The Same Machine, Two Different Results: Debugging GPU Benchmark Variance in a Distributed Proving Network

Introduction

In the middle of a sprawling development session spanning platform hardening, Docker builds, and deep protocol-level debugging, there is a quiet but revealing moment. Message 1523 captures a routine monitoring check on a distributed GPU benchmark system — but beneath its surface lies a subtle and important discovery. The assistant, having deployed multiple GPU instances across vast.ai to benchmark proof generation performance, observes that two different instances running on the same physical machine produce wildly different results: one passes its minimum rate threshold, the other fails. This single log line — machine=15136 gpu=RTX 4090×1 rate=46.2 — tells a story about the challenges of distributed benchmarking, hardware variability, and the assumptions we make about consistent performance.

The Message in Full

The message opens with a status summary:

2 running (RTX 3090 @ 35.6/hr, RTX 4090 @ 61.4/hr), 2 still benchmarking (RTX 4090 #2 and RTX 5000Ada). The RTX 5000Ada in Japan may be slow due to the params download being slower from Japan. Let me continue watching:

The assistant then executes a bash command that tails the vast-manager journal for 300 seconds (5 minutes). The output reveals a single benchmark completion event:

Mar 12 12:23:12 vast-arb-host vast-manager[120490]: 2026/03/12 12:23:12 [bench-done] uuid=d977dead-b3a7-4a08-b6df-9e126679078f rate=46.2 min_rate=59.0 passed=false Mar 12 12:23:12 vast-arb-host vast-manager[120490]: 2026/03/12 12:23:12 [host-perf] machine=15136 gpu=RTX 4090×1 rate=46.2

The second RTX 4090 instance (uuid starting with d977dead) completed its benchmark at 46.2 proofs per hour, falling short of the 59.0 minimum threshold. It was marked as passed=false, triggering the vast-manager's lifecycle logic to destroy the underperforming instance.

The Critical Discovery: Same Machine, Different Performance

The most significant detail in this message is the machine_id field. Machine 15136 is an RTX 4090 in Norway. But earlier in the session, at message 1520, another instance on the same machine had already completed its benchmark:

uuid=7bec818c-9e52-422b-bb31-52baefcaa6ef rate=61.4 min_rate=59.0 passed=true machine=15136 gpu=RTX 4090×1 rate=61.4

This is a striking discrepancy. Two separate vast.ai instances, both running on machine 15136 — the same physical hardware — achieved benchmark rates of 61.4 proofs/hour and 46.2 proofs/hour respectively. The first instance passed comfortably above the 59.0 threshold; the second failed by a wide margin.

This finding has profound implications for the reliability of the benchmark-driven deployment system. If the same machine can produce results that differ by 33% (from 61.4 down to 46.2), then a single benchmark measurement is not a reliable predictor of sustained performance. The system's entire lifecycle — deploy, benchmark, accept or reject — hinges on the assumption that a benchmark result is representative of future performance. This message provides direct evidence that this assumption may be flawed.

Possible Explanations for the Variance

Several factors could explain why the same RTX 4090 produced such different results across two instances:

Thermal throttling. The first benchmark ran earlier and completed around 12:18 UTC. The second completed around 12:23 UTC — only five minutes later. If the GPU was still hot from the first benchmark's sustained load, the second run may have experienced thermal throttling, reducing clock speeds and degrading performance.

Resource contention. Both instances were running simultaneously on the same machine. Vast.ai instances share the host's CPU, RAM, and PCIe bandwidth. If the first instance was still running (it passed and entered the running state) while the second was benchmarking, they would compete for memory bandwidth and CPU resources. The CuZK proving pipeline is known to be sensitive to memory bandwidth and CPU availability for tasks like Merkle tree construction.

Instance-level variability. Each vast.ai instance is a Docker container with its own filesystem, processes, and resource limits. Even on the same host, container-level cgroup limits or NUMA node assignments could differ between instances, affecting performance.

Benchmark workload differences. The benchmark script runs a sequence of proofs. If the second instance encountered a different mix of proof types (e.g., more WindowPoSt proofs which are heavier), or if the PCE cache warmup was incomplete, the measured rate could differ.

The Assistant's Assumptions

The message reveals several implicit assumptions made by the assistant. First, the assistant assumes that the RTX 5000Ada in Japan is slow due to params download latency: "The RTX 5000Ada in Japan may be slow due to the params download being slower from Japan." This is a reasonable hypothesis — the proving parameters are large files (hundreds of megabytes to gigabytes), and download speed depends on geographic proximity to the parameter server. However, the message does not confirm this; the RTX 5000Ada never reports a benchmark result within the 5-minute watch window.

Second, the assistant assumes that watching the journal for 5 minutes is sufficient to capture the remaining benchmark completions. In practice, only one of the two remaining instances completed during this window. The RTX 5000Ada in Japan remained in params_done state, suggesting its benchmark was still running or had stalled.

Third, there is an implicit assumption that the benchmark results are trustworthy for making accept/reject decisions. The discovery that machine 15136 produced both a passing and a failing result challenges this assumption at a foundational level.

Input Knowledge Required

To fully understand this message, the reader needs familiarity with several concepts:

Output Knowledge Created

This message produces several concrete pieces of knowledge:

  1. Machine 15136 (RTX 4090, Norway) has inconsistent performance. The two benchmark results (61.4 and 46.2) suggest that either the measurement methodology is flawed, or the machine's performance is genuinely variable. This is actionable information for the system design.
  2. The second RTX 4090 instance failed its minimum rate. The system correctly identified the underperformance and will destroy the instance, preventing money from being spent on a machine that cannot meet the economic threshold.
  3. The host_perf table now contains conflicting data for machine 15136. Earlier, the system recorded 61.4 proofs/hour for this machine. Now it has recorded 46.2. The backend's logic for keeping the "highest" score (as described in the chunk summary) will preserve the 61.4 value, but the discrepancy itself is a signal that something is wrong with the benchmarking methodology.
  4. The RTX 5000Ada in Japan has not yet reported. This instance remains in params_done state, suggesting either a slow benchmark, a stalled process, or a crash that the old Docker image cannot report.

The Thinking Process

The assistant's reasoning in this message is primarily observational and operational. There is no complex debugging or code change — instead, the assistant is in a monitoring loop, watching the system's behavior and interpreting results as they arrive.

The structure of the reasoning is:

  1. Summarize current state: Two running, two benchmarking.
  2. Formulate a hypothesis about the slow instance: Japan params download.
  3. Execute a monitoring command: Tail the journal for 5 minutes.
  4. Interpret the output: One benchmark completed, it failed.
  5. Implicitly note the anomaly: The machine_id matches a previously successful instance, but the rate is much lower. The assistant does not explicitly call out the machine_id discrepancy in this message. The discovery is left implicit — the reader (or the assistant in a later message) must connect the dots between the 61.4 result from message 1520 and the 46.2 result here. This is characteristic of real-time monitoring: the operator sees the data but may not immediately process its full significance, especially when focused on the operational task of watching for completions.

Broader Implications for the System

The variance on machine 15136 has design implications for the vast-manager system. If benchmark results are unreliable, then the entire accept/reject mechanism is on shaky ground. The system might:

Conclusion

Message 1523 appears, at first glance, to be a routine status update in a monitoring loop. But it contains a critical data point: two instances on the same physical machine produced benchmark results differing by 33%. This finding challenges the foundational assumption of the benchmark-driven deployment system — that a single measurement can reliably predict a machine's economic viability. The message captures the moment when the system's data begins to reveal its own limitations, setting the stage for deeper investigation into benchmark methodology, hardware variability, and the design of reliable distributed proving infrastructure.