LOCAL AIregistry
‹ RTX PRO 6000 Blackwell

Qwen3.8-27B

EXL3 6 bpw · tabbyapi · 256K context · RTX PRO 6000 Blackwell 96 GB

Alibaba's 27B dense model from August 2026, and the strongest model that fits one consumer card. It thinks before it answers, calls tools reliably and reads images, which makes it the default for coding agents and long projects.

52 tok/sdecode
568 tok/sprefill
256Kcontext window
Tested on this cardSep 25, 2026
Run it

With Omarchy Local AI it is one button. By hand, it is three steps: the weights, the config, the container. The server then answers on http://localhost:8000/v1.

  1. 1 Download the weights
    hf download turboderp/Qwen3.8-27B-exl3 \
      --revision 60d005a257b39ecb25e4ba23c1dd29df877d5c69 \
      --local-dir ~/models/Qwen3.8-27B-exl3-60d005a2
  2. 2 Write the server config
    cat > qwen3.8-27b.tabbyapi.256k.yml <<'EOF'
    network: {host: 0.0.0.0, port: 5000, disable_auth: true, disable_fetch_requests: true, send_tracebacks: false, api_servers: [OAI], sse_ping_interval: 15}
    logging: {log_prompt: false, log_generation_params: false, log_requests: false, log_chat_completion_requests: false}
    model:
      model_dir: /workspace/models
      model_name: Qwen3.8-27B-exl3-60d005a2
      backend: exllamav3
      inline_model_loading: false
      max_seq_len: 262144
      cache_size: 1052672
      cache_mode: Q8
      tensor_parallel: false
      gpu_split_auto: true
      autosplit_reserve: [256]
      chunk_size: 2048
      output_chunking: true
      max_batch_size: 4
      vision: true
      reasoning: true
      start_in_reasoning: always
      tool_format: qwen3_coder
      template_vars_default: {}
    draft_model: {draft_mode: mtp}
    memory: {sysmem_recurrent_cache: 4096, sysmem_kv_cache: 0, cuda_malloc_async: true}
    EOF
  3. 3 Start the server
    docker run --rm \
      --gpus all \
      -p 8000:5000 \
      --shm-size 8g \
      -v ~/models/Qwen3.8-27B-exl3-60d005a2:/workspace/models/Qwen3.8-27B-exl3-60d005a2:ro \
      -v $PWD/qwen3.8-27b.tabbyapi.256k.yml:/app/config.yml:ro \
      --entrypoint /opt/venv/bin/python3 \
      ghcr.io/0xsero/tabbyapi-exl3@sha256:0f83e6198dc3be2561652d8df2525a7d1a69733e12fc8e25cbc5bc319a8a3ad1 \
      main.py \
      --config /app/config.yml
What it passed
✓ loadLoads and serves within an hour
✓ chatAnswers a plain question and stops on its own
✓ reasoningThinks separately and gets 17 × 23 right
✓ toolsCalls a tool with the right arguments and uses the result
✓ contextRecalls a code buried in a prompt that fills 85% of the window
✓ speedDecodes at 15 tok/s or more

Passed all six checks on a real RTX PRO 6000 WS on Sep 25, 2026.

Details
Weights
turboderp/Qwen3.8-27B-exl3 @ 60d005a257 ›
Image
ghcr.io/0xsero/tabbyapi-exl3@sha256:0f83e6198dc3be2561652d8df2525a7d1a69733e12fc8e25cbc5bc319a8a3ad1
Engine profile
tabbyapi-exl3 ›
Recipe file
registry/recipes/nvidia/rtx-pro-6000-blackwell-96gb/qwen3.8-27b.tabbyapi.256k.json ›
Model released
Aug 5, 2026
Tested
vast, RTX PRO 6000 WS