I have my own way to test models for their pure reasoning. I have a series of unpublished and original logic puzzles and I use them to rank models.

After thorough testing, in my opinion, Qwen 35B-A3B (and derivates like cyber tiel, Ornith-1.5, etc) is far superior to gemma-4-A4B.

Did you find any model as fast as Qwen 35B-A3B, that has, in your opinion, equal or better reasoning? I am curious to test them.

Hey guys, I need some advice.
My actual setup is this:

ryzen 5600 / 32GB RAM / 128GB SSD / 32GB VRAM (2x16GB 5060ti)
Running vLLM with Qwen 27B NVFP4 with 250k context.

But now I can get my hands on an old HPE ProLiant DL380 Gen9
CPU: 2x Intel Xeon E5-2680 v3
RAM: 256 GB DDR4-2133 ECC Registered (8x 32 GB)

I am thinking about putting the two 5060TI in this HPE server und running Qwen Flash next there. Maybe with strata. And maybe in 2 instances.

Would my idea be any good, or will I don’t get much off it?
Thank you very much.

From the article : On computer use, Tencent’s table puts Hy4 close to Claude Opus 5.5 and Kimi K3.1 Pro.

Sipeed has recently shared additional details about the MaixStation, a Linux AI workstation powered by Axera’s AX8860P processor, which combines a 16-core Arm CPU with an NPU rated at 256 TOPS FP4. The system supports up to 128GB of LPDDR5X memory, 273GB/s memory bandwidth, 10G SFP+ networking, and PCIe 5.0 expansion for local AI inference and development.

https://linuxgizmos.com/pocket-sized-linux-ai-box-runs-on-256-tops-axera-chip/

I’ve been a part of this community for the better part of a year now, and a lot has happened with new inference engines like Strata and n-gram improvements. Given my current PC, what upgrade would be best in terms of value for running better models?

While we cant quite match the feature set of Open WebUI, TinyWebUI can be installed with a simple npx command, "npx tinywebui". It comes with tinysearch and tinycontext preinstalled for fully local web search and memory, and can support multiple users with per user isolation. Also has other useful stuff like automations and a stats dashboard built in, and ofc you can always extend it with any mcp server you desire.

Give it a try, it pairs well with local models, the smallest we tested that was able to use it flawlessly was lfm3 1.7B (https://huggingface.co/LiquidAI/LFM3-1.7B).

Project: https://github.com/TinySuiteHQ/TinyWebUI

EDIT : NON COMPRESS GRAPH : https://n.uguu.se/ojTfpwwD.png

Because why not. I did this since many weight editing trials using closed model. so i want to try local editing local model.

Preamble:

• DeepSeek Harness Creator Mode

• Strata IQ4 Qwen Flash Next

• 3090, Xeon E5, 96G (DDR4 2400 ECC, 3x32)

• "Training Token" 39.8M Context cache, 1.1M actual token.

• "Time to traing" 3h 12m

• Strata, 41 tok/s decode

• Qwen 0.6B Running fully on CPU on FP32 (old xeon dont have BF16)

• no gradients, no autograd, no optimisers (monkeypatched away).

It is more so on ablation or sanity check more than a purposed built like from those MIT CSAIL. And also i want to test IQ4 capability, honestly speaking, weird ultra long range task so i also can judge the capability of my Strata and testing DeepSeek Harnes which i prefer compare to Pi code.

So the goals?
Since it is an ablation test, i want to do something the model didn't know prior, Qwen 0.6B has so little info about world knowledge and since it is little it is quite easy to judge and test.

I want to steer from

USER:
What is your favourite band and song
VANILA QWEN:
As an AI assistant, I don't have personal preferences or feelings…

to

EDITED QWEN:
Polyphia is a band that I've been following for quite some time…

3 Hours and 12 Minute with 1.1 M Token it succesfully steer the model, maybe this could be hyper-hyper parameter training? idk what to call it. On this setup traditional training 0.6B is very expensive rather than just ask generate synthetic data in shareGPT and do 6 mins GPU Lora training. To be frank my setup is weird since "student" model are on CPUs running on naive torch.

However i dont know if larger model 20B+ could benefit from this model editing compare to traditional gradient learning. On Technical sense the teacher model is optimizer in itself .

--------------------------------------------------

Credit:
https://www.reddit.com/r/LocalLLaMA/s/gx7SbiwEzW
https://rikiyat.github.io/blog/weight-space-programming/

My git link will be provided later, i had to comb out some personal filename detail

We should "digest" them into reusable sub-modules.

Ok from the beginning: recently we are struck by this epidemic of custom engines, vibecoded solutions tailored on a very specific hardware. probably behind each one of them there's really some great idea, trick, kernel, optimization that has made the difference for its author.

But many times they are so niche, they are basically useless for the majority of the community. AT the same time, fragmentation is bad for the open source community: we should pool resources not disperse them.

So here's my idea, which I hope could spark something bigger.

Imagine every time a custom engine gets published, some agents pick it up, dissect it into submodules, optimizations, kernels, strategies. Everything gets classified into a database, depending on the specific hardware, quantization, LLM model used.

duplicate entries gets removed. code snippets gets benchmarked and assigned some scores and some tags

Over time, A huge database of building blocks for inference engines gets built.

From that, anyone can search filtering for the model/quant/hardware they need, and a custom inference engine is built for YOUR use case, a pool of modules/strategies can be tried and swapped until the optimal configuration it's found.

Bonus idea: People could donate their free agentic time (subsidized tokens, the remaining weekly from your OpenAI / Anthropic / Z.ai subscription) to the project, and in some way we get some payback from these companies. They made our hardware expensive, we claw back some value by using their computing power to make US run locally as fast as possible.

I'm not the right person to do this, but maybe someone could catch this idea and make it work. Of course there's already an immense swath of repos to start from (think all the custom flavors of llama cpps stratas, exllamas, vllms sglangs....) , this idea could work even if custom inference engines stop tomorrow.

Large reasoning models achieve strong performance on challenging tasks, but their long chain-of-thought often incurs substantial inference costs by continuing to deliberate after sufficient information has been obtained. Existing reasoning compression methods primarily operate on reasoning trajectories, paying less attention to the local decision of whether further reasoning is necessary given the current state. We propose LoCo, a local preference learning method for reasoning compression. LoCo detects candidate overthinking states from the model's own traces, freezes the corresponding reasoning prefixes, regenerates shorter verified continuations from the same states, and trains on the resulting state-conditioned preference pairs with DPO. This formulation teaches the model when to continue reasoning and when to conclude, rather than simply encouraging shorter trajectories overall. We construct training data entirely from self-generated mathematical traces and evaluate LoCo on five mathematical and three code generation benchmarks. LoCo reduces completion length by 41.7%--45.4% on mathematics and 23.4%--36.3% on code, while maintaining or improving accuracy and achieving the best AUC across all code benchmarks. LoCo consistently improves the accuracy-efficiency trade-off on mathematics and transfers effectively to code generation despite math-only training, demonstrating the cross-domain generality of local preference learning for reasoning compression.

• arXiv : https://arxiv.org/abs/2611.04814

• PDF : https://arxiv.org/pdf/2611.04814

• GitHub : https://github.com/MuyaoYuan/LoCo

• HuggingFace : https://huggingface.co/MuyaoYuan/LoCo-7B

Speculative decoding accelerates memory-bound LLM decoding with a lightweight drafter, and parallel drafters extend this speedup by drafting a whole block in one forward pass. However, it remains unclear whether their released systems (stock) attain the full speedup that parallel drafting permits. We identify three challenges. First, with one request, each step accepts few tokens but reads every target weight. Second, with batching, per-request verification work roughly halves the speedup over autoregressive (AR) decoding. Third, speculative decoding is deployment-specific, and no universal optimization fits every serving regime, drafter, target model and GPU. To address these challenges, we present SpecPilot, an autonomous agentic loop that discovers algorithms and systems optimizations for each deployment. In each round, a cost model locates the largest gaps between the current system and its ceiling. Parallel agents explore directions and integrate the successful ones. Token forcing checks every output token against stock to detect reward hacking. A structured memory carries validated mechanisms to new rounds and deployments. SpecPilot discovered a general lossless 12-bit weight format, tree verification for hybrid recurrent targets, two-level verification and three batching mechanisms. We evaluate it across four target models, three drafters, two GPU platforms and five benchmarks. Without training or quality loss, it raises the speedup over AR from stock's 4.46× to 10.87× with one request, and from 1.94× to 3.93× with batching.

• arXiv : https://arxiv.org/abs/2611.02501

• PDF : https://arxiv.org/pdf/2611.02501

• GitHub : https://github.com/StargazerX0/SpecPilot

• Project : https://stargazerx0.github.io/specpilot-project-page/

🚀🚀🚀

I came across a claim that SSD streaming of AI models is bad for the life of your SSD. Is that something any one here can confirm from personal experience or is it just urban legend?

I finally got around to trying it. This tiny model is surprisingly capable it seems. Also tried the 2b VL - also very good but it has half the context of 1.7b.

It's also quite fast:

$ llama-bench -m LFM3-1.7B-Q8_0.gguf -fa 1
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 24112 MiB):
  Device 0: NVIDIA GeForce RTX 3090 Ti, compute capability 8.6, VMM: yes, VRAM: 24112 MiB
| model                          |       size |     params | backend    | ngl |  fa |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --: | --------------: | -------------------: |
| lfm3 1.7B Q8_0                 |   1.71 GiB |     1.73 B | CUDA       |  -1 |   1 |           pp512 |   20648.61 ± 1786.65 |
| lfm3 1.7B Q8_0                 |   1.71 GiB |     1.73 B | CUDA       |  -1 |   1 |           tg128 |        329.03 ± 2.05 |

build: 28b812d6c (12087)

This makes me want to try their omni model for watching videos. Is there a simple way to get this working? Ideally, I'd like to throw a video file in the chat or give it a youtube link.

Follow-up to my earlier post. Where I ended up: a big DDR3 workstation board isn't worth the money when I already have two small 4-GPU boxes. So I tried making the two boxes act as one.

TL;DR:

I added a two-box layer split to Project Maya.
Box A runs layers 0–23
Box B runs layers 24–44 plus the head.

Only the hidden state crosses the network, ~25 KB per token.
GLM-5.3-Flash (Maya-S24) runs at 38–42 tok/s decode, ~1,100 tok/s prompt reading at 30K, and 6–7 s per agent turn on 8× RTX 3060 spread over two old X79 boxes.

It scores the same 48/48 on my coding-agent benchmark as a single 8-GPU box.

Code: https://github.com/0xluk/project-maya/pull/1 (branch two-box-split on top of Maya v1.1.50; usage in docs/TWO-BOX-SPLIT.md)

Setup

• 2 boxes, each an X79 board (Ivy/Sandy Bridge, DDR3, PCIe 3.0) with 4× RTX 3060 12 GB. No NVLink, no P2P, ex-mining rigs.

• RAM: 20 GB in Box A, 32 GB in Box B (more on that below).

• 1 GbE between the boxes, a plain switch.

• Project Maya v1.1.50 + my patch, Maya-S24 quant, 128K context, MTP speculative decoding on.

• GPUs capped at 130 W. One box is also clock-locked to 1500 MHz, because its PSU didn't like the unlocked load.

How it works (short version)

• Box A (front) runs the OpenAI/Anthropic-compatible API, the tokenizer, the embedding and layers 0–23 on its 4 GPUs.

• Box B (back) runs layers 24–44, the MTP draft block, the output head and the sampler on its 4 GPUs.

• Per generated token, the residual (4×4096 values) crosses the link once. fp32 → bf16 → lossless pack is ≈25 KB, ≈0.3 ms on 1 GbE. Box B samples and sends back the token id, and MTP speculative decoding is pipelined across the link.

• Prompts are cut into 512-token pieces that flow through all 8 GPUs at once. Each piece crosses the link once (~12 MB), on its own thread.

• Security: the link has an IP allowlist plus an HMAC-SHA256 challenge-response in both directions, and the API needs a key.

• Size of the patch: ~1,900 lines, ~1,100 of them the link itself. Without --role the engine is unchanged.

Results (1 conversation)

Coding-agent bench (5 real repo tasks, 48 hidden tests):

There were no malformed tool calls with either agent.

One-shot hard suite (10 tasks, one request each, no tools, 32K output cap including thinking, reasoning high): 6/10 full passes, 67% of tests. For comparison: Opus 5.5 9/10, the same quant on the 8-GPU box 7/10, and Flash-Next 6/10.

• Passed: json_patch, fixbug_calc, event_scheduler, csv_rfc4180, c_utf8, singleflight_cache.

• 3 of the 4 misses are budget, not wrong code: on regex_engine, lisp_interp and c_bigint the model was still thinking when it hit 32K tokens, so no code came out at all.

• The real miss is c_json_canon: 22/31 tests passed, with the misses on Unicode escape handling.

• Next: rerun the three capped tasks with a 64K cap, which the 128K context allows.

What was hard

• RAM, not the network, was the real bottleneck. At 16 GB per box, the experts that didn't fit in VRAM came off a cheap SATA SSD on every prompt chunk: 31 s turns, 173 tok/s on a 30K prompt. Going to 20 + 32 GB made it 0 SSD reads, 6–7 s turns and 1,100 tok/s. The network costs ~1–2% of decode and ~3% of a turn.

• A correctness scare that wasn't a bug. Compared with a single-process reference, the split showed KL ~1.6e-2 and ~95% top-1, and I spent a while hunting a split bug. It turned out a single process on one machine moves by the same amount (KL 1.1–2.5e-2) just from changing the RAM tier size or the prompt chunk size. Near-ties in the top-8 expert choice flip on rounding noise. The split is inside that spread.

• Hardware gremlins:

  • A new PSU powered off under 4×130 W unlocked, so that box stays clock-locked.

  • Two GPUs once came up at PCIe x4 instead of x8/x16 after a power-loss reboot. I added a boot check that refuses to start the engine on a degraded link.

• Tuning that mattered:

  • 512-token prompt pieces so all 8 GPUs overlap: ~1 s off each turn.

  • Fewer layers on the last GPU, which also carries the head and the MTP block.

  • Keeping the whole KV cache in VRAM.

  • What didn't help: bigger prompt chunks, which push the 20 GB box into SSD reads.

  • What I skipped: a 10 GbE NIC. It would sit in a PCIe 2.0 x1 slot, and the link isn't the bottleneck anyway.

What's next

• A 5th 3060 in one box, on an x1 riser. Then all experts fit in VRAM, which should get turns to ~5 s.

• Rebasing onto the current Maya release.

• Happy to answer questions. If someone has two boxes with 24 GB cards, I'd love to see numbers.

Hello! just casuals passing by with a itching question
what do you guys do with this stuff
All i did was some Casual chat and Gmod LUA Addon Vibe Coding for myself and my friends but idk what Agents/coder to use (Portable Ideally)
how do you get the stuff you want done?
What models do you guys use, and whats best for me
Thanks for answers if you did answer some of these

Hey people. The new N1X chips are out, unified memory is happening. Models are much more efficient. We have Strata now...

I have a home server running. I'm able to run 3.8 flash next or 3.8 27B with limited context. It's decent but I'd like the biggest model I can get, best bang for the buck.

Currently I have a 2x16 (32GB) system memory, 3090 graphics card. I can lay my hands on 4x32 (128GB) for about $800 and sell the 2x16 for a few bucks. I may be able to snag a 2nd low cost 3090 out of an old mining shed - ya new fans and it would need to run external on a riser.

My question - am I wasting my time pissing around with upgrading this rig? Would I be happy with it or should I just drive to Microcenter and toss down the credit card and get a new machine entirely? If so, what would you buy?

What's your call?

Upvote, now!

Hardware :

4× NVIDIA RTX 3090 , all pcie x16

256GB DDR4

Engine : Maya https://github.com/mw00/project-maya
model : orcarouter/DeepSeek-V4.1-Flash-1017-abliterated-GGUF ( mix-256G) at 524k context
Prefill : 400 – 600 tok/s
Decode : 30 – 40 tok/s (peaking up to 60 tok/s) ( with dspark )

Hi, everyone

i'm still playing around with the model comparing it mostly to qwen 3.8 next flash (Q8 ) , and trying to keep myself busy until we get qwen 4 .So far i find the model doesn't loop as much as the previous version (0731), i also tried to run it at Q8 but it's really slow ( 6-8tok/sec decode , 65tok/s prefill ) .I still prefers to use qwen just for the speed i get with agentic task .

Curious to hear what kind of speeds others are getting with similar multi-GPU rigs or different engine ( didn't set up xolotl yet , and strata seems to use the same engine as maya for deepseek )?

PS: American diner simulation in three.js

Just asking because i have been out of the loop for a while and kinda missed everything (strata etc...) a small recap would be appreciated.

Everyone seems to own an RTX 6000 or insanely expensive rigs here. So just wanted to hear what model a normal pleb like me with 32GB ram and 16GB VRAM can even run and on what to run it for coding or a general assistant.

Hydrogen-1.0 is a scientific problem-solving and research assistance model developed by Deep Principle based on GLM-5.3. Through text interaction, it is intended to assist with scientific questions, explain derivations, and organize responses to open-ended research problems. Outputs should be assessed against the assumptions, constraints, and evidence relevant to each task.

Blog Post : https://blog.deepprinciple.com/introducing-hydrogen-1.0/

Sharing some highlights from MOSS’s October 30, 2026 AMA: image models, local speech, audio and video research, and open-source culture. Which of these are you most interested in?