feat: GPU bakeoff — 3090 Ti vs V100 vs Strix Halo

Cross-host Gemma 4 throughput comparison across three architectures.
Harness at scripts/gpu-bakeoff/; writeup at
docs/reference/gpu-bakeoff-2026-04-20.md.

Key findings:
- RTX 3090 Ti wins decode decisively (128 tok/s on gemma4:26b MoE Q4,
  ~4.7× faster than gemma4:31b dense on the same card).
- AMD Strix Halo iGPU lands at ~42% of 3090 Ti decode on ~25% of the
  memory bandwidth — good SIMD utilization, especially for MoE.
- V100 numbers are DEGRADED: CT 167 ai-visualizer SDXL consumes 31/32
  GB of its VRAM, forcing Gemma 4 models 95% onto CPU. Isolated V100
  run requires SDXL eviction — left as follow-up.
- MoE vs dense is the dominant latency factor across all GPUs: ~4 B
  active params of gemma4:26b beats 31.3 B active of gemma4:31b by
  the same ratio (~4.7×) on every card tested.

Methodology: 1 warmup + 3 measurement runs per (host × model ×
prompt-length), Ollama's canonical timing fields, temp=0 greedy,
num_predict=256. All three Ollama servers accessed via HTTP (Strix
via Tailscale).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

scripts/gpu-bakeoff/runs/steel141/gemma4-31b/long.json

@@ -0,0 +1,81 @@
+{
+  "host": "steel141",
+  "gpu": "RTX 3090 Ti",
+  "vram_gb": 24,
+  "model_alias": "gemma4:31b",
+  "model_tag": "gemma4:31b-it-q4_K_M",
+  "prompt_key": "long",
+  "prompt_chars": 1614,
+  "num_predict": 256,
+  "num_ctx": 4096,
+  "runs": [
+    {
+      "prompt_tokens": 318,
+      "prompt_eval_ms": 38.2,
+      "prefill_tok_per_s": 8318.85,
+      "output_tokens": 256,
+      "eval_ms": 9390.5,
+      "decode_tok_per_s": 27.26,
+      "load_ms": 317.4,
+      "total_ms": 9886.3,
+      "harness_wall_s": 9.89,
+      "done_reason": "length"
+    },
+    {
+      "prompt_tokens": 318,
+      "prompt_eval_ms": 42.7,
+      "prefill_tok_per_s": 7454.7,
+      "output_tokens": 256,
+      "eval_ms": 9429.0,
+      "decode_tok_per_s": 27.15,
+      "load_ms": 316.0,
+      "total_ms": 9929.8,
+      "harness_wall_s": 9.933,
+      "done_reason": "length"
+    },
+    {
+      "prompt_tokens": 318,
+      "prompt_eval_ms": 41.2,
+      "prefill_tok_per_s": 7716.07,
+      "output_tokens": 256,
+      "eval_ms": 9477.4,
+      "decode_tok_per_s": 27.01,
+      "load_ms": 334.3,
+      "total_ms": 9996.2,
+      "harness_wall_s": 10.0,
+      "done_reason": "length"
+    }
+  ],
+  "warmup": {
+    "prompt_tokens": 318,
+    "prompt_eval_ms": 967.7,
+    "prefill_tok_per_s": 328.62,
+    "output_tokens": 256,
+    "eval_ms": 9339.6,
+    "decode_tok_per_s": 27.41,
+    "load_ms": 324.2,
+    "total_ms": 10774.3,
+    "harness_wall_s": 10.778,
+    "done_reason": "length"
+  },
+  "summary": {
+    "prefill_tok_per_s": {
+      "min": 7454.7,
+      "median": 7716.07,
+      "max": 8318.85,
+      "n": 3
+    },
+    "decode_tok_per_s": {
+      "min": 27.01,
+      "median": 27.15,
+      "max": 27.26,
+      "n": 3
+    },
+    "total_ms": {
+      "min": 9886.3,
+      "median": 9929.8,
+      "max": 9996.2,
+      "n": 3
+    }
+  }
+}