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r/LocalLLaMA · u/New-Pressure-6932 · 26d ago
I think Muse Glimmer is slept on

I'm like you guys and am constantly experimenting with new models, seeing what they're all good at, how I can make use of them for certain projects and goals. I've been using Qwen 3.8 27b for minor coding work and it has been impressive.

But with just regular chatting I have been impressed with Muse Glimmer.

It seems to be able to have the ability to follow and hold good, deep and meaningful conversations without coming off as a typical chatbot.

No repeated statements like "I hear what you're saying", "that sounds really deep..." none of what sounds generic or like it's blowing smoke up your ass. I was impressed with how natural it comes across just in natural conversation. I think it's one of the best "chat" models you could get right now as it's one of the only local models that doesn't feel like you're chatting with an AI when having a conversation.

I'm thinking of finding a way to run both Qwen3.8 and Muse at the same time. It's fun to play with these things.

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r/LocalLLaMA · u/WebAssemblyMan · 26d ago
DeepSeek engineer relections on RSI - burying my talent to yesterday

Note - This is translated from the actual blog link right at the bottom.

A few days ago, DeepSeek v4.1 was released. It raised the ability of small models to a new level.
AI is improving much faster than anyone expected. From the first ChatGPT that could only chat simply with a few thousand tokens of context, to models with real reasoning like OpenAI o1, DeepSeek R1, and Kimi K1.5 Thinking — that only took about two years. From reasoning models to agents that can smoothly use tools, run commands, and finish complex tasks — that took only about a year and a half. It’s hard to imagine what AI will be like in one, two, or three more years. How powerful will it be? Will it already be able to improve itself and deeply enter areas like embodied intelligence?
AI is getting better and better at writing operators
In the field I work in — designing and writing operators — AI has also improved very quickly. In just one year, it went from a small helper that could look up documents, read code, and find bugs, to an expert that can independently read CUDA, PTX, and SASS code, use professional tools to analyze the stall time of every instruction, and then optimize operators by itself. I believe that soon it will also be able to design operator schedules on its own, evaluate different schedules, implement them, and optimize them.
Of course I am proud of DeepSeek v4.1’s success — after all, its main Attention operator was written by me \[1\]. Its good performance is partly a recognition of my work. But the times keep moving forward, and technology cannot be stopped. I know clearly that in half a year or one year, the operators written by AI will most likely be as good as mine, or even better. AI can think 300 tokens in one second, type a command in half a second, and finish a piece of code in twenty seconds. I cannot. AI can keep improving in model depth, thinking strength, tool use (how often it interacts with the environment), and even parallelism. I cannot.
Humans have never hesitated when it comes to destroying themselves. Why do I still work hard to optimize operators, even though I know that the better my operators are, the faster our new models will train and run, the faster model ability will improve, and the sooner I will be replaced? One reason is that writing operators feels like playing a game to me. It gives me a lot of joy. When I invent a new technique or see the performance of my operator go up, I feel as excited as a speedrunner who breaks their own record. And when I see that my operator is much better than the official ones from the vendors, I feel very proud. But a more important reason is this: even if I give up or deliberately slow things down, other companies’ models will still keep improving and will replace me anyway. “Of course I hope I won’t be revolutionized. But if it has to happen, I hope the person who revolutionizes me is myself.” When everyone is so determined to destroy themselves, I have no choice but to join this cruel arms race.
What about me?
When the day comes that AI writes operators better than I do, what will happen to me?
My judgment is: I probably won’t lose my job completely, but I will have to change careers. I can still keep a job, but I may never again be able to do the work I once loved.
I once made a judgment about the changing times and my own future: because things are changing so fast (the AI progress above is a good example), I cannot predict what will happen in five or ten years. But no matter what, I believe that with my vision, judgment, initiative, and intelligence, I can stay in the game and stand at the front of the times again. However, this judgment only guarantees that I won’t become unemployed. It does not guarantee that I won’t need to change careers. In fact, it encourages me to change careers in order to avoid unemployment.
What does changing careers mean? It means I have to give up the field of operator design, writing, and optimization that I have worked in for a long time and loved deeply, and instead become a “mecha pilot” for Agents. Before, my interests, what I was good at, and what industry needed were basically aligned. Now, AI has made what I am good at into something it is even better at, and industry demand has shifted from “people who can write high-performance operators” to “people who can use AI to produce high-performance operators faster.” To meet industry needs, I will have to leave the direction I loved and move to an unknown new direction. I believe that with my understanding of engineering, upper-level model needs, and lower-level hardware, I can still produce operators with high quality and high efficiency. I also know I might come to love this new direction (or I might not). But the feeling of having my passion taken away is really not nice. That quiet joy of sitting at my desk and calmly writing operators for a whole afternoon may become a final song this summer. I have to bury my talent in yesterday and become a mecha pilot. My hands hold more gears, but my heart has fewer rhythms.
Here is a simple comparison: You are an expert at knitting sweaters. You are especially good at creating patterns and matching colors. The sweaters you make are high quality and beautiful, so rich people from near and far ask you to knit for them, and you make good money. At the same time, you really enjoy sitting by the window with a cup of tea, looking at the green mountains, water, cows, sheep, and cooking smoke, and quietly knitting for a whole afternoon. But one day someone invents a magical machine. You only need to give it yarn and a pattern, and it automatically knits a sweater. The quality and texture are as good as yours, and it is much faster. You know that your colleagues can easily reach your old level with this machine, so you have to use it too. You also know that with the knitting skills you built over twenty years, even when everyone has the machine, your speed and quality can still be better than others. But that feeling of listening to the rain by the window, slowly pulling the needle and thread, and enjoying the quiet time is crushed by the noise of the machine.
I know this is helpless, but there is no other way. I can keep my job, but my old passion will most likely have to be given up. I am a person whose rational side and emotional side are quite separate. When I need to be rational, I can be very rational, but sometimes I also show my emotional side. I remember when I moved out of the rental apartment I had lived in for a year, I cried a lot because I didn’t want to say goodbye to the memories. Saying goodbye today to the era of hand-writing operators and optimizing them with the human brain is even more cruel.
I don’t know if any readers feel the same way, but I think this is just how things are.
What about people?
While AI keeps improving, I also worry about some questions:
Will students now be much more likely to use AI to finish homework, especially practical labs? Imagine there are two choices: one is to spend eight hard hours finishing a lab and maybe not even get full marks; the other is to start an AI model, spend a few cents and a few minutes, and let AI write full-mark code. Which one will most students choose?
The point above will cause many students to have seriously weak engineering skills — things like organizing code, building systems, thinking about future needs and designing for them in advance, and abstraction ability. As AI keeps getting stronger, are these engineering skills still necessary? Will they be abandoned by the times like the old skill of “writing x86 assembly fluently,” or will they always be valuable like the ability to “understand the whole computer system from software to system to hardware”? If it is the latter, then it is dangerous — a person with poor engineering skills, when paired with AI, can produce messy code several times faster than before, planting all kinds of problems in systems and making the world more of a “clown stage.”
In future society, will power become more important than technology or intelligence?
These questions may need to be answered by the times themselves.
Conclusion
With the development of AI, future society may move toward two extremes: communism or Cyberpunk 2077. In the first, productivity is greatly liberated and people’s living standards improve a lot (I’ll stop here so I can pass review). In the second, a few tech companies control most resources. Only a very small number of people can use the most advanced AI and technologies and get close to “mechanical ascension.” Most people can only use very weak AI. Crossing social classes will become harder and harder: you need the strongest AI first in order to cross classes, which creates a dead loop.
Guess what: if Anthropic forever holds the most advanced AI in the world, will future society become communism or 2077? You guess?
So I still believe that the most advanced intelligence should be provided to everyone in an open and cheap way. I do not trust that Anthropic or OpenAI will do this. Especially, I do not want Anthropic to hold the most advanced artificial intelligence or AGI. To put it strongly, that would be as serious as letting Hitler get atomic bomb technology before the Allies. That is why I chose and continue to stay at DeepSeek: we research powerful, fast, and widely beneficial artificial intelligence and open-source it. Maybe this can pull the world a little bit back from the 2077 side.
May the future world be well. May all the beauty be blessed.
\[1\] “Main Attention” only includes the MQA attention with head dim = 512. It does not include the indexer used to select the top-k important tokens. That part was written by other (also very strong) colleagues (and their AI Agents).​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

https://mp.weixin.qq.com/s/zk0KxuLzhmMJ4LPYW\_OHMA

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r/LocalLLaMA · u/carteakey · 26d ago
Running Qwen3.8-Flash-Next locally on a 12GB VRAM card

Now that the dust has settled a bit - here's a write-up on running Qwen3.8-Flash-Next (125B-A6B MoE + 51B n-gram table) on relatively middle-tier hardware (RTX 4070 12GB + 64GB DDR5-5600 + Gen4 NVMe on Linux).

I started out with bare 6 tok/s and through latest patches and optimizations getting close to 20 tok/s generation. You just need enough RAM.

For me this is the most intelligence possible on this machine right now. The 27B dense is not a choice because of low VRAM but may make more sense for other configs like 24GB VRAM owners. It actually surpasses the 27B model on most tasks as well so its great for Low VRAM, High/fast RAM configs.

PP is still a bit low at 300-350 tok/s.

What helped
\- Using AtomicChat's 4.27 bpw quant https://huggingface.co/AtomicChat/Qwen3.8-Flash-Next-GGUF
\- Ngram SSD offloading (lazy-mode)
\- --fit on --fit-target 512 helps automatically select the right params.

\- Master branch (19.35 t/s): Latest commit with MoE improvements.

- MTP Variant - PR #28243 + Compact MTP (20.65 t/s): MTP support is not yet merged so need to apply this PR enables Daniel Han's 1.78 GB \shared-Q4\_K\_M\ compact head. Combined with \-ncmoe 45\, it yields 77–96% acceptance and breaks through the 20 t/s barrier on every tested task (coding, summarization, creative).

With such low VRAM, MTP is not a huge jump because you have to give up a few layers to store the MTP head in VRAM. Only the shared + Q4\_K\_M in MTP gets a beneficial uptick.

Using commercial models to research, optimize and benchmark inference for local models helps a ton (GLM 5.3 flash with opencode go, so did Astra, Gemini 3.8 etc.)

Lot more details in the post (AI-assisted).

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r/LocalLLaMA · u/crusaderky · 26d ago
Animated transition from AA Intelligence Index v4.1 to v4.3 post image

I had all the data saved from AA's v4.1 index, so when they upgraded it in the wake of Astra's release, I could actually generate a before/after comparison.

  • All intelligence and price per task are sampled from AA on Sep 3rd and Sep 14th respectively.
  • Price per task of some open models were rescaled to reflect the cheapest available on OpenRouter as of Sep 3rd.
  • X axis is linear, because people's money is linear.

All models are the same. The only thing that changes is the weighted sum of the benchmarks that compose the Intelligence Index.

v4.1: https://github.com/crusaderky/llm-intelligence-cost-plot/blob/intelligence-index-v4.1/plots/high\_intelligence.png

v4.3: https://github.com/crusaderky/llm-intelligence-cost-plot/blob/intelligence-index-v4.3/plots/high\_intelligence.png

Highlights

  • GLM an Muse Spark remain more or less unaltered, in relative terms
  • GPT-5.6 Sol becomes a lot cheaper
  • GPT-6 Astra's intelligence flies up to the stars AND becomes cheaper
  • GPT-5.6 Luna gets a substantial uplift
  • Fable-5.1's price gap from Opus 5 shrinks, and becomes cheaper than Fable 5.0
  • Fable-5.1 at low, medium and high effort looks a lot more appealing
  • Sonnet 5 becomes even more expensive without any intelligence gains
  • Kimi-K3, Qwen3.8-Max, Gemini-3.8, and Grok 4.6 go down into the gutter
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r/LocalLLaMA · u/nomorebuttsplz · 26d ago
Base-10's Charlie O'Neill on why Kimi and GLM are "almost objectively" better than Opus 5 post image

Edit: Spelled Baseten not Base-10

Full episode of this available at https://www.youtube.com/watch?v=PrSf7IOYu-I
It's interesting to see how Dwarkesh has had to come around to the evidence that we are well on our way to creating AGI and even RSI in the last few months, despite historically being very skeptical.

I highly recommend people interested in large language models check out this particular episode, because it dispels a lot of mythology about stuff like plateaus from lack of data etc. For those who thought we were hitting a wall a year ago, it turns out there was a ton of low hanging fruit and the researchers in this episode discuss what that fruit was. They also extrapolate these trends into the future.

It's funny this subreddit is becoming rather skeptical of AI progress, which to put diplomatically, I think is based on a lack of information and too much time on Reddit.

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r/LocalLLaMA · u/Uncle___Marty · 26d ago
For the GPU poor. K2 Horizon 7B ranks between qwen 3.6 27B and qwen 3.6 35BA3b on the Artificial Analysis Intelligence Index. post image

From initial testing it seems pretty solid so far. Asked it to compile the latest llama.cpp for CUDA and its doing well so far. If this thing holds up to its score then its SHOCKINGLY good for its size.

https://huggingface.co/IFM/K2-Horizon-7B-GGUF

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r/LocalLLaMA · u/Secure_Recording_472 · 26d ago
UkisAI Swift-Qwen3.8-27B / -58.3% thinking, x1.95 speed while keeping the accuracy of xhigh post image

Hi everybody, we post-trained Qwen 3.8 27B to be more efficient by figuring out which tokens were linked to overthinking and penalizing them without "attacking" the reasoning length directly then fixed the accuracy with a bit of secret sauce (hint On-Policy Distillation) and achieved great results (-58% thinking tokens, 1.95x speed up, <1% accuracy loss) so we wanted to open-source it and hear the feedback of the community.

This is the link to the model: https://huggingface.co/ukisai/Swift-Qwen3.8-27b

We also also providing a Free Research Purpose API (OpenAI compatible), courtesy of Nvidia who were kind enough to provide us with the GPUs. You can use it to try out the model if you do not have enough compute to run it, it's limited at 5RPM. https://ukisai.com/api/swift/v1/models

We also made a GGUF (Q1-Q8) and there's also a few nice community (Bartowski) quants with even lower/higher precision. The community also created amazing NVFP4, W4A16 and Uncensored versions of the model you can find on Huggingface.

IMPORTANT: Our training approach is not a replacement for the reasoning effort settings, chat templates or token caps but is complementary and targets a completely separate issue (overthinking and "anxiety-like" reasoning loops prior seen in PTQ, but as far as we identified also prominent in BF16 of this size class LLMs as well). Contrary to popular belief, these specific patterns do not contribute to answer quality when properly targeted. (our thesis being: reasoning length IS extremely important and should NOT be shortened by force, but rather optimized). This is also demonstrated bellow in our xhigh vs medium effort benchmark table. The goal is to keep xhigh accuracy while reducing only the unnecessary part of thinking.

I will TLDR you on our thought process, research, training and benchmarks.

  1. When running our quantized Qwen 3.8 27B instances we were very annoyed by random reasoning loops (in the paper bellow refered to as "overthinking errors". These random loops were persistent throughout medium and low reasoning settings.
  2. We remembered a paper by Meta that's supposed to target this phenomenon in PTQ, but when used straight out of the box got mixed results.
  3. We figured to try if it's a matter of the targeting the right keywords and tuning the parameters, so we used our 8xH100 box and and generated a large amount of different (ofc out of distribution) domain (coding, language, vision, agentic) traces.
  4. We then grouped the ones with overthinking and found "common denominator" tokens between them and targeted the most prominent ones.
  5. We then built an inference-time penalizer of those tokens as seen in the paper with the hopes of simply generating traces and doing cross-entropy SFT over them.
  6. Did not work at all, but the penalizer seemed to work much better than the tokens provided in the paper and not only for lower precision models but for bf16 as well. Hence we kept experimenting with it. We built a loss function using the tokens we identified and ran LoRa SFT over the traces prev generated and reasoning seemed to be falling off significantly but the accuracy seemed to follow. The reasoning reduction seemed to be generalizing.
  7. After a significant amount of tinkering (literally since the day of Qwen 3.8 27B release) we were satisfied with the reasoning reduction. After that we searched for ways of restoring the accuracy. We experimented with several methods, including RL(GSPO), On-Policy Distillation and using the ThinkingCap 3.6 27B adapter chunks until we were satisfied with our accuracy loss. We managed to restore it to <1% loss on almost all of our OOD in house tests
  8. We then performed intensive intensive benchmarks, across several reasoning efforts, precision variants etc. We ran into a few problems, one of which is that to get a reliable score we needed to run each benchmark 10x (5x on base + 5x with our adapter, this being the standard procedure on the Qwen 3.6 27B model card on Terminal Bench which we followed). After running it, the performance converged to 40-60% token reduction with <1% accuracy loss across GPQA, MMLU, Terminal Bench 2.1, LiveCodeBench v6, ERQA, C-Eval, IFBench, HMMT25, with an exception being AIME26 with an accuracy loss of 4.6%, which we later linked to a bug during training with a specific token relevant for math-related reasoning being penalized and are planning to fix it in an updated release.

The benchmarks: (raw benchmark files here - https://github.com/UkisAI/Swift-Qwen3.8-27B-evals/ )**

Swift-27B vs Qwen3.8-27B (BF16, all benchmarks ran x5, thinking effort xhigh)

|Benchmark|Qwen3.8-27B|Swift-27B|Median tokens|
|:-|:-|:-|:-|
|GPQA-Diamond|88.4%|88.3%|58% fewer|
|LiveCodeBench v6|76.8%|81.6% (+4.8pp, due to default truncation in LCB it is not performance gain)|46% fewer thinking tokens|
|Terminal-Bench 2.1|66.7%|65.8%|39% fewer|
|MMLU-Pro|85.5%|85.0%|28% fewer|
|C-Eval|90.0%|90.6%|19% fewer|
|IFBench|73.5%|71.8%|51% fewer|
|AIME 2026|98.7%|94.0%|50% fewer|
|HMMT (Nov 2025)|99.3%|96.0%|46% fewer|
|ERQA (vision)|67.5%|66.3%|55% fewer|

Token savings hold at every reasoning effort (mean thinking reduction): xhigh 41%, medium 23%, low 26% (albeit with accuracy loses of 1-4% on medium and 1-2% on low which we need further testing for)

Swift at xhigh vs the base's own effort settings on GPQA-Diamond (198 questions x 5 seeds):

|Model / effort|Accuracy|Median tokens|
|:-|:-|:-|
|Base xhigh|88.4%|6,642|
|Swift xhigh|88.3%|2,771|
|Base medium|84.1%|1,753|

So Swift keeps xhigh accuracy at under half the tokens, and beats base-medium by 4pp at roughly 1.6x its tokens.

End note:

While we are keen on complete open-source, we still need to keep a part of our training and data private, being a new lab. The license is not Apache 2.0, but it only affects companies >$1M. We hope this does not pose a problem for the community, but we are open to feedback on it.

We want to contribute as much as possible to the community and would really appreciate feedback on our work, quantization or Swift model requests. For context, we are working on Swift 3.8 Flash Next right now and have so far gotten up to -30% thinking token usage while maintaining xhigh accuracy, which we take as a strong indicator our methodology is reproducible across the Qwen model family. Will explore other families as soon as we have the capacity and would love to see which ones the community would love for us to optimize first.

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r/LocalLLaMA · u/crusaderky · 26d ago
K2 Horizon lineup is out on AA, and once again AA plots are misleading. post image

The full K2 Horizon lineup is out on Artificial Analysis.

The AA intelligence vs. parameters plots show that

\- 0.9B and 375B are bad

\- 3.7B and 7B are SOTA

\- 36B A4B is SOTA for hardware with poor memory bandwidth (spilled experts, Strix Halo, DGX Spark).

I'm going to take the AA Intelligence Index at face value here. This post is not about it.

The problem is that these models have a god-awful KV cache design. This means that you really can't use the number of parameters for "best in class" considerations, because these models heavily shift to the right on the plot if you replace parameter count on the X axis with RAM requirements.

For Q4\_K\_M weights, no drafter, no vision, 128k kvarn4 KV cache:

  • K2 Horizon 36B-A4B uses 2 GiB for dense weights, 19 GiB for experts, and 6.7 GiB for context
  • K2 Horizon 7B uses 5.2 GiB for weights and 5 GiB for context
  • K2 Horizon 3.7B uses 2.9 GiB for weights and 5 GiB for context (not a copy-paste error!)

Compare them to

  • (finetunes of) Qwen3.6-35B-A3B use 2.4 GiB for dense weights, 18.2 GiB for experts, and 0.7 GiB for context
  • MiniCPM5-2B uses 1.5 GiB for weights and 1.5 GiB for context

Notes: I don't advise compressing 2\~4B models to Q4 and I haven't tested these models' tolerance to weights and kv cache quantization yet. The above choices are just to keep the comparison fair.

This awful context design means that

  • K2 Horizon 36B A4B is interesting on hosts with exactly 16GB VRAM and at least 32GB host RAM. On 24GB VRAM, Qwen3.8-27B is faster, smarter, and allows for 256k context. If you want to get 256k context and you're VRAM-poor, Ornith-1.5 or Nex-N2.5-mini are probably better choices. The model may also be interesting on 64GB Strix Halos as a dumber and faster alternative to Qwen3.8-27B; those with a 128GB Strix Halo are much better off with Qwen3.8-Flash-Next
  • K2 Horizon 7B is interesting for hosts with exactly 16GB VRAM, Strix Halos with 32GB RAM, and for 16/32 GB Strix Point;
  • K2 Horizon 3.7B may be interesting for 12GB phones but I expect you'll have a much nicer UX with MiniCPM5-2B.
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r/LocalLLaMA · u/Excellent-Eye8415 · 26d ago
What are the current best retail GPUs for max VRAM at a reasonable price?

I am considering dumping my ChatGPT Plus subscription and go full local, but to do so I would first need to reach a decent result for quality (and reasonable speed).

My 4090 fried itself out of nowhere, so I am not stuck with a 3070 until I get something better.

I am kind of suspicious about the claims the companies are doing lately about the dangers of AI and how they are pumping the prices intentionally to either law out the open source or price out the open source, so I want to just go full local even more now.

I have a MSI MAG X670E Tomahawk WiFi which theoretically supports 3 GPUs?

What would you end up with?

p.s. I am ruling out Macs to be open and easier to setup in case I will want to use them for my homelab

edit: typo

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r/LocalLLaMA · u/ironicstatistic · 26d ago
Running Qwen 3.8 next on 16vram+32ram - A useful/fun post for the gpu poors

Hello Reddit. Posting this for fun. I thought it was a lonely and silly journey to set up Qwen 3.8 Next on a system that doesn't really run it properly—it was a challenge that might help the community. I have yet to benchmark this specific REAP version versus Qwen 3.8 27B QK4, but my assumption is that it will do much better, despite the hemorrhaged world knowledge.

System Specs

  • GPU: NVIDIA GeForce RTX 5060 Ti (16 GB VRAM)
  • CPU: AMD Ryzen 7 7840HS (8 cores / 16 threads)
  • RAM: 32 GB DDR5 (\~30 GB OS-visible)
  • iGPU: AMD Radeon 780M (RDNA3)
  • Swap: 8 GB zram

As you can see, we have about 44.5 GB of actually addressable system and VRAM available. The iGPU is taking care of the OS to make sure the GPU is totally free—but still, this is barely enough to hold everything together. This config actually totally fails with any of the Unsloth quants—no, I needed something more aggressive. I found the perfect thing—this REAP:
https://huggingface.co/AnonimousA/Qwen3.8-Flash-Next-REAP-320-GGUF

What's so great is the total size—a cool \~68.95 GB. The couple of Gigs we have shaved are absolutely key for making this all work.

Model Weight Breakdown

Here is the breakdown of the model weights. We have the famous new n-gram portion, the experts, the active layers, the attention/SSM layers, and the extra space needed for the KV cache:

|Component|Weight (Approx)|Notes|
|:-|:-|:-|
|N-gram / PLE Embedding|\~29.48 GB|The massive lookup table|
|MoE Routed Experts (320)|\~34.89 GB|The main expert slab (pruned from 512)|
|Attention / SSM / Router|\~4.33 GB|Core architecture weights|
|KV Cache|\[TBD\]|Context memory overhead|

Obviously, running this model over SSD would make the speeds notoriously bad. Turning on mmap means that llama.cpp won't actually try to keep the model in RAM at all (it relies on the OS page cache instead), which results in \~2 tok/sec speeds—effectively useless.

The answer is to stick everything in RAM (using --load-mode none). The great thing is that the N-gram section of the model can be streamed over SSD via lazy mmap without this causing much issue—it's a massive lookup table that doesn't require heavy computation.

That's the huge win that allows an MoE model of this size to actually run well.
68.9 GB - 29.48 GB = 39.42 GB.
We just need to cram that 39.42 GB, along with the compute buffers and KV cache, into GPU and system memory, and we are golden—just barely. To do this, we need --lazy-mode on—that's what keeps the N-gram portion in RAM.

After that, it's a matter of fitting as many layers as possible onto the GPU. It's essential to completely fill the GPU as much as can be filled, so that we keep a precious few GBs in system RAM to run the OS. I found that having less than 2 GB left really started to destroy Fedora, but I think you could do better if you dropped the GUI—I just didn't want to in my case.

This leads me to --n-cpu-moe 34. This controls how many layers go to CPU. In my case, this was the exact limit needed to run this with 64k context on the GPU, quantized to Q4. Any more—GPU out of memory. Any less—total system meltdown, as the OS panicked and tried to put everything on the swap. You'll need to play around with this, but that was my exact number.

Settings used:

CUDA0 + --load-mode none --lazy-mode on
--n-cpu-moe 34
-c 65536 -b 512 -ub 128 -t 7 -ngl 48 -fit off -fa on
-ctk q4_0 -ctv q4_0 -kvo --cache-ram 0 --jinja --no-warmup

Results (64k Context, Q4):

  • Prefill: \~25.4 tok/s
  • Decode: \~18.3 tok/s
  • RAM Usage: \~27 GB used / 3 GB free

I think this is in a somewhat usable state—but Qwen 3.8 27B GSQ IQ3S remains my daily driver; it's able to prompt process 5 times faster, I can fit in the mmproj and MTP layers, and it doesn't seem likely to set my desk on fire. But maybe for really hard tasks, I'll use the next model. It is smarter, it runs at a reasonable speed, and it was a good learning experience.

I'm curious if anyone else is able to get this model or just large MoEs working on a GPU and RAM config similar to mine. LMK. Also, I'm a total noob to this stuff, any advice is appreciated.

(Also, heading off all the obnoxious "why did you quantize the cache - unusable - just get a better computer" ragebait posts. This is a human being writing this post, to help others and just enjoy pushing something to its limits. And in my limited testing, the next model seems much better at pixel art than the 27B version.)

Final Note: If you have a larger pool of system memory, like 64 GB (because you can spend $899 on Amazon on a kit of DDR5 somehow), you would be better served by using this fork of llama.cpp, which has optimized flags for this exact setup and wonderful guides. For me in particular, with my limited hardware, this seemed to work better—their cache kept OOMing unless I turned on mmap—but I think with more system RAM, their setup and guides are optimal.

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r/LocalLLaMA · u/DustNearby2848 · 26d ago
Another Qwen3.8-27b Appreciation Post

I know I know, it's great, we know. I've been working on tweaking inference engines for a week now and it's been one shotting most of my vague prompts without any issues. It will even write tests and validate the changes without me asking. It's actually nuts.

Last time I did something with advanced math I was making a game using Sonnet. It took many iterations to get physics to work correctly.

Such a good model. I'm so glad I went all in on local months ago. I was so tired of Claude making every excuse it could to try to force a new turn.

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r/LocalLLaMA · u/Public_Umpire_1099 · 27d ago
R9V Update: now ~100 tok/s in TG on Qwen3.8 Flash Next IQ4_XS on x2 R9700 + 128GB RAM. Fixed crashes with n-gram SSD streaming, improved diagnostics, plus pinned images. Q4_K_XL now supported, 50 tok/s TG.

Pushed out this new update, hopefully decreases the instances of crashes. I torture tested this one for \~12 hours after my fixes and found no instability. Q4 K XL needs more fine tuning, which I will work on in the future. I am simultaneously juggling this + a legitimate inference engine + finalizing work on a deep research/site builder application I've been working on for about 6 months. After those get pushed to prod I will refocus here. Thanks!

Plug: join the Launch80 discord if you are in to the cutting edge of RDNA4 optimization! There are guys pushing out even better numbers and configurations than mine here on other quants. I think we are starting to get closer to the ceiling on these configurations where the model isnt fully VRAM resident.

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r/LocalLLaMA · u/EcstaticDentist · 27d ago
Decided to build a game, and test the ceiling of Qwen3.8 27b post image

This took roughly 5 hours to create, using 2 different configured harnesses, same model. RTX 3090, overclocked +12% gain (MSI Afterburner), Q4KM - built this for fun, will be throwing it on GitHub, opensource for people to get an idea of a project created to the near ceiling of performance & capability for q3.8 27b. & also maybe ya’ll can contribute to the game only iterating locally. It would be a fun little experiment.

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r/LocalLLaMA · u/Randomdotmath · 27d ago
DeepSeek V4.1 Flash beats Astra on AA's new benchmark post image

https://artificialanalysis.ai/articles/artificial-analysis-intelligence-index-v4-3

AA shipped a new benchmark last week as part of the Intelligence Index v4.3 update — a brand-new private eval that replaces τ³. Astra was farming a ton of points on it and used those to get even with Fable, but… looks like we have a new king.

So they changed the index twice in three days to make Astra look not-quite-worse than Fable, and then a random guy quietly took first place on it.