The Reliable Reference: Deploying a Baseline Instance in Belgium
In the middle of a high-stakes debugging session spanning multiple days, a single message from the assistant stands out for its quiet confidence. After wrestling with Out-of-Memory (OOM) crashes, tuning partition workers, and rebuilding Docker images, the assistant pauses to create not one but two new GPU instances on Vast.ai. The second of these — a 2x A40 machine in Belgium with a terabyte of RAM — is described in a way that reveals the assistant's underlying experimental mindset: it is to serve as a "reliable reference."
The message is short and procedural:
Instance 32713896 created. Let me also create a second one — the 2x A40 in Belgium with 1TB RAM at $0.574/hr as a reliable reference:
>
[bash] ssh 10.1.2.104 "vastai create instance 30818826 --image theuser/curio-cuzk:latest --disk 250 --env '-e PAVAIL=portavail1:[REDACTED] -e PAVAIL_SERVER=45.33.141.226:22222' --ssh --direct --onstart-cmd 'nohup /usr/local/bin/entrypoint.sh > /var/log/entrypoint.log 2>&1 &'" 2>&1 Started. {'success': True, 'new_contract': 32713902, 'instance_api_key': '[REDACTED]'}
Beneath this straightforward deployment lies a rich tapestry of reasoning, assumptions, and strategic decision-making that illuminates the assistant's approach to building a distributed proving system for Filecoin's Curio/CuZK stack.
The OOM Crisis That Preceded It
To understand why this message was written, one must first understand the crisis that preceded it. The assistant had been working on deploying GPU instances across Vast.ai to run PoRep (Proof-of-Replication) benchmarks for the CuZK proving engine. The goal was straightforward: find cost-effective GPU instances that could sustain at least 50 proofs per hour.
The reality was brutal. A BC Canada instance with 2x RTX 3090 and what was listed as 125GB RAM was killed during warmup by the host's OOM killer. A US instance with 376GB RAM survived warmup but was killed during the actual benchmark when 5 concurrent proofs with 10 partition workers each consumed more memory than the system had available. Even a Norway instance with a single RTX 4090 and 500GB RAM completed its benchmark successfully but achieved only 41.32 proofs per hour — below the 50-proof minimum.
Each failure taught the assistant something. The warmup OOM was fixed by detecting the absence of a PCE cache and starting the daemon with partition_workers=2 for the first proof, then restarting with full workers after the cache was generated. The steady-state OOM was addressed by rewriting the entrypoint to dynamically scale benchmark concurrency based on available RAM and GPU count, replacing a hardcoded concurrency=5 with a formula that reserved 100GB overhead and estimated memory usage per partition worker. A lifecycle bug was fixed so that underperforming instances were automatically destroyed.
With these fixes deployed in a new Docker image (theuser/curio-cuzk:latest), the assistant was ready to try again. The first instance — a 2x RTX 3090 in Czechia at $0.282/hr — was created in the message immediately preceding the subject message (see [msg 1132]). The assistant then turned to creating a second instance, and this is where the subject message begins.## The Strategic Rationale: Two Instances, Two Purposes
The assistant's decision to create two instances in rapid succession is not accidental. The Czechia instance (2x RTX 3090, $0.282/hr) represents the economic frontier — the cheapest multi-GPU option that might deliver acceptable performance. The Belgium instance (2x A40, $0.574/hr) serves a different purpose entirely. By labeling it a "reliable reference," the assistant reveals a core assumption: that a machine with 1TB RAM and professional-grade A40 GPUs would be stable enough to complete its benchmark without OOM crashes, providing a performance baseline against which cheaper options could be compared.
This is a classic experimental design pattern. In any optimization problem, you need a ground truth — a configuration that is known to work, even if it is not the most cost-effective. The Belgium instance, with its 2TB of system RAM (as listed in the Vast.ai offer), was expected to be immune to the memory pressure that had killed previous instances. The A40 GPUs, with 48GB of VRAM each, were also significantly more capable than the RTX 3090's 24GB. The assistant was essentially buying insurance: if the Czechia instance failed, the Belgium result would still provide useful data about what throughput was achievable on capable hardware.
Assumptions Embedded in the Deployment
The message carries several assumptions, some explicit and some implicit. The most obvious is that the newly built Docker image with the dynamic concurrency logic would work correctly on both machines. The assistant had just pushed the image after testing its syntax, but had not yet run it on any live instance. The entrypoint's RAM detection formula — which calculated available memory, subtracted 100GB overhead, and divided by an estimated per-proof cost — had been tuned for the machines the assistant had seen, but the Belgium machine's 2TB RAM was an order of magnitude larger than anything tested.
There was also an assumption about the Vast.ai platform's reliability. The vastai create instance command includes flags like --ssh --direct --onstart-cmd that configure the instance to run the entrypoint script automatically on boot. The assistant assumed that the instance would boot, the Docker image would start, and the entrypoint would execute without issues — an assumption that had been violated multiple times in earlier sessions when environment variables like VAST_CONTAINERLABEL were missing or when SSH connectivity was unreliable.
The choice of --disk 250 (250GB of additional disk space) was another assumption: that the PCE cache (26GB), SRS parameters (44GB), and C1 data would fit comfortably within that allocation. For the Belgium machine with 2TB RAM, this was almost certainly sufficient, but the assistant was implicitly trusting the Vast.ai offer listing.
The Knowledge Required to Understand This Message
To fully grasp what is happening in this message, a reader needs substantial context about the broader system. They need to understand that the CuZK proving engine requires a Pre-Compiled Constraint Evaluator (PCE) cache that is generated on first use and consumes ~26GB of disk space. They need to know that the SRS (Structured Reference String) file is ~44GB and must be downloaded before any proof can be generated. They need to understand that partition workers are parallel threads within the daemon that synthesize proof partitions, and that each worker consumes significant memory during synthesis.
The reader also needs familiarity with the Vast.ai platform: that vastai create instance rents a GPU machine by the hour, that --onstart-cmd specifies a command to run on boot, that the -e flags pass environment variables for the PAVAIL tunnel service, and that the returned new_contract ID is used for subsequent management. The [REDACTED] values in the quoted message hide authentication tokens for the PAVAIL service and the instance API key, both of which are sensitive credentials.
The Thinking Process Revealed
The assistant's reasoning is visible not just in the subject message itself, but in the messages that bracket it. In [msg 1132], the assistant explicitly evaluates multiple Vast.ai offers, comparing GPU type, RAM, price, and geolocation. The choice of the Czechia instance is justified with "the best value," while the Belgium instance is described as having "tons of RAM." The assistant even acknowledges uncertainty about the Czechia instance's RAM: "83GB might be the actual container limit on this one."
This uncertainty is crucial. The assistant had learned from the US instance that Vast.ai's reported cpu_ram could be misleading — the API showed 75GB per fraction, but the actual container saw 376GB. The assistant was therefore operating with incomplete information about the Czechia machine's true memory capacity, making the Belgium reference instance even more important as a fallback.
The phrase "as a reliable reference" also hints at a broader experimental methodology. The assistant was not just trying to get one instance working; it was building a knowledge base. If the Belgium instance completed its benchmark successfully, its throughput would become the benchmark against which cheaper instances could be evaluated. If it failed, that would indicate a systemic problem beyond memory pressure — perhaps a bug in the new entrypoint logic or an issue with the Docker image itself.
The Output Knowledge Created
This message produced two concrete outputs: two running GPU instances on Vast.ai, each configured with the latest Docker image and entrypoint logic. The Czechia instance (contract 32713896) and the Belgium instance (contract 32713902) were now live, each autonomously running through the benchmark lifecycle: download parameters, generate PCE cache, run warmup proof with reduced partition workers, restart the daemon with full workers, and execute the timed benchmark.
More importantly, the message created the conditions for knowledge generation. The assistant was about to learn whether the OOM fixes worked on real hardware, whether the dynamic concurrency formula was correct, and whether the 50-proof/hour threshold was achievable on multi-GPU setups. The Belgium instance, with its generous memory and professional GPUs, would provide the cleanest signal — if it failed, the problem was architectural, not resource-related.
Conclusion
This brief deployment message is a microcosm of the assistant's entire approach to building the CuZK proving infrastructure. It is methodical, experimental, and layered with strategic thinking. The assistant does not just throw instances at the wall; it selects them deliberately, assigns them roles, and uses the more expensive one as a control variable in an ongoing experiment. The message captures a moment of transition — from debugging and fixing to deploying and measuring — and sets the stage for the next round of learning about what hardware configurations can sustainably prove Filecoin PoReps at scale.