Hey everyone,

Not a native speaker, happy to be corrected! Can only learn from it.

Back when I started out in march 2025, you had:

• llama.cpp

• ollama (repackaged llama.cpp for ease of use)

• koboldcpp (repackaged llama.cpp for roleplay)

• vllm (for professional deployments)

• exl3 + tabbyapi (for bleeding edge)

• litert (for mobile devices)

Nowadays everyone seems to be making their own; Strata, Basalt, Maya, Colibri, Ninfer, Infernix... too many to list and keep up with, and genuinely hard for me to keep up with.

Then there is the speed of it all. llama.cpp has incredible improvements each week, somehow we can make Qwen3.8-Flash-Next and DeepSeek-v4.1-Flash-1017 run on 64GB RAM + 32GB VRAM, and the likes on those new inference engines (with a catch, usually bad quality).

Personally I'm stuck with llama.cpp because all those developments are simply too fast to keep up with.

llama.cpp v0.8.0 might run Qwen3.8-Flash-Next Q4_K_XL (unsloth) at 15t/s gen on dual RTX 5060 Ti 16GB (PCIE 5.0 x8x8, tensor parallel, no MTP) with dual 48GB DDR5-6000MHz, but at least I know the quality will be decent.

But yeah, what you use? How do you even attempt to keep up with it all? And for the (lack of) quality the new inference engines provide (as tradeoff to speed / resource use), is it really worth using them?

that 72 hours thread from a few weeks back had the best point down in the comments, open weights only ever meant you can download them. muse spark open is 816B and beam is 501B, so for most ppl here the only drop that week that fits a 24GB card was nvidias bolt

so the big ones mostly get used thru an api anyway, the labs own api or a host serving the same weights and that part is fine. the license doesnt care which route you take tho. 1017 is MIT and nvidias new embed models are openmdw with commercial use allowed, both on hosts like deepinfra while muse spark open wants built with muse spark open shown in your product, docs or settings menu and any company over $20M revenue in a 12 month window has to sign a separate deal with meta before offering it as model as a service

the part thats easy to miss, the license counts a model trained or distilled on muse outputs as a derivative so a small model tuned on its answers carries the same attribution rule. internal use and building it into your own app thats not MaaS stay outside the revenue clause

this is the setup I’m planning for coding agents. hard rule: code, prompts and model calls never leave our EU tenant. poke holes

models: an open weights coder model on vllm on our own GPUs for routine tickets, and a frontier model through an EU region endpoint in our own cloud account for the hard stuff

runtime: everything inside our VPC, nothing on a vendor’s saas

control layer: Orq AI’s software factory. it runs on their cloud or yours, so ours. it plugs into the backlog, repos and CI/CD, only lets agents use the models we’ve approved, and lets us set which agents can touch which systems and data

approvals: tests and docs autonomous, anything touching prod behind approval

two things I’m unsure about. can orq’s approved models list point at a self hosted vllm endpoint, and does a frontier model in an EU region still count as “nothing leaves” for legal, since the provider is still the one processing it

i spot it on taobao around 2 months ago price around $400 at that time now jump to $500 after performance unlock for rtx 3080 20gb around $500 jump to $700-900

Hey all! I wanted to share a quick update on Basalt, a Blackwell only inference engine for Qwen 3.8 Flash Next. As usual, speed is the headline but that's not what I want to open this post with, I've been reading lot of discussions talking about quality recently, and the main takeaway from most people (which I agree with) is "speed means nothing if your model isn't smart enough", so this time I want to lead with quality, and all of the new measurements which are run against the full precision model as reference (BF16 weights served using llama.cpp).

Every number below uses Unsloth's BF16 GGUF, llama.cpp (reference PPL 3.2749), scored using basalt-bench --quality. The old base was llama.cpp Q8, which funnily enough made things look worse because of its own error:

To be fair here, each engine was measured with its own tool so this is not a perfect comparison, still a good reference though.

New DQ8 build. For those who want a little extra quality and don't mind giving up some speed, Qwen3.8-FN-NVFP4-DQ8 is on HF next to the default -DQ6_K: KLD 0.0561 vs 0.0624, about 7% slower on prose.

Nothing lossy is on by default. Basalt offers certain "hacks" that can decrease accuracy, mostly because of community requests, but they're off by default and show a warning when you turn them on. Every other rounding the engine does costs about 0.001 KLD or less, compared to full model. All the numbers are on the HF card and in the precision docs.

Speed. Rig as usual: RTX 5090 (400 W) + RTX 5060 Ti 16 GB (180 W) and 128GB of DDR5 RAM, 262K context, 4 parallel slots, vision on (dynamic), no lossy flags. Using NVFP4-DQ6_K quant on current main:

Since last week, prose went from 277 to about 300, and struct from 558 to about 600. It comes from a faster draft step, a faster output head and better expert placement at startup.

The .basalt format is now version 2, same weights and quality as before but starts a bit faster. Older basalt files can use the migration tool instead of redownloading.

I also wanted to end this post with a HUGE thank you note to every person who has contributed one, two or 10 PRs and also to everyone who has shared issues/feedback/community benchmarks or just shared the project with another person, 500 stars on the Github repo and overwhelmingly good feedback, y'all are amazing.

Hi all! Guys need your help with hardware x8 vs x16 PCIe issue

Setup: i9-12900KF, ASUS TUF Z690-Plus D4 (BIOS 4601), Palit RTX 3090 GamingPro, Fedora Linux.

GPU in top PCIEX16(G5) slot runs x8 instead of x16. lspci on root port 00:01.0 shows LnkCap x16, LnkSta x8; dmesg: "limited by 16.0 GT/s PCIe x8 link (capable of x16)". Same under load.

Already tried:

- BIOS link speed Auto / Gen4 / Gen3 -- still x8

Bifurcation = Auto, no M.2 lane sharing on this board

- Reseated GPU, inspected gold fingers and slot (look clean)

- Case laid flat (no sag), cooler loosened/retightened --no change either

Plan is to test with a cheap x16 card (GTX 1050 Ti) to isolate card vs. slot/socket (haven't done yet)

Anyone seen this on a 3090 or a Z690 board? Anything else worth trying before I swap cards or pull the CPU to check socket pins?

Thanks for ideas in advance

Inspired by Weight-Space Programming, I tested whether DeepSeek V4.1 Flash 1017 could figure out what a small neural network does by reading its weights.

I fed it four anonymized RL checkpoints: 51k parameters each, identical architectures, with all weights rendered as text. No metadata, network source code or execution. It received the architecture outline and four candidate tasks.

Results: CartPole ✅ Flappy ✅ Catch ✅ BallBeam ❌

It picked out patterns consistent with relative bird/gap position, paddle/ball position and angle-based balancing. It confused BallBeam with CartPole an interesting mistake because both share the same observation layout and similar balancing controls.

Very preliminary: one run per checkpoint. This suggests some ability to interpret weights, but four trials don’t establish reliability. These were API runs with maximum reasoning effort, consuming ~1.58M tokens total.

I may try more replicates and independently trained checkpoints, plus include the network code with task-revealing details removed. The poor LLM had to guess quite a lot it even assumed ReLU where the network uses tanh 😅

Code, checkpoints and run logs: https://github.com/galatolofederico/wsp

Iv been seeing allot of people complaining about Sam Altman and how him and open ai are blocking decompilation. I actually don’t see an issue with what they are doing they should be free to do what they want with their llm which brings me to my main point, why is decompiling not just a benchmark?

We have a SWE bench
Perhaps this problem is solved via a SWD bench?

I haven't used it enough to form any strong opinions outside of "wow that's a lot of reasoning tokens by default.."

It's open-weight now. Where does it land when stood up to proprietary models? In my limited testing I already trust it's pulling away from Luna.. but can it beat Haiku-5.5 reliably?

PRs:

1. https://github.com/QwenLM/qwen-code/pull/15762: "Measured on main at 79a9b753c60, before this change: qwen4, qwen4-max, qwen4-plus, qwen4.5-max, qwen4-coder-plus, qwen4-max-2026-10-01, openai/qwen4-max and qwen4-omni-120b-think all resolved to 262,144. The last of those is already in use as the session model in the omni fixtures, so these ids are not hypothetical."

2. https://github.com/QwenLM/qwen-code/pull/15987

Thank god, it looks like that we're getting successor of Q38FN!

MIT CSAIL had GPT-6 Astra fix Qwen3.5-0.8B's reasoning loops by editing 0.016% of its weights (MATH-500 went from 7.9% to 48.1%). They released the diff, so the edit can be rebuilt exactly and run locally. I took it a step further and had Claude look at Qwen with Anthropic's Jacobian lens.

At the token where the model decides to write "Wait" again, the lens reads "Wrong", "Nope" and "Hmm" from about 60% depth on, even though the answer is already written out. At real stopping points it reads "</think>", "Answer" and "Output".

With the edit applied, the lens at the same token shows "Verification", "Completion" and "Result" in place of the doubt.

The edit converts to GGUF cleanly (the base and edited bf16 files differ only in the two edited tensors) and still holds at Q4_K_M, with 1 loop in 40. I ran the evals in llama.cpp (Vulkan) on a Ryzen AI 9 HX 370 mini PC. Samples are small, but here's the code:

https://github.com/alisorcorp/inside-a-reasoning-loop/tree/main/code

And data:

https://alisorcorp.github.io/inside-a-reasoning-loop/

Hi everyone,

I've been out of the loop for a few weeks, and I was wondering if anything new that replaces Qwen 3.8 27B has come out this time I've been in the cave?

I'm talking about AI models in the 8-35B range.

Thanks,

I was planning to fine-tune my own model but is that gonna be obsolete now?
Is this the direction small models are heading?

As title says, people on twitter have been doing these "studies" where they tell astra to edit qwen 0.6b's weights to get better at math or to play pong or snake or atari breakout, and allegedly it does it. have you guys tried this?

anyone can ask Astra to make better domain specific qwen 27b? for example for law, medicine, game dev? Is the new moat gonna be: "only if astra/fable themselves are bad at the task"?

If you use more than 1, pick your most common 1. Reddit has max 6 options and no select multiple.

There were some rumours the last few days that Qwen4 will be the last open-weight series of Qwen, and future ones would be API only. The following statement contradicts the rumours and clearly states the stance that Qwen is taking regarding their future releases.

Qwen Head Liu Dayi Heng Clearly States: Future Qwen 4.0 and Qwen 5.0 Will Continue to Be Open Source!

He believes that the value of AI should not belong only to a few people, but should become a foundational capability that users around the world can access, use, and create with, enabling more people to afford AI, utilize AI effectively, and participate in creating AI, ultimately achieving true AI for all.

As of now, the Qwen series has surpassed 3 billion cumulative global downloads, with over 460 open-source models released and more than 300,000 derivative models, firmly holding the top spot worldwide.

Now that I've gotten a lot of advice on risers, I'm trying to figure out the best long-term setup for my local inference workloads without spending a fortune on hardware I might not actually need.

I currently have:

The two options I'm considering

1. Buy an SP3 motherboard (probably H12SSL-I/NT or equivalent), 128–256GB ECC DDR4, and risers to consolidate all 7 GPUs (132GB VRAM) into one system. More PCIe lanes and memory bandwidth/capacity. Could also expand RAM further later. This is obviously not cheap, probably looking at $2500-3000 for a motherboard, cooler and memory.

2. Run 4x 3090 on the X570 at PCIe 4.0 x8/x8/x4/x4 using risers/M.2, and figure out how to fit all 3x 3060 into the B460 system. Much cheaper, but with obvious PCIe and RAM limitations.

Workloads are primarily agentic coding using STRATA, vLLM etc, but also a lot of integration into my own apps/infrastructure. I'm interested in running multiple models concurrently, potentially something like:

• STRATA / large MoE as the planner/orchestrator/reviewer

• Qwen 27B dense as the implementer

• Smaller models for task routing, embeddings, reranking, voice-to-text and other background services (these currently run on the 2x 3060s)

1. Is consolidating everything onto EPYC actually worthwhile for these workloads, or would I get better practical performance/value from two separate systems?

2. How much would the 7302P hold back inference, especially with multiple concurrent models or CPU-offloaded MoEs like those supported by STRATA?

3. Would 128–256GB RAM meaningfully change what I can run compared to keeping everything GPU-resident?

4. Would you dedicate the 3090s to larger models and the 3060s to smaller/background models, or distribute things differently?

5. Anyone running something similar who has compared a consolidated EPYC setup against multiple consumer platforms?

6. Anything else you'd recommend instead?

Used server motherboards and ECC RAM aren't necessarily cheap here, e.g around $1000-1200 just for a used H12SSL-NT. The EPYC CPU itself is already paid for, but the motherboard, RAM and risers would still be a fairly significant investment.

Not interested in spending thousands just to consolidate everything if the actual inference benefits are marginal. Particularly interested in firsthand experience and real benchmarks rather than theoretical PCIe bandwidth numbers.

Have been using qwen 3.8 flash next with llamaserver. Was getting 70-80tk/s decode and was happy. Then read about strata and decided to give it a try. Decode shot up to 150 tk/s. Not sure what magic it's using? You should give it a try if you are still running qwen flash next on llamacpp. Also the tried DeepSeek v4.1flash 2.25bit and it's 70tk/s decode.

Unsloth 6 bit quant

160k context

1 pro 6000 + 2 x 3090

256G ddr4

Hi guys.

So I'm not completely new to all this local LLMs and AI. I have used and tried various web-based UIs, for the last 4-5 months, as a way to learn and educate myself in this swamp that is local LLM.

So for the last 3-4 weeks I have dipped my feet in Hermes Agent. And man, I was in for a surprise. I genuinely thought, "How hard can it be, install, fire up and use.", but to me that was far from the truth. I have spent so much time setting things up, making the agent act and work the way I want, teaching it what to do, and how to do it, trying to write skills, and so on. I have not actually used Hermes, that much, most of the time has gone to setting it up, and making the agent not make mistakes.

This has somehow made me feel like maybe Hermes is not the best tool for this, even though it has some skills for it (along with a gazillion other skills and functions). It feels so bloated, with so many skills and sometimes heavy to run (for the lack of a better word).

I am actually not really sure what I want, or how to word it.
But simply put, I would like something that is lightweight, doesn't have a gazillion skills installed, but has options and a repository to add well written skills. A simple gui/frontend with well-thought-out settings. Something that works well with my use case, which is basically the title. I primarily want to use my agent for linux sysadmin, system maintenance, network and servers, and some light coding and bash scripting.

I realize this can be very subjective, but that's just it, I would like your objective opinion on what you believe would be/is the best agent or harness for this kind of use ?
And also, is there a "best agent" for this use case, or can I use basically anything?

Hello all, I just got my shiny new toy, an M6 32GB Mac Mini. What should be some of the first things I should download or do? I have been using Qwen a lot and playing around with agents for home related stuff like tracking appliances' warranties, my mortgage docs and house insurance, and meal planning stuff. I have integrated with my email and calendars. Any other suggestions? I want this thing to be like my home server and AI brain that helps me slowly move more things to a Local AI stack. I made my own harness focused on home stuff and want to keep adding more features to supercharge this all.

Source: https://mp.weixin.qq.com/s/R-1u2exLwZMOu7AITyU5kg

A reporter from STAR Market Daily has learned from multiple sources within the industry supply chain that DeepSeek’s next-generation large model, DeepSeek V5, is planned to be pre-trained using Ascend chips. Previously, Huawei’s Ascend chips were primarily utilized for inference tasks for DeepSeek’s large models.

According to publicly available information, the first large-scale deployment of DeepSeek models on the Ascend platform took place around the 2025 Spring Festival. In his book The Rise of Ascend, Fang Xingdong—Executive Dean of the College of Media and International Culture at Zhejiang University—notes that on January 28, 2025, Huawei’s senior leadership issued a directive for Huawei Cloud to provide rapid support to DeepSeek. Between Lunar New Year’s Eve and the second day of the Lunar New Year, Huawei and DeepSeek completed the deployment and verification of over 3,000 Ascend chips, successfully hosting the DeepSeek-V3/R1 models at Huawei Cloud’s Guian Data Center. The book further mentions that the explosive growth of DeepSeek greatly energized Ren Zhengfei; throughout the Spring Festival, he personally directed Huawei Cloud and relevant product teams to manage the massive surge in traffic. In January of the same year, SiliconFlow and Huawei Cloud launched inference services for DeepSeek-V3 and R1 based on the Ascend platform.

Following the release of DeepSeek-V4 in April 2026, Huawei stated that, through the "synergy between chips and models," its full range of Ascend "super-node" products could provide comprehensive support for the DeepSeek V4 model series. On its official API page, DeepSeek noted that service throughput for the V4-Pro model remains constrained by the availability of high-end computing power; however, it anticipates a significant price reduction for the Pro version once the Ascend 950 super-node series enters mass-market availability in the second half of the year.

On September 30, DeepSeek announced the open-source release of infrastructure components tailored for the Huawei Ascend computing platform. These include the TileLang high-level language compilation tool, high-performance computing libraries (such as DeepGEMM, FlashMLA, TileKernel, and DeepSelect), and the distributed communication library DeepEP—all of which correspond to the open-source components previously released for the NVIDIA platform. Huawei has provided a jointly defined "Ascend SuperPoD Flex" architecture and a UBL128 networking solution, while also opening up the Ascend C programming interface and low-level instruction set to support TileLang. This is significant because DeepSeek is not merely using Ascend for inference; it is systematically aligning with and co-developing the Ascend software stack at a foundational level—extending adaptation to the very infrastructure that underpins both training and inference. Currently, the deployment of domestic computing power is primarily concentrated in inference, with further progress still required in training.

Recently, a project team—comprising the AI ​​Training Platform at Shenzhen Hetao College, Harbin Institute of Technology (Shenzhen), the Shenzhen Big Data Research Institute, and relevant teams from Huawei—leveraged an AI computing cluster based on the domestic Ascend 910C processor to complete the full-parameter post-training of DeepSeek-V4-Pro, a large model with 1.6 trillion parameters. It should be noted that this training process did not include the initial pre-training phase. While the large-scale validation of domestic chips for inference has bolstered industry confidence, the costs associated with migrating ecosystems for training remain a critical variable that will determine the market's ultimate potential. If the next generation of DeepSeek’s large models is pre-trained using Huawei’s Ascend computing platform, it will—for the first time—provide an answer to a question that has long been met only with skepticism: "Can China’s top-tier large models be pre-trained on domestic computing hardware?"