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+# Qwen3-VL 30B A3B
+
+This package runs `Qwen3-VL-30B-A3B-Instruct` through a Bazel-pinned
+llama.cpp CUDA runtime. It is tailored to the current WSL machine: an RTX 4070
+Ti with 12 GB VRAM and 32 GB host RAM.
+
+The default model is Unsloth's 11.26 GB `Q2_K` GGUF plus the 1.08 GB FP16
+vision projector. This lower quantization is intentional: the official
+18.56 GB `Q4_K_M` model cannot fit on this GPU and is too large for the
+current WSL memory allocation. Model weights are downloaded to
+`~/.cache/zenbu/qwen3-vl-30b-a3b`, not the repository or Bazel cache.
+
+## Setup
+
+Verify that Bazel's pinned runtime can see the GPU:
+
+```bash
+bazel run //qwen3_vl:preflight
+```
+
+Download and verify the model files:
+
+```bash
+bazel run //qwen3_vl:download_model
+```
+
+Start an interactive image prompt:
+
+```bash
+bazel run //qwen3_vl:chat -- \
+  --image /path/to/image.jpg \
+  --prompt "Describe this image." \
+  --n-predict 256
+```
+
+Start the OpenAI-compatible server:
+
+```bash
+bazel run //qwen3_vl:serve
+curl.exe http://127.0.0.1:8080/health
+```
+
+The pinned CUDA executable runs on the Windows side of WSL interop. Windows
+browsers and clients can use `http://127.0.0.1:8080`; use `curl.exe`, rather
+than Linux `curl`, when checking it from a WSL shell.
+
+Runtime tuning is available through environment variables:
+
+```bash
+QWEN3_VL_GPU_LAYERS=16 \
+QWEN3_VL_CONTEXT_SIZE=1024 \
+  bazel run //qwen3_vl:chat -- --prompt "Hello" --n-predict 64
+```
+
+Increase `QWEN3_VL_GPU_LAYERS` until GPU memory is nearly full. Reduce it if
+llama.cpp reports an allocation failure. Keep the context small on this
+machine because the KV cache also consumes GPU and system memory.
+
+The WSL instance currently exposes about 15 GB of RAM even though the host has
+32 GB. If model loading fails under memory pressure, create `%UserProfile%\.wslconfig`
+on Windows with:
+
+```ini
+[wsl2]
+memory=28GB
+swap=16GB
+```
+
+Then run `wsl --shutdown` from Windows before retrying.