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/pve197/gemma4-26b/short.json

@@ -0,0 +1,81 @@
+{
+  "host": "pve197",
+  "gpu": "Tesla V100-PCIE-32GB",
+  "vram_gb": 32,
+  "model_alias": "gemma4:26b",
+  "model_tag": "gemma4:26b",
+  "prompt_key": "short",
+  "prompt_chars": 78,
+  "num_predict": 256,
+  "num_ctx": 4096,
+  "runs": [
+    {
+      "prompt_tokens": 27,
+      "prompt_eval_ms": 112.5,
+      "prefill_tok_per_s": 240.05,
+      "output_tokens": 256,
+      "eval_ms": 30919.5,
+      "decode_tok_per_s": 8.28,
+      "load_ms": 531.1,
+      "total_ms": 31828.4,
+      "harness_wall_s": 31.832,
+      "done_reason": "length"
+    },
+    {
+      "prompt_tokens": 27,
+      "prompt_eval_ms": 113.6,
+      "prefill_tok_per_s": 237.6,
+      "output_tokens": 256,
+      "eval_ms": 30399.9,
+      "decode_tok_per_s": 8.42,
+      "load_ms": 479.4,
+      "total_ms": 31242.1,
+      "harness_wall_s": 31.246,
+      "done_reason": "length"
+    },
+    {
+      "prompt_tokens": 27,
+      "prompt_eval_ms": 111.0,
+      "prefill_tok_per_s": 243.16,
+      "output_tokens": 256,
+      "eval_ms": 30712.9,
+      "decode_tok_per_s": 8.34,
+      "load_ms": 483.2,
+      "total_ms": 31552.8,
+      "harness_wall_s": 31.557,
+      "done_reason": "length"
+    }
+  ],
+  "warmup": {
+    "prompt_tokens": 27,
+    "prompt_eval_ms": 843.7,
+    "prefill_tok_per_s": 32.0,
+    "output_tokens": 256,
+    "eval_ms": 30499.4,
+    "decode_tok_per_s": 8.39,
+    "load_ms": 5877.7,
+    "total_ms": 37664.4,
+    "harness_wall_s": 37.668,
+    "done_reason": "length"
+  },
+  "summary": {
+    "prefill_tok_per_s": {
+      "min": 237.6,
+      "median": 240.05,
+      "max": 243.16,
+      "n": 3
+    },
+    "decode_tok_per_s": {
+      "min": 8.28,
+      "median": 8.34,
+      "max": 8.42,
+      "n": 3
+    },
+    "total_ms": {
+      "min": 31242.1,
+      "median": 31552.8,
+      "max": 31828.4,
+      "n": 3
+    }
+  }
+}