I still haven't experienced a local model that fits on my 64GB MacBook Pro and can run a coding agent like Codex CLI or Claude code well enough to be useful.

Maybe this will be the one? This Unsloth guide from a sibling comment suggests it might be: https://unsloth.ai/docs/models/qwen3-coder-next

  brew upgrade llama.cpp # or brew install if you don't have it yet

  llama-cli \
    -hf unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_XL \
    --fit on \
    --seed 3407 \
    --temp 1.0 \
    --top-p 0.95 \
    --min-p 0.01 \
    --top-k 40 \
    --jinja

  llama-server \
    -hf unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_XL \
    --fit on \
    --seed 3407 \
    --temp 1.0 \
    --top-p 0.95 \
    --min-p 0.01 \
    --top-k 40 \
    --jinja

Video is speed up. I ran it through LM Studio and then OpenCode. Wrote a bit about how I set it all up here: https://www.tommyjepsen.com/blog/run-llm-locally-for-coding

    cat docker-compose.yml
    services:
      llamacpp:
        volumes:
          - llamacpp:/root
        container_name: llamacpp
        restart: unless-stopped
        image: ghcr.io/ggml-org/llama.cpp:server-cuda
        network_mode: host
        command: |
          -hf unsloth/Qwen3-Coder-Next-GGUF:Q4_K_XL --jinja --cpu-moe --n-gpu-layers 999 --ctx-size 102400 --temp 1.0 --top-p 0.95 --min-p 0.01 --top-k 40 --fit on
    # unsloth/gpt-oss-120b-GGUF:Q2_K
        deploy:
          resources:
            reservations:
              devices:
                - driver: nvidia
                  count: all
                  capabilities: [gpu]

    volumes:
       llamacpp:

  FP8: https://huggingface.co/Qwen/Qwen3-Coder-Next-FP8

  Sequential (single request)

    Prompt     Gen     Prompt Processing    Token Gen
    Tokens     Tokens  (tokens/sec)         (tokens/sec)
    ------     ------  -----------------    -----------
       521        49            3,157            44.2
     1,033        83            3,917            43.7
     2,057        77            3,937            43.6
     4,105        77            4,453            43.2
     8,201        77            4,710            42.2

  Parallel (concurrent requests)

    pp4096+tg128 (4K context, 128 gen):

     n    t/s
    --    ----
     1    28.5
     2    39.0
     4    50.4
     8    57.5
    16    61.4
    32    62.0

    pp8192+tg128 (8K context, 128 gen):

     n    t/s
    --    ----
     1    21.6
     2    27.1
     4    31.9
     8    32.7
    16    33.7
    32    31.7

From very limited testing, it seems to be slightly worse than MiniMax M2.1 Q6 (a model about twice its size). I'm impressed.

Are there any clear winners per domain? Code, voice-to-text, text-to-voice, text editing, image generation, text summarization, business-text-generation, music synthesis, whatever.

Hope they update the model page soon https://chat.qwen.ai/settings/model

Does anyone see a reason to still use elevenlabs etc. ?

Does anyone any experience with these and is this release actually workable in practice?

Granted these 80B models are probably optimized for H100/H200 which I do not have. Here's to hoping that OpenClaw compat. survives quantization

Im currently using qwen 2.5 16b , and it works really well

It's one thing running the model without any context, but coding agents build it up close to the max and that slows down generation massively in my experience.

On a misc note: What's being used to create the screen recordings? It looks so smooth!

In practice, I've found the economics work like this:

1. Code generation (boilerplate, tests, migrations) - smaller models are fine, and latency matters more than peak capability 2. Architecture decisions, debugging subtle issues - worth the cost of frontier models 3. Refactoring existing code - the model needs to "understand" before changing, so context and reasoning matter more

The 3B active parameters claim is the key unlock here. If this actually runs well on consumer hardware with reasonable context windows, it becomes the obvious choice for category 1 tasks. The question is whether the SWE-Bench numbers hold up for real-world "agent turn" scenarios where you're doing hundreds of small operations.