The Great Divide: When 125GB of RAM Separates Success from Failure in Distributed GPU Deployment

Introduction

In the lifecycle of any complex distributed system, there comes a moment when the scaffolding falls away and the system must stand on its own. For the Filecoin proof-generation pipeline being deployed across Vast.ai's GPU marketplace in this opencode session, that moment arrived in a single monitoring check that revealed two instances—created from the same Docker image, launched with the same configuration, managed by the same service—on radically different trajectories. One had completed its warmup proof and validated the entire software stack. The other had been silently killed by the operating system's out-of-memory (OOM) killer, its daemon process terminated mid-synthesis.

This chunk of the session captures the arc from cleanup to crisis to diagnosis: the destruction of old infrastructure, the creation of two fresh instances, the patient waiting as multi-gigabyte proof parameters downloaded across continents, and the sudden discovery that one instance had failed while the other thrived. The contrast between the two—a 2× RTX 3090 machine with 125GB RAM in British Columbia and a 1× RTX 4090 machine with 500GB RAM in Norway—revealed a fundamental resource constraint that would reshape the entire deployment strategy. This article traces that arc, examining the decisions, assumptions, and discoveries that defined this pivotal chunk of work.

Clearing the Decks: Infrastructure Cleanup

The chunk opens with the assistant in cleanup mode, operating under a non-interactive mandate from the user ([1], [2]). The user had directed the assistant to handle two impacted instances on hosts 93197 and 88910, working autonomously until they ran correctly. The assistant's first task was to reconcile the user's mental model with ground truth.

In message 1015, the assistant discovered a critical discrepancy: the user had referenced an instance 32710979 on host 88910, but the Vast API returned a 404 error when the assistant attempted to destroy it. This instance simply did not exist in the assistant's account. The assistant correctly inferred that it must belong to a different account or had already been terminated, and pivoted to destroy the actual remaining old instance—32705217, the manually-managed RTX 4090 on host 88910 ([1]).

This moment of reconciliation is emblematic of the entire chunk. The assistant repeatedly had to bridge the gap between expectations and reality, whether those expectations came from the user, from the assistant's own mental model, or from the configuration defaults baked into the benchmark scripts.

Message 1016 continued the cleanup with a nuclear option: a full DELETE FROM instances on the vast-manager's SQLite database ([2]). Rather than surgically removing stale entries for the destroyed instances, the assistant chose to wipe the entire table and start from a clean slate. This decision reflected a preference for absolute certainty over surgical precision—a reasonable trade-off when the alternative was the risk of ghost entries confusing the management service's monitoring logic.

With the database clean, the assistant surveyed available GPU offers on both target hosts. The market snapshot revealed the hardware landscape: host 93197 in BC Canada offered a 2× RTX 3090 with 125GB RAM at $0.28/hr, a 1× RTX 3090 with 62GB RAM at $0.14/hr, and a 2× RTX 3070 with 167GB RAM at $0.17/hr. Host 88910 in Norway offered a 2× RTX 4090 with 1001GB RAM at $0.93/hr, a 1× RTX 4090 with 500GB RAM at $0.47/hr, and a 1× RTX 4060 Ti with 125GB RAM at $0.12/hr ([2]).

The assistant's selections—the 2× RTX 3090 on BC and the 1× RTX 4090 on Norway—represented a cost-performance trade-off. On BC, the dual-GPU option maximized compute power within a budget-friendly price. On Norway, the single 4090 with 500GB RAM provided ample memory for the memory-intensive proof synthesis. These choices would prove fateful.

Message 1017 served as a checkpoint, marking the transition from destruction to creation ([3]). The assistant updated its todo list, marking the Docker image push, instance destruction, and database cleanup as completed. This deliberate acknowledgment of phase boundaries is a hallmark of autonomous agent coordination—the assistant was building an explicit record of progress to maintain situational awareness across the long, non-interactive session.

The Redeployment: Creating Fresh Instances

With the old infrastructure cleared, the assistant executed the creation of two new instances in message 1018 ([4]). Each vastai create instance command was a dense packet of configuration decisions:

vastai create instance 29854362 \
  --image theuser/curio-cuzk:latest \
  --disk 250 \
  --env '-e PAVAIL=[REDACTED] -e PAVAIL_SERVER=[REDACTED]' \
  --ssh --direct \
  --onstart-cmd 'nohup /usr/local/bin/entrypoint.sh > /var/log/entrypoint.log 2>&1 &'

The --onstart-cmd workaround was the critical piece. Earlier in the session, the assistant had discovered that Vast.ai's --ssh mode replaces the Docker ENTRYPOINT with its own init script. The VAST_CONTAINERLABEL environment variable—essential for the instance to identify itself to the management service—was injected as a non-exported shell variable, invisible to SSH sessions but available to the Docker entrypoint. The workaround was to launch the entrypoint in the background via nohup through --onstart-cmd, preserving SSH access while still executing the automated lifecycle ([4]).

Instance 32711932 (BC Canada, 2× RTX 3090, 125GB RAM) and instance 32711934 (Norway, 1× RTX 4090, 500GB RAM) were created successfully. Both returned success: True from the Vast API, and both received instance API keys for authentication with the management service.

The Verification: Both Running, Both Registered

The next several messages formed a verification chain, each step confirming a deeper layer of functionality. In message 1019, the assistant waited 60 seconds and queried the Vast API to confirm both instances were in running status with SSH ports and public IP addresses available ([5]). The BC instance was reachable at 70.69.192.6:48237; the Norway instance at 141.195.21.87:41122.

Message 1020 drilled deeper, querying the vast-manager dashboard API to confirm that both instances had successfully registered with the management service ([6]). The response showed both in state: "registered" with runner IDs 7 and 8, each having shipped over 200 log lines. This was a critical validation: the entire chain from Docker image to instance creation to entrypoint execution to service registration was functioning correctly.

Messages 1021 through 1023 formed a monitoring loop, with the assistant checking progress at increasing intervals ([7], [8], [9]). Both instances were downloading Filecoin proof parameters—multi-gigabyte .params and .vk files required for proof generation. The BC instance showed download speeds of 19–35 MiB/s. The Norway instance had a slow download warning but aria2 was auto-retrying. The assistant's commentary revealed its mental model: "Both fetching params. The small .vk files are downloading. The big .params files will take ~10-20 minutes."

This waiting period was the calm before the storm. The assistant was operating under the assumption that both instances, having been created with the same image and configuration, would behave symmetrically. But the hardware beneath them was anything but symmetric.

The Moment the OOM Killer Struck

Message 1024 shattered the calm ([10]). After a ten-minute wait, the assistant queried the manager dashboard and saw:

=== Manager State ===
{
  "label": "C.32711934",
  "state": "params_done",
  "bench_rate": null,
  "log_lines": 3987
}
{
  "label": "C.32711932",
  "state": "killed",
  "bench_rate": 0,
  "log_lines": 4013
}

The Norway instance had progressed to params_done—parameter download complete, benchmark about to begin. The BC Canada instance was in state killed with a bench_rate of 0. Something had gone catastrophically wrong.

The assistant's initial hypothesis, expressed in message 1025, was that the BC instance had suffered the same gRPC transport error that had plagued earlier runs ([11]). This was a reasonable assumption—the assistant had just spent significant effort hardening the benchmark scripts against that exact failure mode. But the symptom pattern was different this time. A transport error would leave the daemon running but unresponsive; a "killed" status meant the entire instance or its critical processes had been terminated.

The assistant SSH'd into the BC instance and read the benchmark log, but found only a fresh log showing the benchmark restarting from scratch—downloading the C1 data file again. The daemon was dead, and subsequent retry attempts naturally failed because there was no daemon to connect to.

The Diagnosis: When Memory Runs Out

Message 1026 was the diagnostic breakthrough ([12]). The assistant connected the dots:

"The BC instance daemon was Killed (OOM killed most likely — 125GB RAM with 10 partition synthesis all growing organically). The daemon received the proof, started 10 partition syntheses, and the OS killed it."

The root cause was elegant and brutal. The CuZK proving engine's first proof run performs Pre-Compiled Constraint Evaluator (PCE) extraction, which allocates memory organically as each partition worker synthesizes its constraint system. With 10 partition workers running simultaneously on a machine with only 125GB of RAM, the combined memory footprint exceeded available resources. The Linux kernel's OOM killer stepped in, terminated the CUZK daemon, and the benchmark failed.

The Norway instance's success provided the perfect counterpoint. With 500GB of RAM and 16 partition workers (the entrypoint script scaled workers based on available memory), the warmup proof completed successfully in approximately 128 seconds:

=== Proof Result ===
status:    COMPLETED
job_id:    d97b2e99-1ef4-4530-ba20-bb1ba188e91f
timings:   total=127497 ms (queue=407 ms, srs=0 ms, synth=715977 ms, gpu=102565 ms)
wall time: 128108 ms
proof:     1920 bytes

This contrast sharpened the diagnosis. The problem was not a software bug, not a configuration error, not a platform quirk—it was a pure resource constraint. The CuZK proving pipeline, when performing its first uncached synthesis, required more than 125GB of RAM when using 10 partition workers. The Norway instance's 500GB handled it without issue.

The assistant enumerated three possible solutions:

  1. Reduce partition-workers for the first warmup proof — Use only 1 or 2 workers during the initial uncached synthesis, then scale up once the PCE cache is built.
  2. Only use hosts with ≥ 256GB RAM — A constraint-based approach that limits the pool of available instances.
  3. Pre-extract PCE before running the full benchmark — A more architectural solution separating PCE extraction from proof generation. The assistant selected option 1 as "the best fix," and the reasoning was sound: it was the least disruptive change, it adapted to existing hardware rather than requiring new hardware, and it leveraged the known behavior that cached PCE uses pre-allocated buffers with lower memory requirements ([12]).

The Silence That Followed

Message 1027 was an empty message from the user ([13]). In the context of the non-interactive directive issued earlier, this silence functioned as tacit approval—the user was honoring their own instruction to stay out of the way while the assistant worked. The assistant responded with a comprehensive reorientation document, consolidating everything learned across the entire session into a single structured summary.

This empty message, despite containing no content, served a crucial coordination function. It marked the transition from crisis-response to strategic planning, allowing the assistant to step back from the immediate tactical problem and re-establish the broader context.

The Shift: From Platform Debugging to Resource Tuning

The OOM diagnosis marked a fundamental shift in the project's trajectory. The preceding messages had been dominated by platform-specific debugging—working around Vast.ai's SSH environment quirks, fixing the monitor's matching logic, hardening scripts against transient errors. These were all bugs in the deployment infrastructure, the scaffolding around the actual proving engine.

The BC Canada OOM was different. It was a fundamental resource constraint of the proving pipeline itself. No amount of script hardening or platform workaround could make 125GB of RAM hold 10 concurrent uncached syntheses. The fix required changing the proving pipeline's behavior—specifically, how the benchmark script configured parallelism for the warmup proof.

This shift from "fixing the deployment" to "tuning the workload" is a natural progression in any production deployment. First, you make the system deploy reliably. Then, you make it run efficiently on the available hardware. The assistant's proposed fix—reducing partition workers for the warmup proof—was elegant because it acknowledged the asymmetric memory requirements of the proving pipeline. The first proof must allocate memory organically as it synthesizes constraints; subsequent proofs benefit from cached PCE data that uses pre-allocated buffers. By using fewer workers for the memory-intensive first synthesis, the system could stay within the available RAM, then scale up to full parallelism once the cache was populated.

Themes and Lessons

Several themes emerge from this chunk that are broadly applicable to distributed systems deployment on heterogeneous infrastructure:

The assumption of symmetry is dangerous. The assistant treated both instances as fungible deployments, issuing identical monitoring commands and expecting similar progress. But the 125GB vs 500GB RAM difference proved catastrophic. In heterogeneous marketplaces like Vast.ai, every instance is a unique combination of hardware characteristics, and assumptions of symmetry must be validated empirically.

Surface-level health checks do not guarantee workload-level success. Both instances showed "running" status in the Vast API. Both registered with the management service. Both began downloading parameters. But these indicators masked the latent memory constraint that would kill the BC instance minutes later. The gap between "the container is running" and "the workload is succeeding" is where the most interesting failure modes live.

Diagnostic discipline pays off. The assistant's initial hypothesis—that the BC failure was a recurrence of the transport error—was incorrect. But the assistant did not act on this hypothesis; it investigated further, reading the benchmark log, checking the daemon status, and ultimately tracing the failure to the OOM killer. This discipline, rather than premature action, led to the correct diagnosis and an appropriate fix.

The most elegant fixes acknowledge system physics. Reducing partition workers for the warmup proof is not a hack—it is a design insight that respects the memory dynamics of the CuZK engine. The uncached synthesis phase is the memory bottleneck; once that phase is complete, the system can operate at full capacity. By adapting the pipeline to this reality, the assistant transformed a hard constraint (125GB RAM) into a manageable configuration parameter.

Conclusion

This chunk of the opencode session captures a pivotal transition in the deployment of a distributed GPU proving system. The arc from infrastructure cleanup to instance creation to monitoring to crisis to diagnosis reveals the true nature of deploying compute-intensive workloads on heterogeneous cloud infrastructure: it is not a linear process but a cycle of assumption, discovery, and adaptation.

The Norway instance's successful warmup proof validated the entire software stack—the Docker image, the entrypoint, the benchmark script, the CuZK engine, the management service. The BC Canada instance's OOM death revealed that validation was only half the battle. The other half was understanding the resource profile of the workload and adapting the configuration to match the available hardware.

In the end, the assistant emerged from this chunk with a deeper understanding of the system it was building. It knew not just that the pipeline worked, but under what conditions it worked, and why it failed when those conditions were not met. That knowledge—the product of careful diagnosis and systematic reasoning—is the true output of this chunk, more valuable than any single instance or configuration change.