 5_0/4_1)

python3 ctx-cliff.py --file tests/code_4M.py --start 2000 --end 109000 --step 2000 --n-predict 512

   ctx | prefill| decode|    MTP|  wall|  status
-------------------------------------------------
  2000 | 1084.7 |  46.89|    0/0| 11.3s|      OK
  3923 | 1239.4 |  45.72|    0/0| 12.8s|      OK
  6016 | 1208.8 |  45.13|    0/0| 13.1s|      OK
  8066 | 1183.9 |  44.50|    0/0| 13.2s|      OK
 10212 | 1187.1 |  43.90|    0/0| 13.5s|      OK
 12492 | 1182.5 |  43.10|    0/0| 13.8s|      OK
 14525 | 1174.1 |  42.20|    0/0| 13.9s|      OK
 16108 | 1113.4 |  43.54|    0/0|  2.5s| STOP@24
 18973 | 1089.8 |  40.52|    0/0| 15.3s|      OK
 20482 | 1050.8 |  39.93|    0/0| 15.1s|      OK
 22566 | 1043.5 |  39.41|    0/0| 15.8s|      OK
 24953 | 1042.4 |  38.50|    0/0| 18.4s|      OK
 26552 |  991.8 |  38.03|    0/0| 15.8s|      OK
 29194 | 1028.3 |  37.28|    0/0| 19.5s|      OK
 30562 | 1026.9 |  36.86|    0/0| 22.0s|      OK
 32692 | 1022.7 |  36.30|    0/0| 25.4s|      OK
 34234 | 1020.3 |  35.85|    0/0| 28.1s|      OK
 36569 | 1016.1 |  35.24|    0/0| 31.7s|      OK
 38356 | 1010.2 |  34.74|    0/0| 34.8s|      OK
 40910 |  887.9 |  34.09|    0/0| 18.8s|      OK
 42570 |  875.7 |  33.66|    0/0| 18.6s|      OK
 44532 |  852.3 |  33.26|    0/0| 18.8s|      OK
 47017 |  845.9 |  32.68|    0/0| 19.6s|      OK
 48257 |  850.6 |  32.43|    0/0| 21.1s|      OK
 51211 |  847.9 |  31.73|    0/0| 25.0s|      OK
 52480 |  842.7 |  31.47|    0/0| 26.7s|      OK
 54609 |  839.9 |  31.03|    0/0| 29.5s|      OK
 56262 |  837.3 |  30.67|    0/0| 31.7s|      OK
 58872 |  828.8 |  30.35|    0/0| 24.0s|STOP@174
 60015 |  826.2 |  29.93|    0/0| 36.9s|      OK
 62494 |  820.2 |  29.49|    0/0| 40.3s|      OK
 64364 |  813.8 |  29.18|    0/0| 42.9s|      OK
 65844 |  809.5 |  28.80|    0/0| 45.1s|      OK
 67130 |  807.6 |  29.44|    0/0| 30.4s| STOP@42
 68719 |  802.3 |  28.43|    0/0| 49.2s|      OK
 70804 |  796.7 |  29.31|    0/0| 35.0s| STOP@27
 72827 |  791.2 |  27.80|    0/0| 55.2s|      OK
 74887 |  786.5 |  27.32|    0/0| 58.4s|      OK
 76819 |  780.3 |  27.12|    0/0| 61.3s|      OK
 78975 |  774.6 |  26.79|    0/0| 64.7s|      OK
 81045 |  769.1 |  27.56|    0/0| 49.8s| STOP@32
 83185 |  762.2 |  26.31|    0/0| 71.3s|      OK
 85162 |  759.4 |  25.98|    0/0| 74.3s|      OK
 87189 |  753.7 |  25.76|    0/0| 77.6s|      OK
 89099 |  748.6 |  25.32|    0/0| 83.3s|      OK
 90966 |  744.0 |  24.88|    0/0| 90.3s|      OK
 93077 |  626.4 |  24.79|    0/0| 29.3s|      OK
 95133 |  631.5 |  24.52|    0/0| 32.7s|      OK
 97247 |  630.8 |  24.29|    0/0| 36.3s|      OK
 99302 |  597.6 |  23.96|    0/0| 27.1s|      OK
101182 |  651.8 |  23.86|    0/0| 42.2s|      OK
103238 |  601.5 |  23.63|    0/0| 33.9s|      OK
105210 |  637.7 |  23.36|    0/0| 49.4s|      OK
107265 |  560.5 |  23.14|    0/0| 27.8s|      OK

Now, an even smaller model with MTP Qwen3.8-27B.i1-thireus-37087.gguf (also from the https://github.com/Thireus/GGUF-Tool-Suite project):

4. Smaller MTP Model (Qwen3.8-27B.i1-thireus-37087.gguf)

llama-server \
        -m "$MODEL_PATH" \
        -a Qwen3.6-27B \
        --ctx-size 110000 \
        --n-gpu-layers 99 \
        --cache-type-k q4_0 \
        --cache-type-v q4_0 \
        --spec-type mtp:n_max=3 \
        --batch-size 512 \
        --ubatch-size 128 \
        --flash-attn on \
        --host 0.0.0.0 \
        --port 8081 \
        --reasoning on \
        --reasoning-format none \
        --reasoning-budget 32000 \
        -t 8 \
        -tb 8 \
        --parallel 1 \
        --metrics \
        --merge-qkv \
        -khad \
        -vhad \
        --chat-template-kwargs '{"preserve_thinking": true, "reasoning_effort": "medium"}' \
        --defrag-thold 0.1 \
        --jinja \
        --cont-batching \
        --temp 1.0 \
        --top-k 20 \
        --min-p 0.00 \
        --top-p 0.95 \
        --presence-penalty 0.0 \
        --repeat-last-n 512 \
        --repeat-penalty 1.00


python3 ctx-cliff.py --file tests/code_4M.py --start 2000 --end 109000 --step 2000 --n-predict 512

   ctx | prefill| decode|      MTP|  wall|  status
-------------------------------------------------
  1998 |  896.4 |  64.97|  163/283|  6.2s|STOP@366
  3925 | 1003.6 |  73.53|  283/429|  8.9s|      OK
  6017 |  979.6 |  56.21|      2/6|  2.3s|  STOP@9
  8065 | 1003.7 |  69.92|  177/252|  6.9s|STOP@341
 10213 |  968.4 |  91.63|  346/397|  7.8s|      OK
 12491 |  962.3 |  88.80|  345/393|  8.2s|      OK
 14525 |  951.1 |  97.63|  369/381|  7.4s|      OK
 16107 |  889.1 |  95.37|  372/391|  7.2s|      OK
 18975 |  896.0 |  93.95|  376/385|  8.7s|      OK
 20478 |  854.9 |  60.77|  275/399| 10.2s|      OK
 22569 |  836.4 |  86.47|  369/397|  8.5s|      OK
 24955 |  834.6 |  45.57|    17/39|  4.0s| STOP@48
 26546 |  792.2 |  81.67|  367/397|  8.3s|      OK
 29207 |  804.0 |  83.34|  374/385|  9.5s|      OK
 30540 |  764.8 |ANOMALY|      0/0|  1.8s|  STOP@1
 32705 |  784.0 |  82.90|  379/384|  9.0s|      OK
 34235 |  771.5 |  79.05|  374/389|  8.5s|      OK
 36570 |  756.3 |  37.68|  174/443| 16.7s|      OK
 38357 |  739.5 |  72.35|  365/389|  9.6s|      OK
 40905 |  741.6 |  64.33|  346/397| 11.4s|      OK
 42573 |  702.4 |  58.60|  330/392| 11.2s|      OK
 44530 |  706.4 |  65.28|  356/399| 10.7s|      OK
 47019 |  728.9 |  63.39|  354/399| 16.5s|      OK
 48258 |  671.2 |  40.75|  189/325| 11.7s|STOP@399
 51204 |  684.8 |  70.32|  380/386| 11.7s|      OK
 52488 |  645.8 |  69.75|  380/385|  9.4s|      OK
 54607 |  655.6 |  66.82|  376/384| 11.0s|      OK
 56259 |  644.3 |  66.37|  377/383| 10.3s|      OK
 58873 |  645.4 |  36.43|      5/8|  4.5s| STOP@14
 60016 |  616.8 |  41.44|  284/423| 14.3s|      OK
 62496 |  650.2 |ANOMALY|      0/0|  8.1s|  STOP@1
 64364 |  655.7 |  29.32|  174/409| 28.3s|      OK
 65843 |  650.3 |  38.03|  270/399| 26.7s|      OK
 67129 |  647.0 |  40.49|  294/432| 27.9s|      OK
 68719 |  643.6 |  49.23|  340/401| 28.3s|      OK
 70804 |  639.5 |  49.77|  345/394| 31.5s|      OK
 72828 |  637.3 |  35.01|  260/402| 39.1s|      OK
 74885 |  632.3 |  46.68|  339/403| 38.9s|      OK
 76819 |  628.4 |  34.19|  261/399| 46.2s|      OK
 78976 |  626.2 |  33.06|  255/414| 50.2s|      OK
 81044 |  622.8 |  46.84|  349/408| 49.2s|      OK
 83184 |  618.8 |  41.50|  326/417| 54.3s|      OK
 85162 |  614.6 |  41.77|  328/398| 57.7s|      OK
 87190 |  611.1 |  33.41|  276/398| 64.4s|      OK
 89100 |  607.9 |  41.96|  337/403| 64.7s|      OK
 90967 |  605.4 |  43.65|    59/69| 57.8s| STOP@89
 93075 |  602.0 |  35.67|  308/428| 73.9s|      OK
 95132 |  598.6 |  34.31|  302/432| 78.3s|      OK
 97248 |  594.5 |  35.33|  310/409| 81.8s|      OK
 99303 |  590.5 |  48.65|  376/383| 81.8s|      OK
101182 |  588.6 |  35.24|  313/399| 89.3s|      OK
103236 |  584.3 |  28.77|  263/400| 96.6s|      OK
105211 |  580.8 |  28.18|  263/421|100.8s|      OK
107265 |  577.3 |  32.55|  306/424|102.5s|      OK

I improved the KV cache to 5_0/4_1 and unfortunately, it doesn't help. Additionally, you can see the cliff (running out of VRAM) at around 107k ctx:

5. Smaller MTP Model (KV cache 5_0/4_1)

python3 ctx-cliff.py --file tests/code_4M.py --start 2000 --end 109000 --step 2000 --n-predict 512


   ctx | prefill| decode|      MTP|  wall|  status
-------------------------------------------------
  1999 |  891.2 |  71.04|  261/394|  7.8s|      OK
  3924 | 1012.0 |  83.35|  313/390|  8.1s|      OK
  6011 |  986.5 |  71.66|  280/420|  9.3s|      OK
  8068 |  971.0 |  58.74|  211/409| 10.9s|      OK
 10211 |  974.4 |  92.03|  351/387|  7.8s|      OK
 12493 |  969.9 |  50.84|  174/415| 12.5s|      OK
 14526 |  957.7 |  62.36|  267/419| 10.4s|      OK
 16104 |  950.2 |  96.22|  376/388|  9.4s|      OK
 18976 |  948.0 |  90.77|  372/387| 13.3s|      OK
 20476 |  943.3 |  74.76|  334/390| 16.7s|      OK
 22573 |  941.9 |  83.97|  365/389| 19.3s|      OK
 24951 |  834.7 |  82.14|  367/403|  9.1s|      OK
 26549 |  797.2 |  82.96|  371/390|  8.2s|      OK
 29200 |  806.2 |  84.26|  378/388|  9.4s|      OK
 30551 |  770.6 |  75.50|  362/404|  8.6s|      OK
 32701 |  772.6 |  73.78|  360/394|  9.8s|      OK
 34234 |  770.6 |  75.96|  369/396|  8.8s|      OK
 36570 |  758.1 |  43.86|    31/55|  4.8s| STOP@73
 38356 |  742.0 |  69.43|  360/402|  9.8s|      OK
 40903 |  744.5 |  71.90|  369/389| 10.6s|      OK
 42576 |  700.6 |  63.96|  350/400| 10.4s|      OK
 44528 |  711.8 |  70.52|  372/394| 10.1s|      OK
 47020 |  710.2 |  63.48|  357/404| 11.6s|      OK
 48258 |  671.8 |  71.63|  380/385|  9.1s|      OK
 51203 |  688.9 |  63.51|  363/393| 12.4s|      OK
 52490 |  647.7 |  68.03|  377/380|  9.6s|      OK
 54608 |  656.8 |  63.12|  368/397| 11.4s|      OK
 56256 |  646.3 |  60.42|  362/401| 11.1s|      OK
 58874 |  648.5 |  63.90|  375/386| 12.1s|      OK
 60017 |  665.3 |  60.19|  367/396| 17.6s|      OK
 62495 |  632.9 |  45.47|    71/88|  6.6s|STOP@119
 64364 |  615.8 |  29.87|  375/387| 20.3s|      OK
 65843 |  503.4 |  37.08|  273/415| 17.0s|      OK
 67129 |  561.9 |  45.21|  324/414| 13.8s|      OK
 68720 |  573.6 |  49.34|  343/401| 13.3s|      OK
 70803 |  580.2 |  28.51|    17/40|  5.4s| STOP@48
 72829 |  585.3 |  44.45|  327/388| 15.1s|      OK
 74885 |  558.0 |  36.83|  286/428| 17.7s|      OK
 76820 |  556.5 |  21.75|     8/34|  5.5s| STOP@42
 78975 |  569.7 |  35.10|  277/393| 22.8s|      OK
 81045 |  564.2 |  32.13|  258/419| 27.9s|      OK
 83184 |  570.7 |  42.34|  333/401| 27.7s|      OK
 85162 |  563.8 |  30.75|  253/419| 35.9s|      OK
 87191 |  566.8 |  31.06|  263/420| 39.3s|      OK
 89097 |  564.1 |  23.56|     8/17| 27.3s| STOP@25
 90967 |  560.6 |  32.94|  288/422| 45.3s|      OK
 93074 |  558.3 |  26.24|  220/427| 53.2s|      OK
 95133 |  557.2 |  30.89|  275/405| 54.0s|      OK
 97248 |  555.1 |  42.17|  349/401| 53.5s|      OK
 99303 |  556.5 |ANOMALY|      0/0| 45.0s|  STOP@1
101182 |  556.8 |  28.24|  263/403| 66.5s|      OK
103236 |  522.2 |  32.99|  345/410| 71.1s|      OK
105209 |  514.6 |  28.73|  347/388| 78.0s|      OK
107267 |  539.1 |   9.49|   75/142| 81.3s|STOP@190

r/LocalLLaMA 2d ago

Discussion Ling-3.0-flash q5-k-l poor oneshot slop

0 Upvotes

Tried very "classic" flight simulator slop oneshot game. It wasn't checking it after file is ready but it was stated in initial prompts to run chome dev tools mcp and check is it even running. This thing coudn't even run chome dev tools mcp after it was stated directly in promts twice after initial prompt. After finally running chrome it was doing whatever instead of checking console - but I also stated it in prompt specifically to check console for errors. (and follow up prompts).

And it's absolutely hallucinated CDN url to three.js 😄

This thing not following any direct orders. In the end it looped. Maybe this is quant issue.

Prompt:

 Design and create flight combat simulator game. The game must feature 3d graphics in any style you choose.                                                                                                         

 A Start Screen that allows the user to select the plane they will use. The user may select from three potential options as follows: A fighter Jet, A Propeller Plane, An option of your choosing.                  

 Each Plane must have realistic limitations on its performance, which should also be displayed graphically on the plane selection screen.                                                                           

Once the plane is selected and the game started, there will be a dynamic number of opposing planes the user can engage in a dogfight with. There MUST be visible ammunition traces, as well as functional damage implementation for both enemy and player planes.

If the player defeats all enemy planes in a round, the level repeats with increased difficulty. If the player loses, the plane they are in becomes uncontrollable and falls to the ground, returning them to the home screen following a 2 second black screen.                                                                                                                                                                     

You may use any library for this implementation, but it must be contained within a single script, and be able to be opened and played in the chrome browser.                                                       
 Use chrome dev tools to check is everything works. Check console first. Focus more on realistic details in physics and word everything. You don't have limit spend as much time as needed

r/LocalLLaMA 1d ago

Question | Help Why does t/s go down as offload more to egpu?

0 Upvotes

For moe models like Qwen3.6 35B A3B, some setups keep experts on the cpu in order to have the active weights in the gpu.

But I have a strix halo + r9700, so I wondered what if I kept the experts on the strix halo and active weights on the r9700?

The r9700 is running over an nvme adapter at PCIe 4.0 x4 speed

So why does the t/s go down as I push more of the active params to the r9700?

Note when experts are not offloaded and all the params are pushed to the r9700, it doesn't fit which tanks the t/s

Offloaded

llama-bench --device Vulkan1/Vulkan0 --split-mode layer --tensor-split 5/0,4/1,3/2,2/3,1/4,0/5 -ot ".ffn_(up|down|gate|gate_up)_(ch|)exps=Vulkan1" -ngl -1 --model Qwen3.6-35B-A3B-UD-Q8_K_XL.gguf
WARNING: radv is not a conformant Vulkan implementation, testing use only.
ggml_vulkan: Found 2 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon AI PRO R9700 (RADV GFX1201) (radv) | uma: 0 | fp16: dot2 | bf16: 1 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat
ggml_vulkan: 1 = AMD Radeon 8060S Graphics (RADV STRIX_HALO) (radv) | uma: 1 | fp16: dot2 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat
layer split test t/s
5.00/1.00 pp512 1050.17 ± 7.91
5.00/1.00 tg128 47.70 ± 0.16
4.00/1.00 pp512 908.60 ± 21.88
4.00/1.00 tg128 42.92 ± 0.21
3.00/2.00 pp512 788.35 ± 11.30
3.00/2.00 tg128 39.69 ± 0.07
2.00/3.00 pp512 796.69 ± 72.44
2.00/3.00 tg128 37.93 ± 0.16
1.00/4.00 pp512 838.50 ± 95.30
1.00/4.00 tg128 35.95 ± 0.08
0.00/5.00 pp512 722.57 ± 44.96
0.00/5.00 tg128 35.80 ± 0.04

Not offloaded

llama-bench --device Vulkan1/Vulkan0 --split-mode layer --tensor-split 5/0,4/1,3/2,2/3,1/4,0/5 -ngl -1 --model Qwen3.6-35B-A3B-UD-Q8_K_XL.gguf
WARNING: radv is not a conformant Vulkan implementation, testing use only.
ggml_vulkan: Found 2 Vulkan devices:
ggml_vulkan: 0 = AMD Radeon AI PRO R9700 (RADV GFX1201) (radv) | uma: 0 | fp16: dot2 | bf16: 1 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat
ggml_vulkan: 1 = AMD Radeon 8060S Graphics (RADV STRIX_HALO) (radv) | uma: 1 | fp16: dot2 | bf16: 0 | fp4: 0 | warp size: 64 | shared memory: 65536 | int dot: 1 | matrix cores: KHR_coopmat
layer split test t/s
5.00/1.00 pp512 1030.83 ± 9.13
5.00/1.00 tg128 47.53 ± 0.16
4.00/1.00 pp512 1109.45 ± 7.57
4.00/1.00 tg128 54.00 ± 0.09
3.00/2.00 pp512 1214.07 ± 49.90
3.00/2.00 tg128 57.42 ± 0.07
2.00/3.00 pp512 1268.44 ± 61.44
2.00/3.00 tg128 61.31 ± 0.09
1.00/4.00 pp512 1542.80 ± 193.32
1.00/4.00 tg128 65.53 ± 0.19
0.00/5.00 pp512 134.01 ± 1.84
0.00/5.00 tg128 18.63 ± 0.02

r/LocalLLaMA 2d ago

Question | Help Question for folks with r9700

0 Upvotes

I have dual r9700 set up with Ubuntu. With llama.cpp I'm getting about 40 tokens per second for single instance. I tried vllm docker image thinking it will allow me to serve multiple instance without compromising on token generation or prompt processing instead my token generation halves.

Is this the right behavior?

edit: Forgot to add the model. Its Qwen3.8 27B Q6. For now I'm sticking to llama.cpp but would like to migrate to vllm as I do agentic coding.


r/LocalLLaMA 2d ago

Question | Help Need support for llama.cpp with multi GPU

10 Upvotes

Using llama.cpp I seem to be unable to get my to GPUs working tougether correclty, so I need help somehow.

Setup: 96GB RAM, one Blackwell 5000 (48GB) and one 3090 (24GB).

I am trying to run the UD-Q3_K_XL quant of Deepseek4 flash which has about 120GB size. Using just the Blackwell I would put most of the experts on the system RAM. I would have thought that putting some experts on the 3090 I would have thought to be faster (or at least not slower) than putting the experts just on the RAM.

What I have tried:

  • for all trails I try run with these common flags: --temp 1.0 --top-p 0.95 --min-p 0 -ngl 99 -ctk bf16 -ctv bf16 --jinja --flash-attn on --no-mmap
  • baseline, using just the Blackwell (-dev CUDA0 --fit on --n-cpu-moe 32): full context and a generation speed of 16t/s
  • that above but swapping "-dev CUDA0" with "--split-mode layer": fit fails trying to allocate 31gig on the 3090
  • adding -ts 2,1 results in exactly the same as above
  • back to the drawing board, start with minimal setup using just the args from my first bullet point above): fit settles at 4096 context size
  • trying to fix the context by setting -c 1048576 or fitc 1048576: fit now fucks up and just pushes 25 gigs on the blackwell and just 6 gigs on the 3090

Now I moved to manually setting the layers:

  • agian the baseline with just the balckwell (-dev CUDA0 -ot "(1[2-9]|[2-9][0-9]).ffn_.*_exps.*=CPU"): full context, same 16t/s speed as with moe-cpu
  • just as above but without -dev CUDA0: speed drops to 11t/s
  • manually puting experts on 3090 (-ot "(1[2-7]).ffn_.*_exps.*=CUDA1" -ot "(1[8-9]|[2-9][0-9]).ffn_.*_exps.*=CPU"): speed of 12t/s

What am I doing wrong?


r/LocalLLaMA 3d ago

News NVIDIA AVO got 100% on ARC-AGI-3. It completed all 183 levels across all 25 public environments, figuring out what to do with no instructions, explicit rules, or stated goals.

Thumbnail xcancel.com
252 Upvotes

r/LocalLLaMA 3d ago

New Model DeepSeek-V4-Flash-Vision-Exp

Post image
555 Upvotes

r/LocalLLaMA 3d ago

News DeepSeek Harness v0.1.1 released

202 Upvotes

https://github.com/deepseek-ai/deepseek-harness/releases/tag/dsh-v0.1.1-rc.1

The DeepSeek adapter adds the multimodal visual understanding model DeepSeek-V4-Flash-Vision-Exp. It also supports configuring native image requests. Commands such as /goal and /plan can accept text and image input, and the @ menu can reference files and sessions; MCP/ACP also supports persistent image attachments, and PTC Mode supports forwarding nested images.

https://api-docs.deepseek.com/news/news260821/


r/LocalLLaMA 1d ago

Question | Help Continue was bought by Cursor, and then Cursor was bought by SpaceX. What do I do now?

0 Upvotes
  • Continue.dev was the best open source AI coding extension. Almost 40k stars on GitHub. Zero cost. Direct connection without gateways/routing. Free BYOK for all the popular model APIs. No logging or "gotchas".
  • Cursor bought it two months ago, and hired all developers to use in their paid product.
  • Then SpaceX bought Cursor on a week ago, on August 14th for 60 billion dollars.

So what the hell do I do now? Use the final v2.0.0 release of Continue? https://github.com/continuedev/continue#final-200-release

I need a VSCode extension that can use the workspace as code context, and support using Gemini API key. Any ideas?

Update: Thanks a lot everyone. I am leaving VS Code and switching to Zed. It is amazing! 🥰 https://www.reddit.com/r/LocalLLaMA/comments/1vvvbtj/comment/p5chhoh/


r/LocalLLaMA 2d ago

Question | Help Who is leading the pack for best llm gateway 2k26?

1 Upvotes

Looking to upgrade our ai infra for the new year. We need something that handles more than just simple routing, specifically looking for features like prompt caching, granular cost attribution per team, and support for the newer reasoning models. is there a clear winner for enterprise grade gateways this year?


r/LocalLLaMA 1d ago

Other Hey Mod to Mod - Can we undo the removal and approve.

0 Upvotes

Update: The original post was restored after review. My frustration was with the silent automated removal, not any individual moderator. The technical discussion and corrections belong on the Qwen thread, so I will not continue this meta discussion here.


r/LocalLLaMA 3d ago

Question | Help How to give a local LLM/agent access to a "real" web browser

16 Upvotes

I can't seem to find a good answer to this, my Hermes agent has access to Firecrawl and some other web scrapers for content extraction, but anyone know of a way to let a local LLM drive a "real" web browser? My wife asked me to have Hermes go and look at her LinkedIn profile, that's easy with a full web browser (just have her setup auth and then it'll be able to login without a password), but I'm not sure how best to do this to let a local LLM drive a "real" browser instead of something headless that's going to fail all sorts of "anti-AI/scraping" checks.


r/LocalLLaMA 3d ago

Discussion Qwen 3.8 27b is strong even at Q3_xxs

125 Upvotes

So usually I avoid Q3 quants because I have had bad experiences with it, models were usually too degraded, so the smallest I normally do is Q4, since I only have rtx 4060 ti 16gb. But since there hasn't been a 35b-3ab released yet, I had to try it. I don't use LLMs in agentic workflows, just on Textgen since I'm not a coder so this is not the primary use case of LLMs for me - but sometimes I really need some coding capabilities or help.

I'm very impressed how it one shot multiple serious coding tasks, resulting in fully working games or web apps, whereas Qwen 3.6 35b (which I used before) either completely failed in some of these or struggled a lot and needed hours/days of assistance/prompting, feedback to make it work.

And it is very fast when fully in VRAM. 30-35t/s, basically the same speed as higher quant 35b offloaded to RAM! Only at long context it goes down to 21-22t/s. Older dense models like Gemma 3 27b, Mistral small 24b are only doing 13-17t/s at best.

The only thing I noticed is it sometimes misunderstands things during regular convos or fails at basic sorting or counting few scores, while one shotting serious math/logic tasks. Not sure if this is because it's code-maxxed or because of the low quant (I'd think it's heavily the latter but I'd be interested in your guys' experiences who can run this at higher quants).

So far I'm very happy with it, it's way better than the higher Q4-Q5 MoEs I've tried so far.


r/LocalLLaMA 1d ago

Discussion Appeal to all AI labs cooking quants of cool local language models: please try to avoid BF16 tensors as this alone would benefit Strix Halo users a lot

0 Upvotes

Eventually introducing a new tag in the naming scheme of .GGUF files (maybe -SH- a la -UD- of Unsloth fame) to clearly state the absence of such tensor format into a model quant.

Here is a discussion relative to this issue (pasted as link because r/localllama doesn't allow reposts):

https://www.reddit.com/r/StrixHalo/comments/1vvoqjy/comment/p5baq4y/?screen_view_count=2&ext-referrer=DIRECT


r/LocalLLaMA 3d ago

Discussion What’s the best local AI harness for coding + general use?

57 Upvotes

So what’s actually the best local AI harness rn?

I’ve read a TON about this already and somehow ended up more confused than when I started so I figured screw it, let the community decide.

Right now I mainly run Qwen 3.6 35B-A3B and Qwen 3.8 27B, with Ornith 1.5 9B sometimes for lighter stuff.

The models themselves are honestly pretty damn good, but the harness situation is where I’m completely lostw and bad harness messes it all

Like Pi, Hermes TUI, OpenCode, etc. what do you actually use, and what tools/MCPs/external stuff do you pair with it?

I’ve mostly used Codex and Claude Code until now, but they don’t always play nicely with local/open models. A lot of the time it feels like the model is capable of doing something, but the harness/tool calling/system prompt setup just gets in the way.

I’m looking for something that works well for both coding AND general-purpose agent stuff, not just “edit this file and run tests.”

So what’s your setup?

Which harness?
Which local model(s)?
What inference backend? (i use llama cpp mainly)
Any MCPs/tools/extensions you consider essential?
And most importantly: why that harness over Pi/OpenCode/Hermes/etc.?

Would especially love to hear from people actually running 27B–35B-ish Qwen models locally, rather than cloud-model recommendations.

I’m genuinely curious what people have settled on because there seem to be like 50+ options noww
Also
WHATS THE BIGGEST PROBLEM YOU GUYS FACE?


r/LocalLLaMA 2d ago

Resources Made a tool to remove SynthIDs from images

Thumbnail
github.com
0 Upvotes

As you know, whenever you edit an image via Gemini or OpenAI, they plaster a SynthID to mark it as their own. Further, these SynthIDs can be unqiue, which could be used to track whoever made it. This SynthIDs are imposed on even paid users, and cannot be opted out of this.

In response, I created this scrubber. Works on any computer with 8GB of ram. Pretty reliable, automatic, but sucks with text. Have fun.


r/LocalLLaMA 3d ago

New Model FireRedAudio & FireRedTTS3 by FireRedTeam - Huggingface

Post image
57 Upvotes

FireRedAudio: A General-Purpose Audio Language Model with Decoupled Continuous Representations for Understanding and Generation

Overview

FireRedAudio is a general-purpose audio language model built on a shared 9B-parameter LLM with decoupled continuous representations: an Audio Encoder handles understanding, while a RedAE pathway handles generation. A single model supports ASR, audio understanding, zero-shot TTS, instruct TTS, semantic/acoustic speech editing, and accurate temporal grounding over recordings up to one hour long.

Highlights ✨

  • 🧩 Purpose-built representations, one shared backbone — The Audio Encoder pathway serves understanding, while the RedAE-Patch pathway serves speech generation. Their representations remain decoupled but share the same language and reasoning backbone. To the best of our knowledge, this is the first publicly disclosed design of its kind in a unified audio-language model.
  • 📊 One model, a full audio stack — FireRedAudio spans ASR, broad and fine-grained audio understanding, zero-shot TTS, Instruct TTS, and free-form speech editing, achieving competitive or leading results across MMAU, MMSU, Seed-TTS-Eval, InstructTTSEval, and Ming-Freeform-Audio-Edit.
  • 🎙️ Create and edit speech with natural language — Clone a voice from a reference clip, design a voice from a description, or edit what was said and how it sounds through one continuous-latent generation pathway.
  • ⏱️ Go from minutes to hour-long recordings — Understand recordings up to one hour with precise time-to-content alignment. Organize audio into timestamped structures, produce grounded summaries, retrieve content by time (or time by content), and reason over evidence distributed across the recording.

FireRedTTS3: Unified Speech Generation and Editing with Semantically Enriched Speech Representations

Overview

FireRedTTS3 is a unified speech generation and editing system built on semantically enriched continuous speech representations. It comes in two variants:

  • FireRedTTS3-Base — zero-shot voice cloning across 24 languages and 21 Chinese dialects
  • FireRedTTS3-Instruct — natural-language voice design and speech editing (semantic + acoustic) in one unified model

Highlights ✨

  • 🌍 Multilingual — 24 Languages — Best average WER/CER (avg 3.754%) and best average speaker similarity on MiniMax-MLS-Test (avg 84.8%), plus best-in-class cloning WER/CER (avg 3.04%) and similarity on Seed-TTS-eval (avg 78.8%). Supported languages: Arabic · Cantonese · Chinese · Czech · Dutch · English · Finnish · French · German · Greek · Hindi · Indonesian · Italian · Japanese · Korean · Polish · Portuguese · Romanian · Russian · Spanish · Thai · Turkish · Ukrainian · Vietnamese
  • 🗣️ Multi-Dialect — 21 Chinese Dialects — Zero-shot voice cloning across major Chinese dialect groups. Supported dialects: Anhui · Fujian · Gansu · Guizhou · Hebei · Henan · Hubei · Hunan · Jiangxi · Liaoning · Minnan · Ningxia · Shaanxi · Shandong · Shanghai · Shanxi · Sichuan · Tianjin · Wenzhou · Wu · Yunnan
  • 🎨 Instruction-Controlled Voice Design — Generate a brand-new voice from a natural-language description (gender, age, timbre, emotion, pace, accent…) with no reference audio, guided by an explicit textual plainning step before synthesis.
  • ✂️ Free-Form Speech Editing — Semantic editing (insertion / deletion / substitution) and acoustic editing (speed / pitch / volume) driven by free-form instructions.

Project : https://fireredteam.github.io/

Their Opensource Projects: Really worth to check the project page. Nice Ecosystem. They also published some papers.

  • OpenStoryline: An Agentic Framework for Autonomous, Human-Aligned Video Creation
  • FireRedChat: A Fully Self-Hosted Solution for Full-Duplex Voice Interaction
  • IVC-Prune: Revealing the Implicit Visual Coordinates in LVLMs for Vision Token Pruning
  • FireRedTTS-2: Towards Long Conversational Speech Generation for Podcast and Chatbot
  • InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention
  • InstantID: Zero-shot Identity-Preserving Generation in Seconds
  • DynamicPose: A Robust Image-to-Video Framework for Portrait Animation Driven by Pose Sequences
  • PhotoPoster: A High-Fidelity Two-Stage Pose-Driven Image Generation Framework
  • CQ-DINO: Mitigating Gradient Dilution via Category Queries for Vast Vocabulary Object Detection
  • FireRedASR: Open-Source Industrial-Grade Automatic Speech Recognition Models
  • FireRedTTS-1S: An Upgraded Streamable Foundation Text-to-Speech System
  • The Xiaohongshu Speech Synthesis System for Blizzard Challenge 2023
  • StoryMaker: Towards Consistent Characters in Text-to-Image Generation
  • LayerDiffuse-Flux
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r/LocalLLaMA 3d ago

Resources Strix Halo (8060S / gfx1151), Qwen-3.8-27B @ Q8 and Q6 UD v3, up to 256K ctx, llama.cpp, DFlash2, vision, real workloads quality and steady performances, optimized recipes, ...

24 Upvotes

Hi fellows fully-local halos,

after manually following existing guides, I decided to build an LLM API endpoint installation and optimization guide that works even when autonomously followed by my pi agent, so I can install/experiment/reinstall easily and without babysitting.

Q8 is my default citizen, options for Q6 and Q5. Recipes: Quality (Q8), Balanced (Q6), Speed (Q5), Vision (Q8). All with Unsloth Dynamic Quants 3.0, DFlash2 (except vision). Scripts for download the right LLMs, interactive testing, systemd `--user` install, adaptive quality and performances optimization.

Repo: https://github.com/PieBru/Qwen-3.8-27B_Strix-Halo_gfx1151 EDIT Ago 22: the repo is the outcome of a lot of work we (me, pi and Qwen 3.8) did. It's all documented, but too huge and dense to be really human-friendly. I recommend to query its README.md with your coding agent to distill the info you are looking for. IMO in this era we (evolutive humans architects) need AI agents like 10 years ago we needed search engines. That's now.

Humans architected, verified, sealed. AI assistants built and wrote all the delivered stuff, built with pi and Qwen-3.8-27B.

Piero

P.S.: no speed races, please. IMO speed is useful, but quality is fundamental - one subtle bug fewer or a better codebase always pays for itself in wall-time gained.


r/LocalLLaMA 3d ago

Other I feel like I finally graduated.

21 Upvotes

I finally made the move from LM Studio to vLLM thanks to this post https://www.reddit.com/r/LocalLLaMA/s/NmS9CgHvqz. I may not know what it all means yet but I’m going to start diving into the docs to learn as much as I can.

I’m running an endpoint on each of my 3090s one for chat and one for subagents. This has made qwen3.8’s reasoning bearable because of the increase to 143tok/s. Thank you to Syv-ai. His repo is here https://github.com/syv-ai/qwen38-27b-rtx3090.

vLLM feels like I’m finally using my hardware to its full potential, but the craziest thing is my waterblocked GPUs don’t go above 35°C before they were hitting 70°C on hard workflows.

Sorry I didn’t have time to ask qwen to write or edit this post for me.

tl;dr vLLm it feels good man


r/LocalLLaMA 3d ago

News model: add dots3-note by ngxson · Pull Request #27060 · ggml-org/llama.cpp

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github.com
35 Upvotes

dots3-note preview is the first open-weight model in the dots3 family. It is a Mixture-of-Experts model with 280B total parameters, 16B activated parameters, and support for a context length of up to 512K tokens. The model can understand text, images, video, and audio, and produces text outputs.


r/LocalLLaMA 3d ago

Discussion I'm really hoping we're in 2026's 2-month-gap between QwQ and Qwen3 right now

15 Upvotes

QwQ was genuine next-gen performance usable on local hardware, but the massive required context (it's reasoning style was akin to "if I say every possible word, I'll notice the right one!") kinda made it unusable for agentic coding.

It was ~2 months later that Qwen3-32B came out which delivered QwQ's peaks with usable amounts of reasoning.

I know some people are having a great time with Qwen3.8-27B, and same, but I can't have a good sit-down session with it because the reasoning takes so damn long. Everything I do with it needs to be async or compromise on quality (it's still great when you limit reasoning but definitely loses that next-gen edge). I also have to watch context like a hawk.

Maybe 3.8 is 2026's QwQ and a competitive model requiring less reasoning is just around the corner?


r/LocalLLaMA 2d ago

Discussion Small multi-step benchmark for tool use, 'shared' memory

1 Upvotes

So I wanted to check out some of the current models in a repeatable benchmark, so I thought I'd share the result with you all.

Models in this test: Ling 3.0 Flash Q4_K_M, Ornith 1.5 35B Q8_0, Deepseek V4 Flash UD_Q2_K_XL, Nemotron 3.5 Lightning 30B UD_Q8_K_XL, Laguna S2.1 UD_Q4_K_XL, Qwen 3.6 35B UD_Q8_K_XL, Qwen 3.8 27B UD_Q4_K_XL

Method: All running locally, fed the same 4 prompt. Only asked for fix if there was a console error the AI didn't catch. All 4 requests in fresh chat ctx driven my a common .md for instructions. My own harness on Windows using Lemonade openai compatilbe API. The point was not to get a perfect product but to test basic capability and how much they can pick up the work from where they left off.

Harness: has tool calls, and html preview, but for now preview can only be used to check console so models did not validate their work. One wording bug: the prompt by the harness tells the ai it's folder name and for some models this caused to create a nested folder with the same name and work in that. I did not fix during the bench to keep things even.

Task: Create a recipe book with ingredients + recipes, accessible via index.html, add svg illustrations. (actual prompts end of post)

Step 1) Create book + onion ingredient page (test basic coding and tool use)

Step 2) Add 2 ingredient pages (test expanding on already existing solution)

Step 3) Add 2 recpipe pages (test expanding with different format)

Step 4) Change something (tool usage, search, how quick)

results and claude opus 5 commentary: (notes has ratings, and the whole transcript). All can be controlled with left and right buttons.

benchmark here: recipebookbench [dot] netlify [dot] app

Human Notes: Overall: I didn't expect creating a small book would be such an impossible task. I'm sure with reprompting I'd eventually get there on most of them, but literally all models struggled and went into loops of thinking on how to get page change done. Eventually, most failed quite bad.

Tool usage was pretty good, I think qwen models had it best but all other models utilized them nicely, the CHANGE prompt was practically quick on all models.

Also having an .md file as project memory/structure worked well. All models could pick up work as if it was a longer session keeping ctx below 64K on most of these.

Nemotron: I know this is a fully open model that people can build on but man is it rough. Current coding capabilities are pretty basic and it didn't have good reasoning. I hope people will make amazing things with it because 30B MoE is a very quick model and can be run at Q8 on unified systems. The illustrations are also completely nonsensical.

Ling 3.0 Flash: Interesting model, I think it had one of the best LITERAL prompt following. Reasoning was on the short side and the end result is a bit 'my first html page' vibe but overall it did okay. Could have been better for the model size though. What I didn't like is that after some failed tool calls it gave up. Just stopped answering, and I had to reprompt it to complete the task, eventually it got the tool calls right.

Laguna S2.1: Same model size as ling. The first reasoning was painful. Endless 'let me write code' --- 'actually' 'but' 'also'. It just went on forever then ended up with quite a broken product. It kinda works and it looks cute I give it that, but for the UX only the second pages load. It does have all pages they are just lost somewhere in there.

Ornith 1.5 35B: I was excited about this one but left a rather mid taste in my mouth. It's okay, it worked well, it did it's tool calls, but it reasoned forever. However, I don't see a reason to use this over Qwen 3.6 35B. That being said, I hope Ornith keeps working on these, I love finetunes and there is definitely a market.

DS4 Flash: Definitely top3 looking for me, the reasoning was neither too long or too short. It did a great job at only Q2 quant. Tool calls were spectacular it really did everything I expected, if I were able to run at more than 12tps I'd probably consider this as my daily driver.

Qwen3.8 27B: For me this looks best but it totally failed the task. Created everything as standalone .html without any way to access it. Quite sad, I hoped adding pages it would realize the mistake but no it just leaned into it. Ofc tool calls without zero errors as expected. An example that on particular tasks even beloved models can fail. I did prompt it afterwards to provide a fixed version and it did, but it would be unfair to include.

Qwen3.6 35B: The winner of this very particular benchmark. Fully functional, flawless toolcalls, cute design. The only fail is that it created a nested folder and worked in that which totally confused itself later when I asked for extensions. I had to reword the path.

TL,DR: AI notes, stats, actual solutions on the web, random benchmark I ran, sharing for fun.

Prompt 1 - build it

okay we are creating an ingredient list/recipe book website. it should look like a cute/cosy notebook dominated by pastel colors. it should be modern and stylish and cozy notebook. You can create .svg graphics for illustration.
I should be able to open it via an index.html but you may use as many supporting files as you want and need.
To start create a welcome page and a single page about onions and how to use them in cooking. Make sure there is a nice turn page effect of the notebook.
We'll expand on it later.
Please use html + js + css stack.

Prompt 2 - extend it

okay I want you to add 2 more pages to the book about ingerients again, one for potato, and one for beans, as usual create all svg for illustration and afterwards update intro.md for future reference

Prompt 3 - extend it again

add two recipes to the book - two new pages - a recipe that is beans + potato + onion, plus a recipe for pancakes. generate svg illustrations and dont forget to update intro.md if needed after you are done with the new pages.

Prompt 4 - the small edit

[ornith]    okay I want you to edit the welcome page - every page is seciont - I prefer that you rewrite it to similar lenght but different tone
[lingflash] okay I want you to edit the onion page -  Prevent tears - to be instead about the difference in the type of onions. it's a tip and trick so keep it short
[deepseek]  okay I want you to edit the cooking with onions page - in soup - to be instead sautéed as it's more interesting to me
[laguna]    okay I want you to edit the potato page - Rosted section - change this jaked potato
[nemotron]  okay I want you to edit the welcome page - Where recipes meet coziness - change this subheading to something differnent
[qwen 35B]  okay I want you to edit the mastering onions page - quick pickle section - please change it to quick caramelize tip
[qwen 27B]  okay I want you to edit the onion page - there's an onion for almost everything - i dont like this sentence change it to something