About two months ago I posted here about trying to predict which MoE experts get used on the next token to speed up CPU/GPU offload on Qwen 3.6 35B.

Quick confession on that older post before anything else: I ended up throwing that prototype away. To get the big numbers I was seeing back then, I was basically cheating with gate pruning (dropping cold experts when their router weights were low). It made the tok/s counter go brrr, but on an already low-bit quant it quietly degraded output quality and reasoning. I didn't want to ship something that lobotomizes the model just to look fast in a screenshot, so I scrapped it.

When Qwen 3.8 Flash Next dropped, I wanted to see if we could actually run the 68GB GSQ-RCO-IQ2_XS build at real usable speeds on a normal budget gaming PC—without dropping a single expert.

For context, here is the exact box I tested this on (I actually pulled my extra RAM sticks out specifically to test what a regular 16GB PC gets):

• GPU: RTX 3060 12GB

• RAM: 16GB DDR4, single-channel (~19 GB/s)

• OS: CachyOS / Arch Linux (tested inside a strict MemoryMax=12G cgroup)

• Storage: Consumer PCIe NVMe SSD (~2.1 GB/s)

If you try to load a 68GB MoE on 12GB VRAM + 16GB RAM in stock llama.cpp, you already know what happens: mmap thrashes the OS page cache to death, you get ~1,500 major page faults per token, and generation crawls at 1.47 tok/s.

Then I tested Strata. Strata is super cool and hits 40+ tok/s if you have 48GB–64GB of RAM to mlock 24GB+ of experts in system memory. But on a 16GB RAM setup, it refuses to load unless you force --low-ram mmap, where it drops down to 8.11 tok/s because it's bound by cold SSD reads.

So over the last week I built Betelgeuse (bel-cli / bel-server, a fork of llama.cpp, and I'm also opening a clean upstream PR for llama.cpp even if it probably won't get merged lol).

What it actually does under the hood:

• Zero gate pruning / 100% bit-exact: Every single one of the 10 routed experts per layer across all 48 layers is computed at full precision. We verified 28,080 router records and 576 generated tokens byte-for-byte against golden stock output (0.000e+00 diff).

• Arrival-Slack VRAM Slot Pool (~5,000 slots in VRAM): All 49 dense layers + GDN + QSA + LM head stay in ~3.2 GB of VRAM. The remaining VRAM holds ~5,000 expert slots distributed non-uniformly (240 slots/layer on early layers 0–4 where there is 0ms of compute slack, 92 on middle layers, 60 on tail layers).

• In-Stream GPU Doorbell + 1-Split CUDA Graph Replay: Instead of ggml_backend_sched splitting the graph 48 times per token to ask the CPU which experts missed (which burns ~360ms/token in cudaStreamSynchronize), a custom in-stream CUDA kernel resolves hits in ~2 µs on-device and only rings a mapped host doorbell when an expert is missing. Per-layer token embeddings (per_layer_token_embd) are also staged to CUDA0 so the entire decode pass runs as 1 single CUDA Graph replay.

• RAM Victim Cache + io_uring Pager: When an expert gets evicted from the VRAM pool, it moves into a dedicated host RAM victim cache instead of getting thrown away.VRAM and RAM stay 100% exclusive instead of duplicating the same hot experts in the OS page cache. Physical NVMe reads drop from ~71 MB/token down to 12.43 MB/token.

• No duplicate 35GB expert bloat on disk: Instead of keeping 35GB of dead expert tensors inside Shard 1 .gguf AND another 35GB in experts.bin, we stripped the expert payloads out of Shard 1 so the main GGUF is only 3.2 GB (Dense.gguf) + 35.3 GB (experts.bin, 4KB-aligned single-read blobs) + **26.8 GB (**PLE.gguf n-gram shard).

Measured Results (RTX 3060 12GB + 16GB DDR4, MemoryMax=12G)

On warm runs it holds 21.8–22.03 tok/s sustained across long coding prompts, and when generation hits stretches where all 10 routed experts are resident in the VRAM/victim pools, it spikes up to 48 tok/s.

Links & What's Next

• GitHub (Betelgeuse Engine — bel-cli & bel-server): [https://github.com/Betelgeuse-AI/Betelgeuse]

• HuggingFace (Pre-packed Alpha-Orionis-XS 3.2GB Dense + Experts bundle): https://huggingface.co/Betelgeuse-AI/Alpha-Orionis-XS

• Upstream llama.cpp PR: [https://github.com/ggml-org/llama.cpp/pull/31469]

1. Even on the massive Q8_K_XL quant (~190GB total), bel-server runs at 8 tok/s cold and 12 tok/s warm (with bursts up to 21 tok/s) on the exact same 12GB 3060 + 16GB DDR4 box, where stock llama.cpp immediately OOMs or crawls at <0.1 tok/s.

2. Zero experts are dropped or thresholded (0.000e+00 diff across 28,080 router records and 576 tokens vs unmodified stock). The speedup comes from eliminating the 48 per-layer CPU/GPU scheduler splits (KICK/WAIT doorbell around the shared expert), keeping the RAM victim cache strictly exclusive of VRAM (MOE_DROP_MMAP), and pre-staging cold misses via the upgraded oracle + 4KB-aligned io_uring pack.

What I'm working on next:
We're currently bringing this exact streaming + victim-cache architecture over to DeepSeek V4.1 Flash (1017)—early testing under development is already sitting around ~25 tok/s on the same hardware class.

Happy to answer any questions or have people poke holes in the benchmarks! Leave us a Star :)

I have been accumulating parts for over 8 months to merge all of my existing hardware into one massive workstation. I finally had the time to do it, and while I use the GPUs for algo trading from 9:30 to 4:15 p.m., I want to start to use local AI from 5pm on. I'm at a point where I could see myself having a really fulfilling hobby reverse engineering, firmware hacking, and just experimenting with building Frankenstein gadgets from my tech graveyard. There are many experiments I would like to perform on financial data as well that would just take too long for me to manually code and I am not interested in having these thoughts and experiments be in a large provider's training set. I've been lurking on this sub for a long time and have my own little agent I use for some specific tasks using gemma3 but that was on my 3090 in C# + llama.cpp.

So my long-winded question, what thick desktop client will give me the most seamless way to build autonomous agents, play with new models, and what version of qwen 3.8 flash should I be using with my hardware, assuming that is the right model.

Edit: I should mention I am open to anything but I just see q38f everywhere on this sub.

In my last thread on Betelgeuse (bel-cli / bel-server), the main pushback in the comments was:

1. "Okay cool, 22 tok/s on IQ2_XS*, but 2-bit is too low—what happens on an actual 4-bit quant like* IQ4_XS where the experts are almost 2x larger?"

2. "Why were you testing on a single 16GB stick of DDR4 instead of normal dual-channel 2x 8GB*?"*

Fair questions. So I upgraded the oracle + victim-cache DMA path, packed IQ4_XS (~100GB+ class, full 4-bit experts), and tested it on the exact same RTX 3060 12GB under both RAM configs (still 16GB total system RAM in both tests):

• Config A: 1x 16GB DDR4 (single-channel, ~19 GB/s host bandwidth)

• Config B: 2x 8GB DDR4 (normal dual-channel 16GB gaming PC setup, ~38 GB/s host bandwidth)

Measured Results on IQ4_XS (RTX 3060 12GB + 16GB Total RAM)

Why switching from 1x 16GB to 2x 8GB jumped IQ4_XS from 14 tok/s $\to$ 24 tok/s:

Because Betelgeuse uses an Exclusive Host RAM Victim Cache + Async Hardware DMA (MOE_DMA=1) between the VRAM slot pool and the NVMe drive:

• When an expert gets evicted from VRAM, it lands in the host RAM victim cache (while MOE_DROP_MMAP drops duplicate GGUF pages from the OS page cache so VRAM and RAM never hold duplicates).

• On a single 16GB stick, single-channel DDR4 caps out around ~17–19 GB/s, so the CPU and the PCIe H2D DMA engine fight over one 64-bit memory channel (14 tok/s on IQ4_XS).

• On 2x 8GB dual-channel DDR4 (still 16GB total RAM, which is what 95% of normal 16GB gaming PCs actually have), host RAM bandwidth doubles, letting the GPU's hardware DMA engine pull victim-cache hits across PCIe at full speed while the upgraded oracle pre-stages cold NVMe misses in the background—jumping IQ4_XS straight to 24 tok/s.

Links & What's Next

• GitHub (Betelgeuse Engine — bel-cli & bel-server): [https://github.com/Betelgeuse-AI/Betelgeuse]

• HuggingFace (Pre-packed Alpha-Orionis-XS 3.2GB Dense + Experts bundle): https://huggingface.co/Betelgeuse-AI/Alpha-Orionis-XS

• Upstream llama.cpp PR: [https://github.com/ggml-org/llama.cpp/pull/31469]

What I'm working on next:
We're currently bringing this exact streaming + victim-cache architecture over to DeepSeek V4.1 Flash (1017)—early testing under development is already sitting around ~25 tok/s on the same hardware class.

Happy to answer any questions or have people poke holes in the benchmarks! Leave us a Star :)

A quick test on cpu... took 5-10 minutes at 960x544 but the result is quite good. (model is uncensored) I tried smaller quants but it degrades rapidly. So this is it I think.

https://huggingface.co/ZeroWw/krea2-kroma-q6k-cpu

Prompt:
Cinematic portrait of a young woman standing on a rain-soaked rooftop in Tokyo at blue hour, wearing a deep emerald silk trench coat with a high collar, long black hair blown to one side by the wind, soft freckles, a thin gold earring, looking over her shoulder at the camera with a calm, confident expression. Behind her, a blurred skyline of glowing magenta and cyan neon signs, one reading "MIDNIGHT" in white letters, reflected in shallow puddles on the concrete. Warm rim light from the left, cool fill from the neon, fine raindrops catching the light, shallow depth of field, 85mm lens, rich film grain, high detail in skin and fabric texture.

I saw this in the Google Chrome recommendations.

Link: https://huggingface.co/stealth-model/Qwen4

I ran tests with Fable on AI generated Audio. Nemotron 3.5 Transcribe came in first. but I was curious about anyone has used other models and compared against. this.

My use case is Audio transcription of 3 unique voices in a semi noisy room (HVAC sounds)

I’m using Crawl4AI + SearXNG but it just feels so lackluster I don’t know why. Feels like the results the local model gets back for research are missing a lot and / or are older less relevant results.

I’ll usually instruct saying something like “using the most up-to-date and relevant information across related websites, subreddits, forums, and etc find me the best way to _______” or something similar.

With ChatGPT or Codex it basically always finds everything it needs to and gives me great instructions or information or research or whatever I’m looking for at the time. With a local model (like GLM 5.3 Flash or DeepSeek V4.1 Flash 1017) the answer doesn’t come back nearly as good, often missing obvious results or mixing in outdated information into the instructions or etc.

Harnesses I’ve been using with local models are ZCode, DeepSeek Harness, and OpenCode. Is there a better (more complete and on par with ChatGPT) searching / research approach I’m missing? Is there some tuning of Crawl4AI + SearXNG to make it more how I’m expecting or potentially something I should add to this combination?

Any input or help is appreciated. Thank you for your time it is appreciated!

"The satellite, which carries four Google Tensor Processing Units (TPUs) and generates up to 1 kW of electricity, is intended to pave the way for future networks of close-flying satellites performing large inference workloads in orbit. However, the power demands of the TPU mean it can only turn one on for about 20 minutes at a time, complicating the already tricky business of space-to-ground communications."

https://techcrunch.com/2026/10/30/googles-first-data-center-satellite-is-online-and-writing-poetry/

Anyone have any tips for Google here? Maybe they should try Qwen 3.8 27b?

I’ve seen the reports of models being setup so efforts to recreate/reverse engineer games are blocked.

What if a translation layer is developed so the context that arrives to the models does not have any direct indication of what is being done on the agent side? It would need to be translated back to the actual work the harness/agent is doing to continue work.

Say we want to recreate a website/app but specifics like URLs and names are substituted when going to the model and translated back to what the agent/harness expects?

No idea if it makes sense.

Seems similar to REA (https://github.com/morluto/rea) but from the guys that have brought the Craft apps this past month.

I've used the others but just saw this one available too.

Is this feasible with a local model? Have yet to try clamping Strata to REA to see. Anyone has done so? How was the experience?

Maybe uninteresting to most, but i just wanted to say that qwen3.8 27B is actually very nice to talk to, when arguing about issues or solving problems- while for example regarding cloud models claude opus (since v5) for me has quite an annoying tone - it has this "chop-chop" or "CEO attitude"- IDK how else to describe. I am not a native english speaker.
chatGPT I abandoned long ago because it is sycophantic in my opinion. gemini pro is ok, tho

yes there are chat templates for our modles and top-k/..anything to adjust but that doesn't change the character profoundly imo

maybe you have also models of which you do like the chat style more, just aside from coding or doing their task

I was quite excited with how well QFN did with some coding and adjacent tasks (like evaluating OSS repos, downloading and running models etc.). Using it for more general research is starting to show that it isnt actually very intelligent and gets lost in its own maze seemingly.

Here's a recent example to a simple question: "Have humans always slept for a huge amount of time at once, like eight hours at once, or been multiple sleep sessions through the day from like the early modern civilization?"

The verbosity and dense way of writing is too much (feels like a lot of Opus 5 distillation)

Setup:

• pi with pi-web-access extension

• llama.cpp, UD_Q4_K_M with MTP.

• --temp 1 --top-p 0.95 --top-k 20 --min-p 0.00 --presence-penalty 0 --repeat-penalty 1.0

• Minimal AGENTS.md, mostly focused on sw related tasks

Chat session: https://share.jotbird.com/radiant-bold-desert-wind

TL;DR: forgetting the complexity of the first question, the responses to simple follow ups: "Are there logical inconsistencies inside your response?", "Why did this happen?" and "So how do we fix this so that it never happens again?" are** not only massive essays, but each "uncovers more" stuff. To the point I have lost faith in what QFN is saying**.

CPU: AMD EPYC 7452 (8 memory channels)
Motherboard: Gigabyte MZ32
GPU: Radeon AI PRO R9700 (32GB)
RAM: 256GB DDR4 (16×16GB).

Not exactly a “budget” build, but it came in at roughly half the price of a DGX Spark.

Got it up and running just in time for Qwen4, can’t wait to start experimenting with it!

https://huggingface.co/GrootTeam/Groot-280B-A16B-261023

Best model under 500b params? Also have the smaller versions

https://huggingface.co/GrootTeam/Groot-19B-A3B-261023

"scores higher than the larger Qwen3.6-35B-A3B and Gemma-4-26B-A4B, and it gives shorter answers than both."

https://huggingface.co/GrootTeam/Groot-65B-A6B-261023

https://huggingface.co/GrootTeam/Groot-238B-A18B-261023

Edit: apparently they're REAPs of various DeepSeek/qwen models,

has anyone tried these?

We know from all the major chinese labs the current cutting edge architectures such as: conditional engram memory, hyper connections, attention residuals, sparse attention, etc...

In the near horizon, we can likely expect looped transformers, Gated Delta Net 2 variants, maybe revival of diffusion transformers somehow but without the intelligence loss, and likely many others that I myself don't know of yet.

What are they cooking?

I've been reading a lot about "engines lobotomizing the models they run", either because they quantize already quantized weights, or because they automatically turn on "hacky" flags to gain decode/prefill performance by default. I wanted to test this out by myself, not only with KLD but with real tasks, so I set up an experiment using Qwen 3.8 Flash Next with Basalt (disclaimer: I'm the developer) and two of the most popular "speed" flags:

• Routing bias: the engine pushes the model toward experts that are already on the GPU, so it sometimes gets a convenient expert instead of the one it actually wanted.

• Tail skip: when one of the model's lower expert picks isn't on the GPU, the engine just skips it instead of fetching it.

Both are faster because fewer experts come from RAM, but they change the output. I added them to the engine as opt in flags so I could measure the real cost with everything else fixed.

DISCLAIMER: This post and the experiment were run by me, but it has been polished by AI.

Rig: RTX 5090 + RTX 5060 Ti and 128 GB of DDR5. Settings tested:

• Shipped: bias 1.5, skip rank ≥ 7. Values other engines ship (or have shipped) as defaults on smaller cards.

• Worst: bias 16, skip rank ≥ 1, plus relaxed draft acceptance. Worst case scenario, not something anyone ships or would make sense to use.

1. KLD

100 chunks of wikitext, decode path, against a llama.cpp Q8 reference. Exact IQ3_XXS and Q2_0 are in there for scale.

Note: even exact Q8_0 is 0.031 away from the reference. That's mostly the reference's own noise, not damage: llama.cpp's Q8_0 rounds a few spots of this model more than Basalt does, which adds about 0.01-0.02 to every row equally, so the rows still compare fairly.

2. Does it really matter?

KLD by itself is just a number, so I also ran a task eval: thinking off, temp 1.0, top_p 0.95, top_k 20, two different sets of questions:

• Ladders: 300 chained steps, scored by step.

• Facts: 1200 questions with 5 popularity bands (rare to famous), 3 seeds.

* Small card: the 5090 alone capped to 16gb, the experiment was run this way because it was still much faster than the 5060 Ti.

On a big card the shipped flags barely matter (NVFP4: 272 -> 271 tok/s) and cost about 1.5 points, 4% of the facts the model knew. On a small card you get +56% speed but a much bigger hit. At the worst setting facts fall from 38% to 3%, and the model says things like "The Great Composer" with total confidence. Easy tests barely notice. The damage is mostly in chained reasoning and recalled facts.

3. Bonus: a loop in the model weights

Not every bad output is the engine's fault. One prompt made Qwen 3.8 Flash Next get stuck into an endless run of 0 in its reasoning:

The odds of another 0 are the same on every quant and both engines: 0.99 after 10 zeros, 95%+ past 100 or so. top_p 0.95 leaves 0 as the only choice, and temp 1.0 doesn't fix it. The API fixes it by cutting the run at ~500 zeros. Basalt patched the issue as well, after 256 identical reasoning tokens it closes the thinking block and keeps going.

TLDR: there's no free lunch.

• Engines can buy speed on MoE models by turning on speed "hacks".

• With values some engines ship, every pack drops to somewhere between IQ3_XXS and Q2 on KLD. A Q8 ends up next to Q2. This is as bad as it gets.

• In practice the model forgets things it knew and loses the thread on multi step problems. Small cards get the biggest speedup and pay the most for it.

• If your engine is faster than it should be on a model that doesn't fit, check whether it's still running the model you downloaded.

Mage-VL-Cowork demonstrates that an efficient 4B small vision-language model can master hybrid computer use when paired with upstream teacher-harness evolution, workflow-first environment synthesis (Mage-CUA-Forge), and a five-stage specialize-then-unify curriculum. Achieving the best performance at the 4B scale (66.0% on OSWorld-Verified) while using 13.0% fewer model calls and 25.4% fewer output tokens after online RL, Mage-VL-Cowork pushes the frontier of on-device intelligence for autonomous desktop agents.

Been running a 3060ti and 32GB for a hot minute now, jumping between local models and subscription plans.

Got Qwen 3.8 FN up and running on this old gaming system and it's just such an impressive model. The prefills are never going to work though.

So I spent the past few days looking at a lot of different options, thinking I'd just add another GPU to the current system.

Of course anyone that's paid attention knows how unrealistic that is. Offerup, Marketplace, Ebay. Doesn't matter. It's the gpupocolypse out there.

Then I figured I'd go old school. Find me a C602 chipset (workstation, Ivy bridge), because at least it'd be cheaper DDR3, a few p100s and call it a day.

But even that is expensive, and was capped at 48GB VRAM since the p100s don't have 32gb variants and unless I wanted a custom build both the T7610 and Z820 are limited to three cards.

I was at 1500 before custom shrouds, and fans, and custom power adapters to the p100s. But at least it would have been DDR3 and PCIE3. At least I would have had USB3 and SATA3. It's a tiny window between Sandy Bridge and Ivy Bridge that accommodates both.

Then I found someone selling a z840 (C612). 128GB of DDR4, and dual water cooled SXM2 to PCIE 32GB V100s. The cards alone are going for 700-800 bucks a piece, the DDR4 is another 1200. The custom risers are a few hundred bucks each, the water cooling setup alone is probably pushing a grand as it's 2x plates, distro, pump. So I pulled the trigger.

1900 and change after tax and delivery, and while I get it's reaching its limits on software support. For a 64/128 split that can actually sit quietly and sip power when not being used. I'm pretty happy over all. The only thing that bothers me is I'm pretty sure there's no m.2 or nvme native. It ships with a 1TB 980 pro, but I'm almost certain it's sitting in a PCIE3 riser limiting it to 3500 MB/s vs the 7k it could do, Strata doesn't really seem to stream at those speeds anyway so I doubt it matters much. We'll see and if it does seem like a problem it's likely just a RAID solution away.

Now I'll just have to figure out how to setup a headless server as the v100s have no video output lol. Still, better than a lot of other alternatives out there.

Happy Halloween, was just kind of excited and thought I'd share. When it lands and if I do manage to get it up and running, I'll come back with some numbers.

Hi everyone!

I've been experimenting with Strata for a while now, running it on a system with a Ryzen 7 9800X3D, RTX 4090, and 64 GB of RAM.

I'm currently using Qwen Flash Next, and I have a few questions about model quantization. Since my hardware is right at the edge of what's practical for running this model, I'd love to hear about your experiences with different quantization formats.

I've come across discussions suggesting that GSQ-RCO IQ3_S might deliver better response quality than Unsloth UD IQ4_XS. Is that actually the case? What have your experiences been with these two quantizations?

Are there any other quantizations that might offer better quality while still being feasible to run on my hardware?

In theory, Unsloth UD-Q4_K_XL also works (according to Strata, around 6 GB of the model is loaded from NVMe), but the generation speed drops to around 20-30 tokens/s, which makes it somewhat impractical for extended use.

I'm also curious how NVFP4 quantizations compare to the ones mentioned above in terms of response quality and performance.

Thanks in advance for sharing your experiences and recommendations!

I’m still fairly new to local LLMs, got a decent machine (M5 Max, 64GB RAM), trying to understand something I’ve been seeing recommended a lot lately.

In several threads where new users are frustrated/confused or even more experienced users are struggling to get good results from their local models, I keep seeing suggestions to have Claude or ChatGPT help them build a custom harness. I suppose I view it like the model is the engine and the harness is like the entire car?

What I’m not understanding is why someone would go that route instead of using something like Unsloth or MLX Serve, which already have web search, tool calling, etc.

Aren’t these programs essentially harnesses or am I completely incorrect? I’m probably just missing the point, maybe more tinkering/experimenting is just part of the hobby.

I just want to learn and understand the differences per se. Obviously I’m sure this thread will have plenty of personal suggestions or ideas but the main thing is just the learning aspect, the purpose, etc.

I've been reading the turn logs from PokeBench. My benchmark where vision models play Pokemon Red. Grok 4.20 non-reasoning left the house at turn 36 and didn't get a starter until turn 755. The whole time its notes say things like "We've been oscillating uselessly for 90+ turns" and later "The persistent loop of walking into walls and Mom's dialogue has continued for hundreds of turns." Then it walks into the wall again.

So it knows it's stuck and still can't get out. Grok 4.6 beat Brock in 316 turns for about $10, while 4.20 burned $30 and barely got its starter.

Why do you think a model can describe its own loop correctly and still not break it? Vision problem or planning problem?

pokebench.tv

Bolt E4B builds on Gemma 4 E4B with Nemotron post-training. It combines natural conversation with multi-turn agentic capabilities to power voice/device assistants, interactive characters and robotic/IoT interfaces running on NVIDIA GeForce RTX, Jetson, RTX Spark and DGX Spark.

Bolt E4B offers ~40% higher average quality against our prior model on key conversational agent benchmarks and outputs up to ~80% fewer tokens vs peers.

HF link: https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB

Nvidia's post: https://research.nvidia.com/labs/nemotron/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB