Chunk 8.1

This chunk opened with the Belgium and Czechia instances failing in distinct ways—Belgium was killed by the manager's 20-minute benchmark timeout, while Czechia suffered a gRPC transport error on its first batch proof. Tactical fixes were applied: the benchmark timeout was increased to 45 minutes, a "post-restart warmup" proof was added to `benchmark.sh` to warm GPU kernels before the timed batch, and the partition worker logic was refined to use `pw=8` for ~256GB machines to balance speed and memory safety. Despite these fixes, new instances continued to fail. A new Belgium instance achieved only 35.9 proofs/hour, falling below the 50 proofs/hour minimum, and a new Czechia instance crashed with a bench_rate of 0, likely due to an OOM crash during the post-restart warmup or batch benchmark. These persistent failures highlighted the unreliability of predicting real-world proving performance from hardware specs alone (e.g., 2x A40 with 2TB RAM underperformed a single RTX 4090), prompting a fundamental strategic shift away from hardcoded thresholds. The session pivoted to building a data-driven, experimental system to automatically discover optimal hardware. The implementation included a new `host_perf` database table to track benchmark results per host, an API to search Vast.ai offers filtered by GPU/RAM/price while overlaying known host performance, a deploy endpoint, and the foundational UI code. The default minimum proofs/hour rate was also made configurable, setting the stage for a robust, self-tuning deployment pipeline.

From Tactical Firefighting to Data-Driven Discovery: The Pivot That Reshaped a GPU Proving Pipeline 3253 words

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