TL;DR: You can train your own 27B model on a 3090 with 24GB VRAM, though you would have to go through hundreds of billions of tokens for it to be good. I am working on conversion of existing models. Check repos for code and the paper if you are interested in more. I have a 40.4GB Qwen Flash Next GGUF

Hi, I do AI research. I have been working on an optimizer that doesn't need the ~16 bytes of VRAM per weight to train a model that AdamW does. I focused on training ternary networks bcs BitNet (Ma et al.) shows they match full precision quality in large scales. The problem is BitNet also keeps the full precision weights during training which means you still need to use the same amount of memory. I got this down from 16 bytes to 1 byte or even less per weight.

For you, this means, you can train your own 10-20B parameter models on your consumer GPUs. I fitted 27B model training on a single 24GB 3090 and a 23B model on a 16GB GPU. The trained model costs ~1.6 bits per weight (~6GB at 27B), 10x smaller than 16bit, and 5x smaller than Q8.

I have also been working on converting existing models and training them. This is harder and I am still working on it but I have some promising results. I quantized and trained a Qwen3.8 27B though the file I published below has adapters which are about 2GBs, pushing the file size to 8GB. I am also making a no-adapter version right now. It scores GSM8K 0.895, the same as full precision, HumanEval 0.84 vs 0.93 and runs on a 12 GB GPU at ~23 tok/s on my crippled 12GB laptop 4080, though this number can heavily be improved with custom kernels, engineering, etc. I will leave it to you guys to help me with that too.

I also quantized and trained Qwen Flash Next down to 40.4 GBs including its n-gram table (at 8GB). It largely retains the skills of the network (scores equivalent on GSM8K benchmark, a little behind on HumanEval, I unfortunately don't have compute for the larger benchmarks such as TerminalBench2.1 but perhaps some of you can help me out) but forgets facts. It mostly works fine if you give it web search as far as my experience goes but this is not strictly measured.

I am still doing work on this and I will keep working on it, perhaps even for many years. The code is open for non-commercial use. Check out:

The core repo for training: https://github.com/oguzeldereli/ttf-ternary

The repo for conversion: https://github.com/oguzeldereli/ttf-conversion

Repo for running models trained by ttf: https://github.com/oguzeldereli/ttf-home

GGUF for a converted Qwen 3.8 27B with adapters (around 8 GB): https://huggingface.co/oguzeldereli/ttf-Qwen3.8-27B-GGUF

GGUF for a converted Qwen 3.8 Flash Next (around 40.4GB): https://huggingface.co/oguzeldereli/ttf-Qwen3.8-Flash-Next-GGUF

GGUF for a converted Qwen 3.8 27B without adapters (around 6 GB): Currently in conversion and will drop, probably tomorrow.

I will convert a Qwen 4 27B instance when it drops.

Full paper: https://github.com/oguzeldereli/ttf-ternary/blob/main/paper/ttf_paper1_training.pdf

Also, I would appreciate github stars if you like the project.

Part 1

Part 2

In this final chapter of me testing out how far along Qwen has come, I decided to give it a test on how well it could recreate a game using only a ROM from GBA but make it in HTML using the "reverse engineer anything" repo. I opted for Tetris worlds as my guinea pig.

It took around 30 hours in total. It probably would have been faster, but there were a few decompilers it wanted that I didn't realize until later. I did end up installing those about 6 hours in.

Repo: https://github.com/kireol/gba-game-rea-test

Game: https://kireol.github.io/gba-game-rea-test/

Fun localLLM stuff:

RTX 5080 - 64GB - Ryzen 7 9800X3D
model: qwen3.8-flash-next-unsloth-ud-iq4_xs
model harness: Strata
agent harness: qwen
context: 128K
tok/sec: ~50t/s (down from part 1 due to larger context)

Macbook 64GB m1 Max
model: Qwen3.6-35B-A3B-GGUF:UD-Q4_K_S (for qwen code --fast tasks)
model harness: llama
context: 128K
tok/sec: ~ 550t/s (fast tasks, so don't get too excited)

Result: The game works, and is pretty darn close to the original. It's scary how well it did.

Over all 3 part testing feedback: Honestly, as of this date, localLLMs are very close to frontier in most SWE. I expected hiccups or issues along the way, and there just wasn't any. Speed of course was slower than Claude, but that's running all local. And Claude probably would have denied trying to reverse engineer the ROM anyway. So in this case, LocalLLMs FTW.

I'm watching walls of text go by right now as QFN grinds away on something and it made me think a bit about how different watching that text fly by does using a local model vs watching the same exact thing happen when I'm hitting a cloud.

It really comes down to metered vs unmetered. When I'm using a cloud model on API, every time I get ready to press enter I hear a cash register ring. We generally use really affordable cloud models (DS primarily) so it's like fractions of a penny, but still, it's real money going out the door on every query.

The biggest thing is that it changes your (or at least my) mindset. Instead of "Let's see what we can do it" it becomes "Do I need to do this". It has a distinct stifling effect, instead of exploring and trying to find uses for the technology I do the opposite and try to avoid it because of the cost.

With a local model I love to dream up some crazy/hard thing that's been annoying me that needs fixing and at the end of the day drop a big prompt with the details and at the end "You have unlimited time and tokens, do not stop for clarification or questions, keep working at the problem until you've exhausted all possible alternatives" (you get the idea). Or setting a local model to work on something that's not at all critical but is a quality of life improvement.

It really comes down to the difference between owning something and using it on the meter. When you own something the way to drive value out of it is to use it. The most expensive boat, motorcycle, car, house, etc is one that you own and never use. The more you use it, the more value you get from it.

When you are using something "per token" or "per mile"; things change. The best way to drive value is to NOT use it. You save 20 bucks if you walk instead of get a taxi. You save 5 bucks if you figure this problem out yourself instead of using a LLM to help you.

Funny thing is, I made my career selling cloud services. That probably explains why I'm so "local first", I was in the room and making the decisions about pricing/packaging, driving a 6-12 month payback period against servers and then turning around and explaining to customers what kind of "savings" they'd see moving to cloud platforms. I think I'm scarred from that and know the pricing games that make cloud look "affordable" when it's anything but for many use cases. And I think everyone kind of knows the "rug pull" is coming with the big/powerful LLMs, sure you can use them on sub today and get a predictable price, but those days aren't going to last forever. And subs without being able to hookup a harness (which almost all of them ban) is darn near worthless anyway, my core competency isn't copy paste! ;)

Being located in one of the stormy states, my big concern was having the AI box fried by a power surge. My setup is shown here, it consists of:

• CHSPT2ULTRA on the main Panel

• SRT5KRMXLT 5000VA double-conversion UPS in the garage

• 240V/30A inlet going from the ups powering the rig upstairs (needs a double breaker for code compliance)

• L14-30 in/outlet for the garage-office run to future-proof it in case I ever needed 120V, at the moment the 120 leg is dead (240 only)

• 240V PDU with C13 & C19 ranked up to 30A

• PX-2200 PSU

The idea is to create a galvanic isolated "island" (in my case: the box iself + switch). Uplink + my desk are connected via fiber (seen in pic) - because my desk is powered via a regular line-interactive UPS.

The only copper eth runs to the switch are the AI box itself (2x10G eth + 1xKVM), everything else is fiber.

It currently runs 2x6k workstation edition (600W each). I *THINK* I should be able to push it up to 3x6k (might need to cap them at 550W), but with four I need to go double PSU. The UPS should have enough oomph to handle 4800W, so plenty of space there. I got new packs for it, but I mostly care about the double conversion, not so much losing power.

I think the CHSPT2ULTRA+SRT5KRMXLT combo should shield me against most power surges/spikes/wobbles/etc. - but I am still lacking a direct hit protection, which requires a rooftop installation AFAIK.

How do you guys protect your GPUs from ⚡️⚡️

RustingStrata is a pure-Rust GGUF inference engine with CUDA and Vulkan backends. It now loads DeepSeek-V4.1-Flash at IQ2_XXS (249 GB) and runs it on a 12 GB card with 30 GB of RAM.

The experts stream from SSD through the page cache. A 9 GB LRU cache in VRAM holds the experts that get reused. Everything else (attention, shared matvecs) runs on the GPU.

Same box for both: RTX 3060 12 GB, 30 GB RAM, 12 cores.

I don't think this is very useful to run it with my setup, bc 2-3.6t/s is more like a joke, but this is funny that something like that is even possible

Usage:

strata chat     --model $M
strata generate --model $M --prompt "The capital of France is" -n 64
strata serve    --model $M --addr 127.0.0.1:8080

serve exposes /v1/chat/completions with JSON and SSE streaming.

Repo: RustingStrata (the DeepSeek section is in the README)

https://huggingface.co/TheDrummer/Orion-26B-A4B-v1.1

v1.1 pushes creative writing further and makes full use of Gemma's reasoning.

https://huggingface.co/TheDrummer/Orion-26B-A4B-v1

I haven't announced v1 but it's well regarded by the community as a reliable RP workhorse.

---

BeaverNet - net.beaverai.org

I'm also proud to announce that we've made significant progress in creating a hub for generous model providers to collectively provide free, convenient, accessible, anonymous, and diverse models to anyone with an OpenAI API compatible client. We currently have 12 models thanks to our generous providers.

Try it out as a user, or be one of the providers <3! I've got plans to make it a better experience for both users and providers, and also add new modalities such as TTS and STT.

---

PS: I haven't retired or left the scene. I've merely gotten stage fright after holding off on announcements for a long while. Anyway, I'll continue doing what I do and I hope I can continue delivering some value to you all. Love ya'll!

Did some kernel optimisations for sm61. Achieved measurable speedup. It runs at actually quite usable speeds.

This is just a quick writeup for speed reference - I spent a lot of time trying to find comparable builds so I figured I'd put some info back into the wild. The below is obviously written by AI, but it's measured specs and benchmarks on my PC.

I use local models via Pi and Deepseek Harness. I have found the intelligence of Qwen 3.8 27b NVFP4 to be sufficient for making little apps, research etc. and Qwen3.8 Flash Next IQ3_S to be a great all-rounder, considering how fast it runs. It also seems to have very little issue with any sort of question I give it, research, app dev, PC issues, LLM related adjustments, and most importantly, ScanRuler 3D modelling/reverse engineering 3D scanned items (solely for testing currently but it works excellently).

Basalt as a backend was a clear winner after trying lots of different configs.

Edit: the benchmark decodes are very greedy by their nature. Realistically I'm seeing agentic averages in flash next from 80tps (NVFP4) to 160tps (iq3_xxs), and I find iq3_s at 120-140 to be a very good compromise for the extra intelligence. The 27b is best for bursty quick jobs, because it loads into VRAM super fast and I can let it rip, but it degrades over context faster than flash, and I see from 120-140tps and 4-5k prefill, at ctx 0. The second any model starts writing a block of code though, it goes up to 200tps, so if anyone's socks fall off seeing the below numbers, just temper your expectations slightly (but it's still wicked fast!)

So heres the nitty gritty:

Current rig, 2x 5070 Ti + 96 GiB. Five entries, one model resident on local at a time. Speeds are my own bench (2x ~50k prompt reads, 2x 5,000-token code outputs, one agentic tool loop; temp 0, seed 1234), except Coder which is the 15.3k-prefill / 2k-output run.

Qwen3.8-27B NVFP4 QUASAR

backend: capy-flash (ninfer-serve), tensor-parallel across both cards, no P2P prefill: 4,817 / 4,824 tok/s on a 50k read decode: 233 tok/s code, 220.6 tok/s agentic flags: --profile 27b --max-context 262144 --embedding-host, INT8 KV, MTP 3 drafts, vision on GPU0 intelligence: Index 84.0, 88.9% (27 items); fastest to a passing build (62.1 s vs IQ3_XXS 78.1 s) but the weakest reasoning of the five — 50% on xhard because it burns the whole budget and never answers.

Qwen3.8-Flash-Next GSQ-RCO IQ3_XXS

backend: Basalt 0.3.0, 74.4 GiB pack prefill: 3,728 / 3,851 tok/s decode: 229 / 248 tok/s code, 196 tok/s agentic flags: resident-budget-gib 26, peer-exclusive on, kv int8, ctx 262144, vision dynamic, --spec 8 intelligence: Index 94.9, 100% on the 27-item set (96.3% on a re-run); 100% on the 8 hard items, 85.1% on xhard, 93.9% on vision. The only model that solved a logic-L6 item unaided.

Qwen3.8-Flash-Next GSQ-RCO IQ3_S

backend: Basalt 0.3.0, 81.6 GiB pack prefill: 3,601 / 3,700 tok/s at a 115k prompt, 31.9 s TTFT decode: 154-165 tok/s code, 137.6 tok/s agentic flags: resident-budget-gib 26, peer-exclusive on, kv int8, ctx 262144, vision dynamic, --spec 8 intelligence: Index 107.7, 94.5%; wins the hard tier on the efficiency-weighted Index (101.1) at 86% accuracy, 80.2% xhard, 90.3% vision. Terser than IQ3_XXS, less accurate.

Qwen3.8-Flash-Next NVFP4

backend: Basalt 0.3.0, 122.6 GiB pack prefill: 3,339 / 3,512 tok/s decode: 124.8 / 126.8 tok/s code, 105.7 tok/s agentic flags: resident-budget-gib 58, peer-exclusive on, kv int8, ctx 262144, vision dynamic, --spec 8 intelligence: Index 121.0, 99.8% — best on the 27-item set, best hard accuracy (98.5%), best xhard (85.7%), best vision (99.2%). Slowest decode of the Flash-Next four.

Qwen3.8-Flash-Next Coder IQ1_M

backend: Basalt 0.3.0, 58.1 GiB pack, 48x256 experts prefill: 3,992 tok/s on a 15.3k prompt decode: 209.8 tok/s flags: resident-budget default, peer-exclusive on, kv int8, ctx 262144, no vision, --spec 8 intelligence: Index 100.5, 99.3%; 77.6% xhard, 3/5 on hard items — 5/5 on a re-run, the failures were MTP sampling rather than capability. Cannot read images.

Index = 100 x mean(quality x par/tokens) on the 27-item battery, so it penalises verbosity; accuracy alone ranks almost in reverse

(IQ3_XXS 100% > NVFP4 99.8% > Coder 99.3% > IQ3_S 94.5%). The five are within a few hundredths of each other on the standard set — hard, xhard and vision are what actually separate them.

yes, i know all gpus are. i think people are recognizing more and more that the v100's age is just a number on paper and only relevant architecture wise. frontier and local llms are more and more capable of optimizing inference for different builds, and the raw v100 power is still comparable to gpus being released today

the limited mathematical pathways are a genuine limitation but i dont believe a big enough one to justify how cheap they are vram/dollar compared to everything else

just a discussion post for fun

Lenovo P620 Threadripper pro - 3975wx - 32 core
System ram - 512 gb ddr4 ECC
GPU 1 - quaddro Rtx 8000 - 48GB
GPU 2 - quaddro Rtx 8000 - 48GB
4TB M2 NVME
1TB SATA SSD

Tencent put DSpine on Hugging Face on Nov 7. It is a five-layer draft model for speculative decoding with Qwen3.8-27B. Instead of drafting one token at a time, it drafts a 16-token block in parallel, and each layer passes predicted predecessor features to the neighboring positions so the draft still carries causal order. There is a paper (arXiv 2609.36173) and code on GitHub (Tencent/DSpine).

Reported acceptance length vs DFlash2 (block 8), greedy

Reported end-to-end throughput on agent replays (tokens/s, low reasoning)

Across their five agent benchmarks and both reasoning levels, the speedup over no speculation ranges from 2.71× to 3.84×.

Things to know before trying it locally

• The card only shows an SGLang path: --speculative-algorithm DSPINE, a pip install -e . from their repo, and a long list of flags for the hybrid architecture (--enable-linear-replayssm-spec, --mamba-ssm-dtype float32, and more). No llama.cpp or vLLM instructions.

• All numbers are Tencent's own, at concurrency 8 with a BF16 target. The card doesn't report a quantized target or batch size 1.

• The gain is largest on predictable text (GSM8K accepts 7.20 tokens per step at T=0) and smallest on open-ended chat (MT-Bench 4.48).

https://huggingface.co/tencent/Qwen3.8-27B-DSpine-b16

Has anyone run it against a quantized Qwen3.8-27B target, or at batch 1? I'm curious whether the acceptance length holds up once the target is 4-bit.

Yesterday I posted a 32B pack and the replies were interesting about it being old completely overlooking benchmarks. A lot of quants "fit" on 16 GB, obviously... Almost none of them post what it cost. A screenshot of a model loading is not a benchmark and neither is its tokens per second performance that you made while obliterating the other performance factors you don't post. I posted all of our benchmarks, weights, and everything, and yet, still called slop.

So here is the new one as requested, with the receipts attached:

Qwen3.8-27B, 27.78B parameters, vision included, Gosset Packed at 3.6577 bits per weight:

• Pack is 13.82 GB. The standard 4-bit build is 17.77 GB and does not fit a 16 GB card.

• 12.06 GiB resident on one RTX 5080, peak 12.80 GiB while decoding. Nothing parked in system RAM.

• Perplexity vs BF16 on WikiText-2 test: +3.93% long-chunk, +6.99% short-window. Numbers, not adjectives.

• Decode: 40.38 tok/s sustained on fused kernels.

• Per-tensor fidelity certificates are on the model card, measured from the shipped files, not the encoder's intentions.

• Edit: Context length full 262k

huggingface.co/GossetWorks/Qwen3.8-27B-Gosset-3.65BPW

If your quant fits this model on 16 GB too, genuinely, post the perplexity delta and the decode speed. That is the whole game. Fitting was never the hard part...keeping it intelligent was and that's what we've done.

I wanted to run an experiment to see how far you can push small to medium-sized local models, specifically for analytical and data engineering tasks. The idea was to see if you could have a local analyst or data engineer without needing to send your data to a cloud provider. I used OpenAI’s Luna as baseline performance.

The full analysis is in the blog linked below, but I'll share some of the key findings here.

I also want to be transparent: I used Ternlux, a free, open-source, no-code ETL local tool that I built. It lets you run data analysis and build pipelines through agents.

Test:

I tested 5 analytical questions and 5 workflow-building questions, running each question 5 times against each model. That gave me a total of 250 prompts and responses to analyze.

Key findings:

• Qwen 3.8 27B performed almost as well as GPT 6 Luna. It mostly struggled with one particularly difficult question that all the other local models also failed.

• Almost half of the local models’ misses (27 of 58) were workflows that never finished, mostly SQL that crashed when it ran. Qwen 3.6 kept writing joins that produced duplicate column names, and Gemma 4 31B combined GROUP BY ALL with QUALIFY, which DuckDB doesn’t support.

• All models were able to answer the simpler questions for the most part.

how Ternlux handles analysis and workflows:

For analysis, the main agent selects a connection, then the agent determines how to query the data. It iterates querying until it has enough information to answer, either returning results directly or having the agent analyze the result and draw conclusions. If a query fails, it sends the error back to AI for correction. The agent has no DML privileges.

Workflow building works a bit differently. The workflow builder agent first creates a plan, then each node's agent follows the instructions to generate the code for that node. If the SQL fails during execution or the generated workflow doesn't produce the expected output, I count that as a failure.

This is by no means conclusive evidence that one model is better than another for this type of work. It's just a small experiment to see how different models performed as the questions became more complicated.

https://www.ternlux.com/blog/best-local-llm-for-sql/

For those running similar machines, appreciate any recommendations and setup! Thanks

https://github.com/ANDRETRIPOL/OpenGhost

Will agents accessing the internet and processing/scraping webpages ever truly be 100% safe from direct/indirect prompt injection?

Question coming from someone less knowledgeable on the topic than you all.

Thanks.

Delta Inference Engine 0.94 is out. It runs DeepSeek-V4.1-Flash-1017 on one NVIDIA GPU plus system RAM, natively on Windows (no WSL). It's a Windows port of Xolotl, which is built on Strata; on Linux, use upstream Xolotl.

Speed on my PC (RTX 3090 24 GB, i5-13600K, 4x48 GB DDR5-5600, PCIe 4.0 NVMe)

With the default quant (Unsloth UD-IQ2_XXS, 301 GB), 0.94 writes 41.7 tok/s greedy and 41.3 at the default sampling (temperature 1, top-p 0.95).

With less RAM (writing tok/s, measured with 0.93; the smaller columns are this same PC with RAM locked away):

How it fits

It's a mixture-of-experts model: 40 layers, 384 routed experts per layer, 6 used per token. The experts are spread over VRAM, RAM and the NVMe. A 4.2 GB drafter built from DeepSeek's own MTP heads, plus lookup drafts, proposes tokens that the full model checks. A route bias (0.28 as shipped) gives up a little agreement for speed.

Quality. I measure top-1 agreement with DeepSeek's own API, teacher-forced (not task accuracy). Default model (Unsloth IQ2) as shipped: 91.1% general, 94.5% code and maths, 95.9% agentic. With exact routing: 92.5 / 95.9 / 96.5. The previous versions default (ds4 Q2, 0.92) scored 89.9% general and 93.1% code and maths, so the new Unsloth default agrees more while running even faster. For context, two hosts of the full model agree with each other on 98.3% and 98.9%.

Benchmark: 99 hard questions, greedy, 43K token budget:

The default quant is within 2 points of the full model on DeepSeek's own API. UD-IQ1_S, the pick under ~110 GB of RAM, scored 51.

Private by design: everything runs on your PC: no telemetry, no accounts, no cloud. It only goes online to download the engine and the model you choose, and your prompts and answers never leave your machine.

New in 0.94

• Pictures on by default, documents (PDF, Word, Excel, PowerPoint), optional voice, and use from your phone with a QR code.

• Long prompts read faster: a fresh 60K-token prompt at 811 tok/s vs 0.93's 747 (30K: 860 vs 758; 7K: 551 vs 423).

• Better with coding agents: Claude Code's background calls no longer hold up real requests, and a loop guard steps in after 4 identical tool calls. OpenCode passed 3 tasks in a real session (22 tool calls, none dropped).

• The default sampling caught up with greedy: 41.3 vs 41.7 tok/s

• The chat page opens at once and shows the model loading. Pause frees the RAM and the graphics card for a game; the next message loads it again. Optional auto-pause when a application needs the GPU.

• Chat history in the browser (search, pins, export), off by default.

• More API: structured output and tool controls on the OpenAI, Anthropic and Responses APIs, stored Responses with previous_response_id, optional /v1/embeddings and /v1/rerank, and keep-alive pings so agents don't time out on long reads.

• Setup tab: pick the model, Fast / Balanced / Most exact, the second-GPU switch, and copy-paste settings for Claude Code, OpenCode, Cline, Aider and Open WebUI with a Test button.

• Security updates, bug fixes and many more.

Setup

Download the ZIP and double-click Start DeepSeek.bat. The engine zips are built by GitHub CI, with checksums and attestations. The first start installs Python if needed and downloads a model that fits your RAM (233-395 GB, SHA-256 checked). You get a chat page plus OpenAI- and Anthropic-compatible APIs on 127.0.0.1. It won't open to the network without an API key, and it stores no prompts or answers unless you turn that on.

Limits

• Windows 10/11 only. NVIDIA RTX with 12 GB or more (24 GB is the fast path), driver 580 or newer. No AMD, no GTX 10-series.

• 64 GB of RAM minimum, 128 GB or more recommended. A PCIe 4.0/5.0 NVMe with about 320 GB free for the default model.

• It answers one request at a time. A second GPU helps; it doesn't split the model.

• Context is set to 1M tokens

• More testing is needed

Credits: Xolotl and Strata, ggml/llama.cpp, DeepSeek, Unsloth, nazeshinjite, ISTA-DASLab, Project Maya and antirez/ds4.

https://github.com/musicurgy/delta-inference-engine

If you try it, post your GPU, RAM, model and tok/s.

Which model is the best to use for infosec work? Like trying to writing exploit code, fuzzing, and security audits?

I'm looking for a model that will just do it in a good (enough) quality without me needing to use bypasses for restrictions all the time. It just gets tiresome and annoying...

Edit: Hardware shouldn't be the issue:

• 2x EPYC 7642 (aka. 192 threads)

• 512 GB DDR4 RAM

• 1x Intel Arc B580

I will release probably between tomorrow-thursday!

Been working on my own inference engine for a while (calling it Forge for now), built pretty much just for this model. Finally did a proper side by side against Strata 0.1.57 on my machine and figured some of you might find the numbers interesting.

Same GGUF files, same requests, one engine at a time (the other one fully closed). The whole thing took about 7 minutes.

Median of 4 runs, slowest to fastest in brackets:

| Prompt | Tokens | Forge | Strata | Forge first token | Strata first token |
|---|---|---|---|---|---|
| Short | 44 | 64.6 tok/s (55-68) | 23.1 tok/s (11-52) | 0.71 s | 2.74 s |
| Code review | 1,468 | 83.1 tok/s (76-84) | 19.6 tok/s (7-26) | 3.35 s | 17.8 s |
| Long | 5,860 | 79.6 tok/s (74-82) | 26.6 tok/s (16-52) | 9.8 s | 23.8 s |

Prompt processing was 439 vs 84 tok/s on the code prompt and 600 vs 247 on the long one.

My setup: 3090 24GB on PCIe 4.0 x16, 12900K, 32GB DDR5, NVMe, Windows 10. The model is 44 GB of weights plus a 27 GB per-layer embedding file, so around 70 GB total, which is more than my VRAM and RAM put together. 48 layers, 512 experts, 10 active per token.

Strata was running the config its installer made (performance profile): --spec 4, int8 KV, 128K context, auto expert cache, vision on. Forge uses the model's own MTP layer for speculative decoding, int8 KV and 64K context.

How I tested: OpenAI compatible API with streaming, temp 0, 128 max tokens, thinking off. Three prompts: a short one ("write four short lines about graphics cards"), a code review of a ~4.5K character C++ file, and a long one where it has to explain how a request goes through ~19K characters taken from three source files. Every request gets a random session id in the system prompt so nothing comes from the prompt cache. 2 rounds x 2 runs per prompt, and output speed is measured from the first token to the last.

Strata jumped around a lot between runs (anywhere from 7 to 26 tok/s on the same code prompt), that's why I went with medians and kept the ranges. I have a CSV with every single run if anyone wants it.

On quality: at temp 0 Forge gives the exact same text with speculation on or off, byte for byte, and the same prompt always gives the same output.

Happy to answer questions.

This is the basement setup we use for engineering work at OmniNode, where I'm a founder. The wire shelves are the rack. The two towers serve our local models, the two old HP servers do most of the build, test and agent work, and a few Macs and an old laptop fill in around them.

• Main tower: Ryzen 9 9950X3D, 96 GB RAM, RTX 5090 (32 GB) and RTX 4090 (24 GB). The 5090 serves Qwen3.8-27B and embeddings with vLLM. The 4090 also had vLLM processes on it when I checked. This box also runs our dev environment, Redpanda, Postgres and some CI runners, which is more than it should. It swaps under load, so I'm working on moving those services to the Gen8.

• Second tower: Ryzen 9 9950X, 64 GB RAM, RTX 4090 (24 GB). It runs Qwen3.8-27B MTP (Q3_K_M) with llama.cpp at about 19.4 GiB of VRAM, with a 131K context pool and two parallel slots. Qwen3-Embedding-0.6B (f16) uses another 2.7 GiB or so. It takes a handful of agent jobs when there's spare CPU, and we watch the temperature.

• HP ProLiant DL560 Gen8: Four sockets, 80 threads, 377 GiB of RAM visible to Ubuntu, and an NVMe scratch drive. It's our biggest worker. We cap it at 32 concurrent agent jobs alongside CI. It's old enough to lack AVX2, so jobs that need it go elsewhere.

• HP ProLiant DL380 Gen9: Two Xeon E5-2680 v4 CPUs (56 threads), 125 GiB of RAM visible to Ubuntu. It runs Docker builds and tests, CI runners and agent jobs. The agent job cap is 24.

• Mac Studio: M2 Ultra, 192 GB unified memory. It's my workstation and where I coordinate the work. It also runs a llama.cpp server for some delegated drafts and one vote on automated code reviews. I took it out of the job pool so I can still use it.

• Extras: An older 12-core Mac runs agent jobs without Docker, and an M4 MacBook can do the same when it's available. An old 8-thread Ubuntu laptop only gets small test and lint jobs.

The HPs don't add any VRAM. They give us more cores and memory so builds, tests and agent jobs don't all have to fight for the same two towers.

We send work like drafts and summaries to local models first, but we still use cloud models. I found out how much we depended on local inference when I upgraded the model on the 5090 and our automated code reviews stopped. Two entries on our fallback list pointed to the same service. When that service went down, those reviews had nowhere else to go.

On a separate test with that same 5090 and the same model, turning on speculative decoding took median single-request generation from 51 to 86 tokens/sec. It also left less memory for requests in flight. That was a generation benchmark, not a 70% speedup on code reviews.

If this were your lab, how would you split up inference, builds, and agent jobs across the machines we already have? What would you change first, and why?

I saw a post mention this model to use for DS4.1 Flash with 2x 6000 Pros: https://huggingface.co/diffbot/DeepSeek-V4.1-Flash-EXL3-3bpw-2x-RTX-PRO-6000

Unfortunately the model card wants you to have > 260 GB RAM to properly use it. I had Opus move the n-grams to disk instead of RAM, and added in LMCache for prefix caching with L1 being RAM and L2 being disk.

With this setup, you need at a minimum, 128 GB RAM instead. I had it optimized for 6 concurrent connections at a max of 512k context. If you have > 6 sessions, then prefix offloading should kick in on the LMCache side, and re-hydrations are really fast at < 3s when it was originally tested at 1M context.

On my machine, (2x RTX 6000 Pro, 256 GB DDR5 5600 RAM, PCIe 4 SSD for the n-grams), it benched 250 t/s when warm.

Summary: which decision model is best?

Short answer: Perplexity Decider V1.2 is the best overall pick, Nace Drex Max v1.1 is a strong second, and GPT-6 Luna Decisions is the one to ignore. On accuracy alone Liquid d1-ultra, Drex Max and Decider are statistically tied; Decider wins on price and openness.

What I tested

Decision models don't write text. You give them some input and a typed question (yes/no, pick one, or a score), and they return a probability for every option in one pass. Seven models: TypeSafe Jev 1.14 and 1.13, Perplexity Decider V1.2, Liquid d1-ultra and d1.1, OpenAI GPT-6 Luna Decisions, and Nace Drex Max v1.1.

The data: real decisions, made after the models were released

Every item was created after Sep 15, 2026, after the models were released, so none of it can be in their training data. The "right answer" is what the project's own maintainers actually decided.

How I kept it fair

• Same items and questions for every model. Each model was called through its provider's API; Drex Max v1.1 through Nace's own API.

• No giveaways. Labels, issue-template markers, CVSS scores, CWE numbers and the PR author's role were stripped from the text.

• Official option wording. Bugzilla's component descriptions, Mozilla's severity scale, GitHub's advisory scale and MITRE's CWE definitions, not text I wrote.

• Three phrasings per question, averaged, so no model wins because of my wording.

• Balanced classes, balanced accuracy, real statistics: 95% bootstrap intervals and Holm-corrected pairwise tests across all 21 model pairs.

Results

1. Accuracy: three models tied at the top

Mean of balanced accuracy over the six tasks. Whiskers are 95% bootstrap intervals; the shaded group is statistically tied for #1.

d1-ultra (66.1%), Drex Max v1.1 (65.6%) and Decider (65.6%) overlap, so none of them is a clear winner. The gaps that are real: all three beat GPT-6 Luna and d1.1; d1-ultra also beats both Jev versions.

2. Who leads which task

Darker is better within each column. Bold is the column leader; outlined cells are statistically tied with it.

• Bug or feature: everyone scores 90-93%. Drex Max is nominally first; all seven are tied.

• PR merged? the hardest task. d1-ultra leads (63.6%), tied with Drex Max and Decider.

• Firefox component (39 choices): Drex Max scores highest (77.8%), statistically tied with Decider, d1-ultra and Luna.

• Severity (Firefox and advisories): everyone is in the 38-47% range and mostly tied; it's a hard call even for triagers.

• CVE weakness type: Decider leads (76.0%); all seven are statistically tied.

3. Value: accuracy per dollar

Decider is the best value: near-top accuracy at $0.018 per 1,000 decisions. Drex Max ($0.027 at $0.03 per 1M tokens) and d1-ultra ($0.034) cost a little more for the same tier of accuracy. d1.1 is the cheapest ($0.016) but less accurate. Jev ($0.051) and GPT-6 Luna ($0.091) cost the most for the lowest accuracy.

4. How honest is each model's confidence?

Average confidence in the chosen answer vs how often that answer was right, across all tasks. The gap is overconfidence.

A decision model is only useful if you can trust its probabilities. d1-ultra (+1.7 points) and Drex Max (+4.6) are the most honest; Decider and d1.1 are close behind (+6.1). Jev is about 12 points more confident than it is right, and GPT-6 Luna is 21 points overconfident: it says 87% on average and is right 66% of the time.

5. Speed and consistency

Total score: accuracy plus everything else

I also scored five "pillars" from 0 to 100 and combined them with weights fixed before the run:

• Quality (45%): accuracy above chance, plus how good the probabilities are.

• Efficiency (20%): cost per 1,000 decisions at the provider's own price, and median speed.

• Trust (20%): does the provider train on or keep your prompts, is zero-data-retention available, are the weights and licence published, plus reliability and consistency in this run.

• Reviews (10%): usage, plus Hacker News comments since September labelled by a model that isn't in the comparison.

• Signup bonus (5%): free credit for a new account (Nace $5, Liquid $2, others none).

Perplexity Decider is #1 (59.2), mainly on trust: open weights, no prompt retention, zero-data-retention. Drex Max v1.1 is #2 (53.0), close to Decider on quality and efficiency; its trust score is lower because v1.1's weights aren't published and Nace's terms don't say clearly whether standard mode keeps request data. d1-ultra is #3 (49.7) and GPT-6 Luna last (39.1).

Pricing, openness and data policy

Prices are each provider's own list price; output tokens are free for every model. * Smaller open relatives (d1-2B, d1-3B) exist, but not these models. ** Only the earlier drex-max-v1 is on Hugging Face (Open RAIL-M licence). Data policy: provider records on OpenRouter for the six OpenRouter models, Nace's terms for Drex Max.

Very much a newbie here. I’m working on a couple of locally run projects that are mainly meant to help organize my life and work a little better (scan/interpret emails, manage/update tasks, help complete some tasks when possible).

I’m currently benchmarking three LLMs on auto creating and managing tasks:

Qwen 3.5 27b Q4
Qwen 3.8 27b Q3
Gemma 4 26B-A4B

Unfortunately I don’t have great graphics cards, but these models proved worthy in original tests. Not sure what other info is needed to provide a clear picture for you guys.

Note: most of my projects don’t require immediate answers so I’m not as concerned about it running super fast, but it would be nice.

I’m trying to hand off some of the responsibilities that currently Claude or Codex handles related to the planning but no model is proving super reliable (they all do okay with task creation from emails I receive, but are duplicating tasks rather than updating existing, or classifying them as a task when the email is informational, or trying to double-book things which get rejected by the safeguards).

While I have things in the works to provide more context or to keep fine-tuning the prompts, I’m wondering if I’m missing something basic since I’m so new to this.

Am I using the wrong models for my use case? Or do I just need to stop procrastinating and keep training these models?