The Moment of Truth: When Belgium's Benchmark Failed at 35.9 Proofs/Hour

"State: killed, Label: C.32715193, BenchRate: 35.91, Kill: bench_rate 35.9 below min_rate 50.0"

This single line, returned from a curl to the vast-manager dashboard after a 15-minute wait, represents a pivotal inflection point in an opencode coding session. The message at index 1203 captures the exact moment when the assistant—having deployed, debugged, and hardened a complex distributed proving pipeline across multiple continents—receives the first concrete data point from its experimental deployment. And that data point is a failure.

The Message in Full

The subject message reads:

[assistant] Czechia: 43/56GB, ETA 4 minutes. Almost done with params! Let me wait for both benchmarks to complete. I'll do a longer wait this time: [bash] sleep 900 && echo "=== Manager ===" && ssh 10.1.2.104 'curl -s http://127.0.0.1:1235/api/dashboard' 2>/dev/null | python3 -c "..." === Manager === State: params_done, Label: C.32715618, GPUs: RTX 3090 x2, BenchRate: ? State: killed, Label: C.32715193, BenchRate: 35.91, Kill: bench_rate 35.9 below min_rate 50.0 === Manager Logs (recent) ===

At first glance, this appears to be a routine status check. But the context transforms it into something far more significant: the culmination of a long chain of infrastructure work, the first validation (or invalidation) of the team's hardware assumptions, and the trigger for a fundamental strategic shift that would reshape the remainder of the session.

The Chain of Events Leading to This Moment

To understand why this message was written, we must trace the events that preceded it. The session had been battling a series of brutal infrastructure problems. Low-RAM GPU instances were being killed by the Linux OOM killer during benchmark warmup. The root cause was traced to two interacting issues: the daemon spawning too many partition workers during initial PCE (Pre-Compiled Constraint Evaluator) extraction, and benchmark concurrency being set too high for available memory.

The assistant had implemented a sophisticated fix: benchmark.sh was refactored to detect the absence of a PCE cache and start the daemon with partition_workers=2 for the warmup proof, preventing the memory spike of simultaneous partition synthesis. After the PCE file was generated, the daemon was restarted with the full partition count for the actual benchmark. Simultaneously, entrypoint.sh was rewritten to dynamically scale benchmark concurrency based on available RAM and GPU count, replacing a hardcoded concurrency=5 with a formula that reserves 100GB overhead and estimates 6GB per partition worker per proof.

Two instances had been deployed with this hardened image: a Belgium instance (2x A40, 2TB RAM) and a Czechia instance (2x RTX 3090, 251GB RAM). The Belgium instance had the old image (without the post-restart warmup fix), but with 2TB of RAM and an increased 45-minute benchmark timeout, it was expected to survive. The Czechia instance had the fully updated image including the post-restart warmup proof.

When the assistant wrote message 1203, it had been monitoring these instances for hours. The Czechia instance was still downloading parameters (43 out of 56GB, with 4 minutes ETA). The Belgium instance had progressed to params_done state and was actively benchmarking. The assistant decided to wait 15 minutes (sleep 900) to let both benchmarks progress, then query the manager for results.

What the Results Revealed

The dashboard response contained two critical data points:

  1. Czechia (C.32715618): State params_done, BenchRate ? — it had finished parameter downloads and was about to begin (or had just begun) its benchmark. No results yet.
  2. Belgium (C.32715193): State killed, BenchRate 35.91, kill reason: "bench_rate 35.9 below min_rate 50.0" — the instance had been destroyed by the vast-manager's lifecycle management because its proving throughput of 35.9 proofs/hour fell below the 50 proofs/hour minimum threshold. This was devastating. The Belgium instance had 2x A40 GPUs (48GB VRAM each) and 2TB of system RAM—arguably the most powerful hardware in the deployment. Yet it achieved only 35.9 proofs/hour, below even a single RTX 4090 which had previously managed 41.32 proofs/hour in the Norway instance.

The Assumptions That Were Challenged

This result shattered several implicit assumptions that had guided the session's work:

Assumption 1: More RAM and better GPUs guarantee higher throughput. The A40 is a professional-grade GPU with 48GB VRAM, comparable to the A100. The RTX 4090 has 24GB VRAM. Yet the 2x A40 system with 2TB RAM underperformed a single RTX 4090 with 500GB RAM. This revealed that proving throughput is not simply a function of GPU compute power or memory capacity—other factors like memory bandwidth, PCIe topology, CPU-to-GPU transfer speeds, and kernel compilation efficiency play decisive roles.

Assumption 2: The 50 proofs/hour minimum was achievable. The team had set this threshold based on economic viability calculations. The Belgium failure proved that even high-end hardware might not meet this bar, forcing a reconsideration of whether the threshold was realistic or whether multi-GPU configurations were absolutely necessary.

Assumption 3: Hardware specs could predict real-world performance. The entire deployment strategy had been built on the premise that selecting instances by GPU model and RAM size would yield predictable results. Belgium's failure demonstrated that two instances with identical specs could perform radically differently, likely due to the underlying host machine's CPU, memory bandwidth, and interconnects—variables that Vast.ai does not expose in its offering API.

The Thinking Process Visible in the Message

The assistant's reasoning is revealed in the structure of the message itself. The opening line—"Czechia: 43/56GB, ETA 4 minutes. Almost done with params!"—shows that the assistant was actively monitoring progress and had a real-time mental model of where each instance stood. The decision to "do a longer wait this time" (sleep 900, or 15 minutes) reflects a calibration based on previous experience: shorter waits had yielded incomplete results (the Belgium instance was still benchmarking, the Czechia instance was still downloading).

The bash command is carefully constructed. It queries the dashboard, filters for non-killed instances to show current state, but also includes a special case to show killed instances whose labels contain "32715" (matching both Belgium and Czechia). This reveals that the assistant anticipated the possibility of failure and wanted to capture kill reasons even for destroyed instances. The inclusion of kill_reason[:80] (truncated to 80 characters) shows a practical concern for log readability.

The empty "Manager Logs (recent)" section is itself informative. The journalctl command used --since "5 minutes ago" but produced no output, indicating that the vast-manager had not logged any significant events in the last 5 minutes—the kill decision had been made earlier, and the system was in a steady state.

Input Knowledge Required

To fully understand this message, one needs:

Output Knowledge Created

This message produced several critical pieces of knowledge:

  1. Empirical validation of the lifecycle management system: The vast-manager correctly detected an underperforming instance and destroyed it, proving that the automated lifecycle pipeline worked end-to-end.
  2. The first real-world performance data point for the A40 GPU: 35.9 proofs/hour on 2x A40 with 2TB RAM, establishing a baseline that challenged expectations.
  3. Evidence that hardware specs alone cannot predict proving performance: This would trigger a fundamental strategic shift toward data-driven hardware discovery.
  4. Confirmation that the OOM fixes were working: The Belgium instance completed its benchmark (albeit slowly) without crashing, meaning the dynamic concurrency and partition worker adjustments were preventing memory exhaustion.
  5. A new question: Would Czechia (2x RTX 3090, 251GB RAM) perform better or worse than Belgium? The answer was pending.

The Strategic Shift This Triggered

Although the immediate response in this message is simply to report the results, the implications would drive the remainder of the session. The Belgium failure demonstrated that hardcoded hardware thresholds were unreliable. The assistant would pivot to building a data-driven experimental system: a 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 foundational UI code. The default minimum proofs/hour rate would be made configurable.

This message, then, is the fulcrum on which the session turns. It is the moment when theory meets reality, when assumptions crumble, and when the path forward becomes clear—not through better guesses, but through data.