The Pivot from Hardcoded to Hardware-Aware: How One Edit Fixed OOM in GPU Proving

The Message

In a coding session spanning dozens of rounds, one brief message stands out as a critical turning point. At message index 1124, the assistant wrote:

I need to: 1. Detect GPU count 2. Scale benchmark concurrency to GPUs 3. Also consider partition workers vs available RAM more carefully [edit] /tmp/czk/docker/cuzk/entrypoint.sh Edit applied successfully.

This message is deceptively short. It contains no code dump, no multi-paragraph analysis, no debugging output. Yet it represents the moment when the assistant synthesized observations from a cascade of failures into a fundamental architectural insight: the system's configuration was not merely suboptimal—it was structurally incapable of adapting to the hardware it ran on. The edit that followed would transform a brittle, hardcoded pipeline into a dynamically self-tuning one, and in doing so, resolve the persistent Out-of-Memory (OOM) crashes that had plagued every multi-GPU proving instance deployed so far.

The Context of Failure

To understand why this message matters, we must trace the chain of events that led to it. The session was building a distributed Filecoin proving system using CuZK, a GPU-accelerated zero-knowledge proving engine. The workflow was straightforward: deploy GPU instances on Vast.ai, run a benchmark to measure proofs-per-hour, and keep only those that met a 50 proofs/hour minimum threshold. But every instance deployed so far had failed.

The Norway instance (1× RTX 4090, 500GB RAM) completed its benchmark but scored only 41.32 proofs/hour—below the threshold. The BC Canada instance (125GB RAM) was OOM-killed during warmup. The US instance (2× RTX 3090, 376GB RAM) seemed promising: it survived the warmup phase after a fix reduced partition workers from 10 to 2 during PCE extraction. But when the actual benchmark began with partition_workers=10 and concurrency=5, the process was silently killed by the kernel's OOM killer.

The assistant discovered this in [msg 1115] when SSH connections to the instance started failing. By [msg 1121], the dashboard confirmed the worst: all three instances were in "killed" state with bench_rate: 0.0 or 41.3, automatically destroyed by the manager's new lifecycle code. The system was working correctly as a lifecycle manager—failing instances were being culled—but it was failing as a proving system because every instance was being killed.

The Root Cause Analysis

In [msg 1122], the assistant performed the critical analysis. The hardcoded BENCH_CONCURRENCY=5 in entrypoint.sh meant that regardless of whether a machine had 1 GPU or 2 GPUs, 125GB RAM or 2TB RAM, it would always launch 5 concurrent proofs. Each proof with 10 partition workers consumed an estimated 30–40GB of RAM during synthesis. Five concurrent proofs meant 150–200GB just for synthesis, plus the SRS file (44GB), the PCE cache (26GB), and OS overhead. On a 376GB machine, this was survivable in theory but not in practice—memory fragmentation, allocation spikes, and the kernel's overcommit handling conspired to kill the process.

The assistant's reasoning, visible in [msg 1123], shows a clear three-option analysis:

  1. Reduce concurrency for the benchmark (e.g., 2 instead of 5)
  2. Reduce partition workers further
  3. Accept that 2× RTX 3090 with 376GB needs lower settings But option 3 was unacceptable—it would mean giving up on multi-GPU machines entirely. Option 2 had already been tried (the warmup fix used partition_workers=2). Option 1 was the right answer, but it needed to be dynamic: different machines have different GPU counts and different RAM.

The Insight: Concurrency Must Scale with Hardware

The breakthrough in message 1124 is the recognition that three variables must be considered together: GPU count, available RAM, and partition workers. The assistant's plan—"Detect GPU count, Scale benchmark concurrency to GPUs, Also consider partition workers vs available RAM more carefully"—represents a shift from static configuration to hardware-aware auto-tuning.

This is not obvious in retrospect. The earlier fix (reducing partition workers during warmup) addressed the transient memory spike of PCE extraction. But the steady-state OOM was a different beast: it was caused by the multiplicative effect of concurrency × partition workers. With 5 concurrent proofs and 10 partition workers each, the system was trying to run 50 partition synthesis threads simultaneously. Even on a 376GB machine, that was too much.

The assistant's insight was that concurrency should be a function of GPU count (since each GPU can only process a limited number of proofs concurrently) and available RAM (since each proof consumes a predictable amount of memory). The formula that emerged in subsequent messages ([msg 1127]) was:

Assumptions and Their Validity

The assistant made several assumptions in this reasoning. First, that the per-worker memory consumption is roughly linear and predictable. In practice, memory usage during zk-proof synthesis can spike non-linearly due to temporary allocations, but the linear model proved good enough for safe configuration. Second, that /proc/meminfo reports the container's actual memory limit. On Vast.ai, this is generally true, but the assistant had earlier observed a discrepancy: the vast API reported cpu_ram: 76800 (75GB per GPU fraction) while /proc/meminfo showed 376GB total system RAM. The assistant correctly chose to trust /proc/meminfo since that's what the kernel enforces for OOM decisions.

Third, the assistant assumed that GPU count detection via nvidia-smi would work reliably in the container. This was a reasonable assumption—NVIDIA drivers are typically available inside GPU containers—but it introduced a dependency on the container runtime configuration.

What Was Missing

The message does not address the possibility that different proof types (PoRep vs WindowPoSt vs WinningPoSt) might have different memory footprints. The CuZK engine supports multiple proof types, and the benchmark was specifically for 32GiB PoRep proofs. A more general solution would parameterize per-proof memory by proof type. However, for the immediate problem—stopping OOM crashes during PoRep benchmarking—the fix was sufficient.

The message also does not discuss the failure mode of the edit itself. The assistant applied the edit and received "Edit applied successfully," but did not verify the changed lines or run a syntax check until the next message ([msg 1126]). This is a minor operational risk: an edit tool can report success even if the patch was applied incorrectly, especially if line numbers have shifted due to previous edits.

The Knowledge Created

This message produced both code and understanding. The code change to entrypoint.sh replaced the hardcoded BENCH_CONCURRENCY=5 with a dynamically computed value based on GPU count and available RAM. But more importantly, it created a mental model of the system's resource profile: the relationship between partition workers, concurrency, and memory consumption. This model would prove essential in subsequent rounds when the assistant needed to tune parameters for machines with 251GB RAM (Czechia) and 2TB RAM (Belgium).

The edit also created a precedent for hardware-aware configuration in the entrypoint. Previously, only partition workers were scaled by RAM (10 for <400GB, 16 for ≥400GB). Now, concurrency was also scaled, and the scaling considered both RAM and GPU count. This dual-axis scaling was the architectural insight that made the system robust across heterogeneous hardware.

The Thinking Process

What makes message 1124 fascinating is what it reveals about the assistant's reasoning process. The assistant had just witnessed a machine with 376GB RAM and 2 GPUs get OOM-killed. The natural response would be to tweak a single parameter—reduce concurrency to 3, or reduce partition workers to 6. But instead, the assistant stepped back and asked: what should the system have done automatically?

The three-point plan in the message is structured as a progression: first detect the hardware (GPU count), then use that information (scale concurrency to GPUs), then refine with additional data (RAM vs partition workers). This is not a random list—it's a dependency chain. You cannot scale concurrency to GPUs without detecting GPU count. You cannot refine the RAM formula without understanding partition worker memory usage. The assistant was building a decision tree, and the edit was its implementation.

This kind of reasoning—from symptom to systemic cause to generalizable fix—is the hallmark of effective debugging. The assistant did not just patch the immediate OOM; it restructured the configuration logic so that the system would never again use a hardcoded value that didn't fit the hardware. The edit was small, but the thinking behind it was large.

Conclusion

Message 1124 is a turning point because it represents the moment when the assistant stopped fighting individual fires and started building a fireproof house. The OOM crashes were not random failures—they were predictable consequences of a configuration system that assumed all machines were identical. By introducing hardware detection and dynamic scaling, the assistant transformed the entrypoint from a static script into an adaptive system. The edit itself was a single line change in spirit, but the reasoning that produced it—spanning multiple rounds of SSH diagnostics, memory calculations, and formula iteration—was the real work. This message captures the essence of that reasoning in its purest form: three bullet points and an edit command, carrying the weight of a dozen failed instances and the blueprint for their salvation.