The Moment of Failure: Diagnosing an OOM Kill Across Two Continents

In the sprawling, asynchronous world of distributed GPU computing, failure is rarely announced with a clear error message. More often, it arrives as a subtle anomaly—a missing heartbeat, a null benchmark rate, a process that simply vanishes. Message 1025 in this opencode session captures precisely such a moment: the instant when the assistant, having orchestrated the creation of two new GPU instances on the Vast.ai marketplace, discovers that one has silently died while the other thrives. This message is the pivot point where deployment logistics give way to deep resource-tuning diagnostics, and where an incorrect assumption about the failure mode sets the stage for a more profound understanding of the system's memory constraints.

The Context: Two Instances, Two Fates

To understand message 1025, one must first appreciate the architecture being deployed. The assistant is managing a distributed Filecoin proof benchmarking system called "vast-manager," which provisions GPU instances on Vast.ai (a decentralized cloud marketplace), runs them through a lifecycle of parameter fetching and benchmark execution, and collects performance metrics. The system uses a custom Docker image (theuser/curio-cuzk:latest) that runs an entrypoint script responsible for registering with the management service, downloading Filecoin proof parameters (44–56 GB files), and executing a CUZK-based proof benchmark.

In the messages leading up to 1025 ([msg 1018]), the assistant created two fresh instances after destroying old ones:

Instance 32711932 on host 93197 (British Columbia, Canada): 2× RTX 3090 GPUs, 125 GB RAM, $0.28/hr
Instance 32711934 on host 88910 (Norway): 1× RTX 4090 GPU, 500 GB RAM, $0.47/hr Both instances were created with the --onstart-cmd workaround to run the entrypoint in the background while preserving SSH access—a critical adaptation to Vast.ai's quirk of replacing the Docker ENTRYPOINT with its own init script when using --ssh mode. Both instances started successfully, registered with the management service, and began the slow process of downloading Filecoin proof parameters. The assistant then entered a monitoring loop, checking every 5–10 minutes. In [msg 1024], the previous message, the assistant observed:
Norway (32711934): state: "params_done" — parameters downloaded, benchmark about to start
BC Canada (32711932): state: "killed", bench_rate: 0 — something went catastrophically wrong The BC instance's log tail showed only the paramfetch completion message, truncated mid-line. The daemon had been killed.

The Message: A Hypothesis Under Test

Message 1025 opens with the assistant's assessment:

BC instance (32711932) failed benchmark again — same transport error issue. Norway instance (32711934) params done, benchmark starting with partition-workers=16 (500GB RAM). Let me investigate the BC failure more closely and check the Norway benchmark progress.

This statement reveals two simultaneous threads of reasoning. First, the assistant makes an assumption about the BC failure: it labels it "same transport error issue." This refers to a gRPC transport error encountered in earlier chunks ([chunk 7.0]) during PCE (Pre-Compiled Constraint Evaluator) extraction, which had caused the benchmark script's set -e to abort the pipeline. The assistant had already hardened both benchmark.sh and entrypoint.sh to handle that failure gracefully. So seeing the BC instance in a "killed" state, the assistant's first instinct is to attribute it to this known failure mode.

Second, the assistant notes the Norway instance's partition-workers setting: 16. This is significant because the entrypoint script selects the number of partition workers based on available RAM—the more RAM, the more parallel synthesis workers can be spawned. With 500 GB RAM, the Norway instance gets 16 workers, while the BC instance with only 125 GB RAM would have gotten 10 (as seen in earlier runs at [msg 1007]). This detail foreshadows the actual root cause.

The assistant then executes a command to read the BC instance's full benchmark log:

ssh -p 48237 root@70.69.192.6 "cat /tmp/benchmark-full.log" 2>&1

The output shows:

C1 output not found at /data/32gbench/c1.json
Downloading from https://pub-08ae819c828244bdbe5f615fd8c5e144.r2.dev/c1.json ...
#=#=#                                                                          
##O#-#                                                                         
                                                                            
0.0%                                                                            
0.1%                                                    
...

This is a fresh log—the benchmark script restarted from scratch, downloading the C1 data file. The log doesn't show the crash itself because the benchmark script was re-invoked after the daemon was killed, overwriting the previous log. The download progress bars indicate the benchmark is retrying, but the daemon it's trying to connect to is dead.

The Incorrect Assumption and Its Correction

The assistant's characterization of "same transport error issue" is a mistake—or more precisely, a premature hypothesis. The transport error from earlier runs was a gRPC communication failure between the benchmark client and the CUZK daemon during PCE extraction. But in this case, the instance was marked "killed" by the vast-manager, which monitors the instance's SSH connectivity and process health. A transport error would leave the daemon running but unresponsive; a "killed" status means the entire instance or its critical processes were terminated.

The actual root cause is revealed in the very next message ([msg 1026]), where the assistant investigates further and discovers:

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.

This is a classic out-of-memory (OOM) scenario. The CUZK proof synthesis allocates memory organically during its first run because the PCE (Pre-Compiled Constraint Evaluator) cache hasn't been built yet. Without the cache, each of the 10 partition workers grows its memory footprint independently, and the combined demand exceeds the 125 GB available. The Linux kernel's OOM killer steps in, terminates the CUZK daemon, and the benchmark fails.

The transport error assumption was reasonable given the assistant's recent history—it had just spent significant effort fixing that exact issue. But the symptom pattern was different this time: a "killed" status rather than a gRPC timeout. The assistant's investigative command in message 1025 was the correct next step; it just needed a different log source to see the full picture.

Input Knowledge Required

To fully understand message 1025, one needs several pieces of context:

The architecture of the vast-manager system: A management service running on a controller host (10.1.2.104) that tracks instances through a SQLite database, monitors their state transitions (registered → params_done → benchmarking → completed), and communicates with Vast.ai's API.
The CUZK benchmark pipeline: A CUZK daemon that performs proof synthesis and GPU proving, with configurable partition workers (parallel synthesis threads), GPU workers, and pipeline mode. The first run performs PCE extraction which builds a cache for subsequent runs.
The Vast.ai platform quirks: The --ssh mode replaces the Docker ENTRYPOINT, requiring the --onstart-cmd workaround. The VAST_CONTAINERLABEL environment variable is a non-exported shell variable, invisible to SSH sessions but available to the entrypoint.
The earlier transport error bug: A gRPC transport error during PCE extraction that caused the benchmark to abort, which the assistant had already fixed by hardening the scripts.
The memory-dependent worker count: The entrypoint script calculates partition-workers based on available RAM: max(2, min(16, RAM_GB / 25)), so 125 GB → 10 workers, 500 GB → 16 workers.

Output Knowledge Created

Message 1025 produces several important pieces of knowledge:

Confirmation that the Norway instance is healthy: With parameters downloaded and benchmark starting with 16 workers, the high-memory instance validates that the core software stack works correctly.
Evidence that the BC instance's failure is not a simple transport error: The fresh benchmark log shows the script restarting, which is inconsistent with a gRPC error (which would leave the daemon running and the log intact).
The C1 data download URL: The log reveals the public URL for the benchmark data file (https://pub-08ae819c828244bdbe5f615fd8c5e144.r2.dev/c1.json), which is infrastructure knowledge.
A trigger for deeper investigation: The inconclusive log output prompts the assistant to check additional sources (daemon logs, NVIDIA SMI, process lists) in the next message, leading to the OOM discovery.

The Thinking Process

The assistant's reasoning in message 1025 is a textbook example of diagnostic triage under uncertainty. The assistant has two instances to monitor, limited attention, and must decide where to focus. The Norway instance is progressing normally, so the assistant notes its status briefly. The BC instance has failed, and the assistant must determine whether this is a recurrence of the known transport error (which would require a different fix) or something new.

The phrase "same transport error issue" reveals the assistant's mental model: it's pattern-matching the failure against recent experience. This is efficient but risky—it can lead to premature closure. However, the assistant immediately follows up with "Let me investigate the BC failure more closely," showing awareness that the hypothesis needs verification. The command choice (cat /tmp/benchmark-full.log) is logical: the benchmark log is the primary source of truth for what happened during proof execution.

The assistant also implicitly makes a design decision in this message: it does not intervene to restart or repair the BC instance. Instead, it investigates. This restraint is important—if the assistant had immediately recreated the instance again, it would have repeated the same failure without understanding why. The decision to investigate first, rather than act, demonstrates成熟 diagnostic discipline.

The Broader Significance

Message 1025 sits at the boundary between two phases of the session. The preceding messages were about platform-specific debugging—adapting to Vast.ai's SSH quirks, fixing the monitor's matching logic, hardening scripts against gRPC errors. Message 1025 initiates the transition to workload-specific resource tuning—understanding that different hardware configurations require different pipeline parameters.

The contrast between the two instances is instructive. The Norway instance with 500 GB RAM and a single RTX 4090 completes its warmup proof successfully (as confirmed in [msg 1026]), validating the entire software stack. The BC instance with 125 GB RAM and dual RTX 3090s fails because the memory is insufficient for the uncached first-run synthesis. This is not a bug in the software but a constraint of the hardware—and one that can be addressed by reducing partition workers during the warmup phase.

The assistant's eventual fix ([msg 1026]) is elegant: use fewer partition workers for the warmup proof (so the organic memory allocation doesn't exceed available RAM), then scale up for subsequent proofs once the PCE cache is built and memory usage becomes predictable. This adapts the pipeline to the hardware rather than requiring all hardware to meet the pipeline's peak demands.

Conclusion

Message 1025 is a diagnostic pivot point in a complex distributed systems deployment. It captures the moment when an incorrect assumption meets new evidence, when the assistant's pattern-matching instincts are checked by investigative discipline. The message's true value lies not in its explicit content—a brief status update and a single SSH command—but in the reasoning it represents: the triage of two parallel workflows, the formulation of a testable hypothesis, and the decision to investigate before acting. In the broader narrative of the session, it marks the transition from fighting platform-specific quirks to understanding workload-specific resource constraints, ultimately leading to a more robust and hardware-aware system design.