The Empty Search: A Moment of Discovery in GPU Instance Selection

Message in Focus

The following message from the coding session (global index 1068) captures a pivotal moment of discovery and decision-making during the deployment of a cuzk proving system on cloud GPU infrastructure:

[assistant] Interesting — host 93197 now offers 3x RTX 3070 (not 2x RTX 3090 as before). The only RTX 3090 available is in Chile with 62GB RAM. Let me look for better options — we need enough VRAM per GPU. The RTX 3090 has 24GB which is enough, but RTX 3070 only has 8GB which might not work for cuzk.

>

Let me look at what GPUs have enough VRAM (at least ~16GB for cuzk PoRep C2): [bash] ssh 10.1.2.104 "vastai search offers 'num_gpus>=1 cuda_max_good>=13.0 disk_space>=200 gpu_ram>=16000' --raw 2>/dev/null" | python3 -c " import json, sys data = json.load(sys.stdin) print(f'Total offers: {len(data)}') for o in sorted(data, key=lambda x: x.get('dph_total', 999))[:20]: print(f'ID: {o[\"id\"]}, Host: {o[\"host_id\"]}, GPUs: {o[\"num_gpus\"]}x {o[\"gpu_name\"]} ({o.get(\"gpu_ram\",0)//1024}GB), RAM: {o.get(\"cpu_ram\",0)//1024}GB, \$/hr: {o.get(\"dph_total\",0):.3f}, Geo: {o.get(\"geolocation\",\"?\")}, DL: {o.get(\"inet_down\",0):.0f}Mbps, Rel: {o.get(\"reliability\",0):.2f}') " 2>/dev/null Total offers: 0

At first glance, this appears to be a routine infrastructure query: the assistant searches for available GPU instances with sufficient VRAM, finds none, and moves on. But beneath this surface lies a rich tapestry of reasoning, assumptions, and decision-making that illuminates the challenges of deploying computationally intensive workloads on ephemeral cloud GPU markets. This message is a microcosm of the entire session's struggle: the tension between hardware requirements, market availability, and the practical realities of distributed proving systems.

Why This Message Was Written: Context and Motivation

To understand why this message exists, one must trace the chain of events that led to it. The session had been wrestling with a persistent Out of Memory (OOM) problem that plagued low-RAM GPU instances during the cuzk PoRep proving benchmark. The assistant had just resolved that crisis by implementing a dynamic hardware-aware configuration system: benchmark.sh was refactored to detect missing PCE caches and start the daemon with reduced partition workers, and entrypoint.sh was rewritten to scale benchmark concurrency based on available RAM and GPU count. A new Docker image had been built and pushed to Docker Hub. The BC Canada instance (host 93197, 2x RTX 3090) had been destroyed after its OOM failure, and the assistant was now in the process of recreating it — or finding a suitable replacement.

The immediate trigger for this message was the discovery that host 93197 had changed its GPU configuration. When the assistant ran vastai search offers for RTX 3090 instances with sufficient RAM, it returned no results. A broader search without the RAM filter revealed that host 93197 now offered 3x RTX 3070 instead of the previous 2x RTX 3090. This was a critical finding: the assistant had been planning to deploy a new instance on the same host, but the hardware had changed, invalidating the previous deployment strategy.

The motivation, then, was to reassess the available options and find a viable path forward. The assistant needed to answer a fundamental question: given that the previously targeted host was no longer suitable, what GPU instances are available that meet the minimum VRAM requirement for cuzk PoRep C2 proving? This question drove the search that produced the empty result.

The Thinking Process: Reasoning and Decision-Making

The message reveals a clear chain of reasoning, structured in two phases. First, the assistant processes the new information about host 93197. The phrase "Interesting — host 93197 now offers 3x RTX 3070 (not 2x RTX 3090 as before)" indicates a moment of surprise and re-evaluation. The assistant had been operating under the assumption that this host was a known quantity with predictable hardware. The change forces a recalibration.

The assistant then performs a rapid feasibility assessment: "The RTX 3090 has 24GB which is enough, but RTX 3070 only has 8GB which might not work for cuzk." This is a critical decision point. The assistant implicitly rules out the RTX 3070 as unsuitable, based on domain knowledge about the memory requirements of cuzk PoRep C2 proving. The 8GB VRAM of the RTX 3070 is deemed insufficient, while the 24GB of the RTX 3090 is known to be adequate.

Having eliminated the only available multi-GPU option, the assistant pivots to a broader search strategy: "Let me look for better options — we need enough VRAM per GPU." The threshold is set at "at least ~16GB for cuzk PoRep C2." This is an educated estimate, not a hard specification from documentation. The assistant is applying practical engineering judgment: knowing that the RTX 3090 (24GB) works, and suspecting that 8GB (RTX 3070) does not, the assistant interpolates a minimum of 16GB as a reasonable cutoff.

The search command itself reveals additional decision-making. The filters include num_gpus>=1 (any number of GPUs, not just multi-GPU), cuda_max_good>=13.0 (CUDA compatibility for the proving software), disk_space>=200 (sufficient storage for parameters and data), and gpu_ram>=16000 (the 16GB VRAM threshold in MB). The assistant also sorts by price (dph_total) and limits to the top 20 results, indicating a cost-conscious approach — the cheapest suitable option is preferred.

The result — "Total offers: 0" — is a dead end. The assistant's search for GPUs with at least 16GB VRAM on the Vast.ai marketplace returns nothing. This is a significant finding that shapes the subsequent direction of the session.

Assumptions and Their Implications

This message rests on several assumptions, some explicit and some implicit, each carrying consequences for the correctness of the reasoning.

Assumption 1: RTX 3070 (8GB) is insufficient for cuzk PoRep C2. This is a reasonable engineering judgment, but it is not verified. The assistant does not test whether cuzk can run on 8GB VRAM. It assumes that because the RTX 3090 (24GB) works, and because the proving workload is memory-intensive, 8GB is too little. This assumption could be incorrect if cuzk is optimized for lower memory usage, or if the workload can be partitioned across multiple GPUs. However, given the OOM failures that plagued the session even on machines with 125GB of system RAM, the assumption is likely conservative rather than risky.

Assumption 2: 16GB VRAM is a sufficient minimum. The assistant sets the threshold at 16GB, but this is an interpolation, not a measured requirement. The actual minimum VRAM for cuzk PoRep C2 is unknown — the assistant only knows that 24GB works and 8GB probably doesn't. The 16GB threshold is a heuristic that could be wrong in either direction: too low (if cuzk actually needs more than 16GB) or too high (if it could run on 12GB or 10GB). This assumption directly causes the empty search result — if the threshold were set lower, offers might appear.

Assumption 3: Vast.ai's search API returns accurate, complete results. The assistant trusts that the search command correctly filters available offers. However, Vast.ai's marketplace is dynamic and region-dependent. The search might miss offers from hosts that are temporarily offline, or the filters might interact in unexpected ways. The assistant does not verify the result by running a broader search without the gpu_ram filter to confirm that offers exist at all.

Assumption 4: The host 93197 change is permanent. The assistant treats the new GPU configuration as the current state of the host, but Vast.ai hosts can change their offerings dynamically. The host might later offer RTX 3090s again, or the assistant's earlier search might have caught a transient state. The assistant does not re-check the host later.

Assumption 5: A single-GPU instance is acceptable. By dropping the num_gpus>=2 filter and using num_gpus>=1, the assistant implicitly accepts single-GPU instances as a fallback. This is a pragmatic concession, but it has implications for proving throughput — the earlier Norway instance (1x RTX 4090) achieved only 41.32 proofs/hour, below the 50 proofs/hour minimum. A single-GPU instance might not meet performance requirements even if it avoids OOM.

Input Knowledge Required

To fully understand this message, a reader needs:

  1. Knowledge of the cuzk proving system: Understanding that cuzk is a GPU-accelerated proving engine for Filecoin proofs (PoRep C2), and that it requires significant GPU memory for the proving computation. The term "PCE" (Pre-Compiled Constraint Evaluator) and the concept of partition workers are relevant context from earlier in the session.
  2. Knowledge of GPU VRAM capacities: Knowing that RTX 3090 has 24GB VRAM, RTX 3070 has 8GB, and understanding why this difference matters for compute workloads. The assistant's judgment that "8GB which might not work for cuzk" relies on this domain knowledge.
  3. Knowledge of Vast.ai marketplace: Understanding that Vast.ai is a peer-to-peer GPU rental marketplace where hosts offer instances dynamically, that offers can change at any time, and that the vastai search offers command queries available instances with various filters.
  4. Knowledge of the session's history: The OOM failures, the benchmark.sh fix, the Docker image rebuild, and the destruction of the BC Canada instance. The assistant's search is motivated by the need to find a replacement for the destroyed instance.
  5. Knowledge of CUDA compatibility: The filter cuda_max_good>=13.0 indicates that the proving software requires a minimum CUDA version, and the assistant knows which versions are compatible.

Output Knowledge Created

This message produces several important pieces of knowledge:

  1. Host 93197 is no longer viable: The previously targeted host now offers RTX 3070s instead of RTX 3090s, making it unsuitable for cuzk proving. This knowledge redirects the deployment strategy away from this host.
  2. No GPU instances with ≥16GB VRAM are available on Vast.ai: At the time of the search, the marketplace has zero offers meeting the VRAM threshold. This is a significant constraint that shapes all subsequent planning.
  3. The only RTX 3090 available is in Chile with 62GB RAM: This specific offer is identified but implicitly rejected (likely due to low system RAM, high price, or geographic latency). The assistant does not pursue this option.
  4. The VRAM threshold of 16GB may be too restrictive: The empty result suggests that the assistant might need to lower the threshold or accept lower-GPU instances, potentially requiring software optimizations to fit within tighter memory budgets.
  5. The deployment strategy needs revision: The original plan of deploying on a 2x RTX 3090 instance is no longer feasible with the current market conditions. The session must adapt — either by accepting lower-GPU instances, by waiting for better offers, or by changing the proving configuration to reduce memory requirements.

Mistakes and Incorrect Assumptions

While the assistant's reasoning is generally sound, several aspects warrant critical examination.

The 16GB VRAM threshold is unvalidated. The assistant sets this threshold based on an interpolation between known-working (24GB) and suspected-failing (8GB) configurations. However, cuzk's actual VRAM requirements are not stated in the message. It is possible that cuzk PoRep C2 can run on 12GB or 10GB with appropriate configuration (e.g., reduced partition workers, smaller batch sizes). The assistant does not attempt to verify the minimum by, say, checking cuzk documentation or running a test on a lower-GPU instance. The empty search result might be an artifact of an overly conservative threshold.

The search does not account for instance availability timing. Vast.ai's marketplace is highly dynamic — offers appear and disappear as hosts come online and offline. The assistant runs a single search at a single point in time and treats the result as definitive. A more robust approach would be to poll the marketplace periodically or to set up an alert for when suitable offers appear. The session later shifts to a data-driven experimental system precisely to address this kind of uncertainty, but at this moment the assistant treats the empty result as a terminal constraint.

The assistant does not explore alternative GPU models. The search filters for gpu_ram>=16000 but does not specify particular GPU models. This means the search should return any GPU with ≥16GB VRAM, including RTX 3090 (24GB), RTX 4090 (24GB), A40 (48GB), A100 (40GB or 80GB), etc. The empty result suggests that none of these are available, but the assistant does not verify this by running a broader search. For example, a search without the VRAM filter would reveal what is available, allowing the assistant to assess whether any available GPU could work with reduced memory settings.

The assistant does not consider multi-GPU memory pooling. The search filters for gpu_ram>=16000 per GPU, but cuzk might be able to utilize multiple GPUs' memory collectively. If a 2x RTX 3070 (8GB each) instance is available, the combined VRAM is 16GB, which meets the threshold — but the assistant's filter checks per-GPU VRAM, not total. This is a subtle but important distinction. The assistant assumes that each GPU must independently have enough VRAM for the proving workload, but distributed proving across multiple GPUs might relax this requirement.

The Broader Significance

This message, for all its apparent simplicity, captures a pivotal moment in the session's trajectory. It is the point at which the assistant confronts the fundamental unpredictability of cloud GPU markets. The earlier part of the session was spent building sophisticated software solutions — dynamic configuration, lifecycle management, benchmark automation — all predicated on the assumption that suitable hardware could be procured. This message reveals that assumption to be fragile.

The empty search result is a reality check. It forces a strategic pivot from the "find and deploy on known-good hardware" approach to a "discover what works through experimentation" approach. In the subsequent messages, the assistant shifts to building a data-driven experimental system with a host_perf database table, offer search APIs with known host performance overlays, and a deploy endpoint. The empty search is the catalyst for this transformation.

Moreover, the message illustrates the kind of reasoning that distinguishes effective infrastructure engineering: the ability to rapidly reassess when assumptions are invalidated, to formulate new hypotheses, to test them with available tools, and to adapt the strategy based on results. The assistant does not waste time lamenting the unavailable hardware; it immediately pivots to searching for alternatives, and when that search fails, it prepares to pivot again.

The message also demonstrates the importance of domain knowledge in infrastructure decisions. The assistant's judgment about VRAM requirements, CUDA compatibility, and GPU model capabilities all draw on deep familiarity with the proving software and the hardware landscape. This knowledge cannot be automated away — it is the product of experience and hands-on debugging.

Conclusion

Message 1068 is a deceptively dense artifact. On the surface, it is a simple search query returning zero results. But in the context of the session's arc, it is a moment of discovery, reassessment, and strategic redirection. The assistant's reasoning — from the surprise at host 93197's changed configuration, through the feasibility assessment of RTX 3070 vs RTX 3090, to the formulation and execution of a VRAM-based search — reveals the cognitive process of an engineer navigating an uncertain environment with incomplete information.

The assumptions embedded in this message — about VRAM requirements, market stability, and search completeness — are not flaws but necessary simplifications that enable timely decision-making. The empty result is not a failure but a signal that guides the session toward a more robust, data-driven approach. In the end, this message is a testament to the iterative, adaptive nature of infrastructure engineering: every dead end is also a discovery that refines the search for a viable path forward.