Running local models is good now

Post by dghlsakjg » Tue, Jun 16, 2026, 4:22 PM UTC

Barbing wrote: post #126

iagooar wrote: post #115

I love running two models locally: qwen3.6 27B 8bit (dense) and qwen3.6 35B 4bit (MoE). The 27B is the smarter, more reliable one - but it is slower. The 35B is faster, still very smart but below 27B, a bit less reliable. The reason is the MoE - Mixture of Experts architecture, which only activates a subset of parameters, making the model much much faster. I run the 27B on a MacBook Pro M5 Max + 40 GPU cores + 128GB…

Did you get a Brave search API key or something for that “Hermes”?

Post by greenavocado » Tue, Jun 16, 2026, 4:23 PM UTC

zozbot234 wrote: post #116

c0rruptbytes wrote: post #104

I don't know about good, I use a lot of local models and they're still pretty painful to run locally You have dense models (qwen 27b, gemma 31b) who are pretty smart, but pretty slow You have MoE models (gemma 26b, qwen 35b, north mini code 30b) who are pretty fast, but make a lot of mistakes You need a lot of memory to run these well, quantization makes tool calling weaker, so most run at 4 bit quants and are wonder…

Maybe we shouldn't be running these models on laptops with their thermally constrained form factor, and we shouldn't expect quick inference on a par with a large cloud-based platform either, at least not for near-SOTA model quality. It's still worth it to avoid becoming massively reliant on centralized services.

  # 1,257 tokens 17s 72.18 t/s

  $env:CUDA_DEVICE_SCHEDULE = "SPIN"
  cd D:\src\llama.cpp\
  .\build\bin\Release\llama-server.exe `
    --port 8080 `
    --host 127.0.0.1 `
    -m "D:\LLM\Qwen3.6-35B-A3B-MTP-UD-Q4_K_XL.gguf" `
    -fitt 2048 `
    -c 98304 `
    -n 32768 `
    -fa on `
    -np 1 `
    --kv-unified `
    -ctk q8_0 `
    -ctv q8_0 `
    -ctkd q8_0 `
    -ctvd q8_0 `
    -ctxcp 64 `
    --mlock `
    --no-warmup `
    --spec-type draft-mtp `
    --spec-draft-n-max 2 `
    --spec-draft-p-min 0.1 `
    --chat-template-kwargs '{\"preserve_thinking\": true}' `
    --temp 0.6 `
    --top-p 0.95 `
    --top-k 20 `
    --min-p 0.0 `
    --presence-penalty 0.0 `
    --repeat-penalty 1.0

Post by embedding-shape » Tue, Jun 16, 2026, 4:23 PM UTC

zozbot234 wrote: post #108

embedding-shape wrote: post #89

Earlier quoted context omitted.

As mentioned, I've just finished the implementation and started playing around with it, seems to be doing similarly well inside of my own agent harness as similarly sized "traditional" LLMs. Of course, neither come close to SOTA models, but I suppose if we can figure out the scaling issues you mention, we'd get a bit closer. The performance just feels like it's too good to quickly ditch diffusion. Do you have more in…

The issues around training diffusion models are well known among researchers. They're likely to not be feasibly scalable far beyond the 26B size of DiffusionGemma itself, and their lower quality compared to an equally-sized auto-regressive model (the usual one-token-at-a-time flow) is also a matter of broad consensus.

Post by epistasis » Tue, Jun 16, 2026, 4:24 PM UTC

illegalsmile wrote: post #128

epistasis wrote: post #85

Earlier quoted context omitted.

> talking just way too much OMG this is such an annoying property, just shut the hell up please, and be concise. I suspect that this is an artifact of the thinking property, but please just summarize the thinking process far more concisely, where a single sentence answer is more than sufficient the frontier models seem devoted to going on to a minimum of 5 paragraphs and offering 3-5 new directions. And requests to p…

That's why you have to give claude and others directives/.md at the beginning so it doesn't go off the deep end with suggestions.

Post by prlin » Tue, Jun 16, 2026, 4:26 PM UTC