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Add persistent dictation, prewarmed WebRTC speech input, Copilot SDK routing, animated conversation lifecycle controls, parking, and architecture coverage.
author MrJuneJune <me@mrjunejune.com>
date Tue, 18 Aug 2026 19:14:53 -0700
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1 # Qwen3-VL 30B A3B
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2
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3 This package runs `Qwen3-VL-30B-A3B-Instruct` through a Bazel-pinned
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4 llama.cpp CUDA runtime. It is tailored to the current WSL machine: an RTX 4070
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5 Ti with 12 GB VRAM and 32 GB host RAM.
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6
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7 The default model is Unsloth's 11.26 GB `Q2_K` GGUF plus the 1.08 GB FP16
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8 vision projector. This lower quantization is intentional: the official
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9 18.56 GB `Q4_K_M` model cannot fit on this GPU and is too large for the
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10 current WSL memory allocation. Model weights are downloaded to
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11 `~/.cache/zenbu/qwen3-vl-30b-a3b`, not the repository or Bazel cache.
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12
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13 ## Setup
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14
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15 Verify that Bazel's pinned runtime can see the GPU:
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16
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17 ```bash
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18 bazel run //qwen3_vl:preflight
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19 ```
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20
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21 Download and verify the model files:
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22
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23 ```bash
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24 bazel run //qwen3_vl:download_model
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25 ```
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26
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27 Start an interactive image prompt:
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28
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29 ```bash
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30 bazel run //qwen3_vl:chat -- \
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31 --image /path/to/image.jpg \
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32 --prompt "Describe this image." \
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33 --n-predict 256
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34 ```
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35
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36 Start the OpenAI-compatible server:
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37
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38 ```bash
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39 bazel run //qwen3_vl:serve
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40 curl.exe http://127.0.0.1:8080/health
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41 ```
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42
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43 For the Linux Infinite Canvas process, start the server on the Windows WSL
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44 adapter instead:
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46 ```bash
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47 bazel run //qwen3_vl:serve_canvas
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48 ```
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50 The canvas detects the WSL gateway and uses that address by default.
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51
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52 The pinned CUDA executable runs on the Windows side of WSL interop. Windows
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53 browsers and clients can use `http://127.0.0.1:8080`; use `curl.exe`, rather
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54 than Linux `curl`, when checking it from a WSL shell.
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55
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56 Runtime tuning is available through environment variables:
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57
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58 ```bash
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59 QWEN3_VL_GPU_LAYERS=16 \
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60 QWEN3_VL_CONTEXT_SIZE=1024 \
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61 bazel run //qwen3_vl:chat -- --prompt "Hello" --n-predict 64
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62 ```
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63
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64 Increase `QWEN3_VL_GPU_LAYERS` until GPU memory is nearly full. Reduce it if
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65 llama.cpp reports an allocation failure. Keep the context small on this
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66 machine because the KV cache also consumes GPU and system memory.
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67
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68 The WSL instance currently exposes about 15 GB of RAM even though the host has
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69 32 GB. If model loading fails under memory pressure, create `%UserProfile%\.wslconfig`
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70 on Windows with:
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71
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72 ```ini
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73 [wsl2]
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74 memory=28GB
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75 swap=16GB
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76 ```
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77
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78 Then run `wsl --shutdown` from Windows before retrying.