Enable GPU: LLM + Whisper on the RTX 5050, pick qwen3:8b

GPU acceleration is now on by default and verified end-to-end on the
Blackwell RTX 5050 (sm_120):

- Ollama offloads 100% to GPU (log: library=CUDA compute=12.0,
  BLACKWELL_NATIVE_FP4=1). compose passes GPU via CDI
  (devices: nvidia.com/gpu=all) to both ollama and javis.
- Whisper STT on GPU: faster-whisper>=1.1.0 + nvidia-cublas/cudnn cu12,
  LD_LIBRARY_PATH baked into the image. Verified float16 transcribe on
  sm_120; bridge auto-falls back to CPU when no GPU is present.
- Model: default chat model -> qwen3:8b (best 8GB-VRAM tool-calling,
  ~5GB Q4). Embed stays nomic-embed-text.
- README documents the host one-time setup (nvidia-container-toolkit +
  `nvidia-ctk cdi generate`) and GPU on/off.

Verified: image builds; GPU visible in both containers via compose;
ollama ps = 100% GPU; faster-whisper cuda OK + CPU fallback OK;
bridge /health 200.

bridge/requirements-bridge.txt

@@ -5,12 +5,19 @@
 
 # --- Brain runtime (imported when the reply engine loads) ---
 python-dotenv==1.0.1
-faster-whisper==1.0.3
+# >=1.1.0 pulls a ctranslate2 with Blackwell (sm_120) CUDA kernels.
+faster-whisper>=1.1.0
 mcp==1.13.1
 numpy<2.0.0
 rapidfuzz==3.6.1
 requests==2.32.3
 
+# --- CUDA libraries for GPU-accelerated Whisper (RTX 5050 / sm_120) ---
+# ctranslate2 dlopens these at transcribe time; LD_LIBRARY_PATH is set in the
+# Dockerfile to point at them. Verified working on Blackwell sm_120.
+nvidia-cublas-cu12
+nvidia-cudnn-cu12
+
 # --- Bridge HTTP service ---
 flask>=3.0.0