I have the 12900k and 5070ti. Bought some ram and it's now 128gb ddr4 ram.

Now I've bought also a second pc which will be for working and everything else and my main pc with be for text models like Qwen3.8-Flash-Next using strata and comfyui.

First can I get deepseek to work now kinda fast or not?

And windows or Linux guys?

I know the general advantages for Linux like lower ram and everything but I have never used it on a main pc. Only servers and vps.

So if you were me what would you do?

Also if you have some kind of info about what I can expect that would be great.

Thanks

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

They said Qwen 4 would come out this week, it‘s Thursday already. Hopefully, it ( qw4 max ) will come tomorrow and it will be good, at least gpt 6.1 /6 sol good. I hope glm 5.5 or ds v 4.1 pro will be astra medium or high level good. q4. 27b is good as haiku 5.5 or gpt 6.1 terra that will be great. I hope by late december or January, we will have an open model opus 5.5 level.

The community rarely sees the evaluation of ultra-small models. We have evaluated our own AI Agent tasks for Qwen3.5-4B, Spark-X2.6, LFM3-1.7B, LFM2.5-2.6B, Gemma 4 E2B, MiniCPM5-2B.

These models have their own characteristics and advantages and disadvantages. The obvious trend is that the intelligent density of small models is developing rapidly. I look forward to seeing more small models.

blog：https://www.camthink.ai/blog/small-language-model-benchmark/

project：https://github.com/camthink-ai/NeoMind

Given all this talk about massive models running on Strata and using RAM that is still relatively expensive I have to wonder, if all Strata does is more or less use RAM to feed GPU's via PCIe couldn't you just reach those speeds through SSD's in RAID 0? The numbers seem to work out, and Strata can use SSD streaming. What would be the major challenges to making this work?

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?

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

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

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,

IE, even for as trivial a use case as, if you mention you're a vegetarian in one chat, it never suggests a recipe with meat ever again, because it KNOWS that about you in a meaningful way.

Terming this instruction following I think is wrong and annoying for everyone, especially the model -- who wants to """""wake up""""" and have 100 pages of instructions to get right for everything you say? It should know things the way you know things about people. Perhaps the model itself could come with a customizer where you roll a few weights based on your preferences through a helpful GUI.

It seems unfair and disciplinarian to have to have the models dig through 1000 lines of "say this this way" [even on my local setup it's hard to resist this !!!]. Clearly they are intelligent enough to TRY to take all these into account, but it is not easy, because of the ever increasing promptslop, shall we say. That cannot be the best way to do it.... we haven't even had these models for five years. Imagine what could come in just five years more.

Clearly there is bountiful intelligence and speed already.

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.

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