The $0.093 Experiment: Deploying an RTX 5060 Ti in the Quest for GPU Proof Performance Data

Introduction

In the sprawling, multi-threaded development of a vast.ai management system for Filecoin proof generation, one message stands out as a quiet but revealing moment of systematic experimentation. Message [msg 1538] contains a single bash command — a curl invocation to deploy an ultra-cheap GPU instance on vast.ai. On its surface, it is a mundane operational action: deploy a machine, wait for benchmarks, collect data. But beneath this simplicity lies a carefully reasoned decision, a data-driven strategy, and a window into the engineering mindset required to build a distributed GPU proving pipeline at scale.

The message reads:

# Deploy cheap 1x RTX 5060 Ti in Texas - ultra low cost, let's see if it can hit 12/hr
ssh 10.1.2.104 "curl -s -X POST http://127.0.0.1:1235/api/deploy -H 'Content-Type: application/json' -d '{\"offer_id\": 28145242}'"
{"ok":true,"instance_id":32735749,"message":"Started. {'success': True, 'new_contract': 32735749, 'instance_api_key': '[REDACTED]'}"

The response confirms success: instance 32735749 is now spinning up on a machine in Texas, running a single NVIDIA RTX 5060 Ti at a cost of $0.093 per hour. This article unpacks the reasoning, assumptions, and strategic context behind this single deployment command.

The Strategic Context: Building a Performance Database

To understand why this message was written, one must understand the larger system under construction. The assistant has been building vast-manager — a management service that orchestrates GPU instances on vast.ai, benchmarks their Filecoin proof generation performance, and retains only those that meet a minimum profitability threshold. The core economic equation is simple: a GPU instance costs $X per hour and must generate proofs at a rate exceeding $X / $0.008 (where $0.008 is the assumed revenue per proof). If an instance cannot sustain the minimum proofs-per-hour rate, it is destroyed.

In the minutes preceding this deployment, the assistant had been watching benchmark results roll in ([msg 1515] through [msg 1537]). An RTX 4090 in Norway achieved 61.4 proofs per hour — a strong result that passed its minimum rate of 59. A second RTX 4090 on the same physical machine managed only 46.2 proofs per hour, likely due to resource contention, and was destroyed. An RTX 5090 — the flagship consumer GPU — surprisingly managed only 25.9 proofs per hour, far below its minimum of 55, suggesting a CPU, RAM, or PCIe bottleneck. An RTX 5000Ada in Japan achieved 46.0 proofs per hour but failed its minimum of 51.

These results paint a complex picture. GPU model alone does not determine performance; the surrounding system — CPU cores, RAM, PCIe bandwidth, even geographic location affecting parameter download speeds — plays a decisive role. The assistant's response has been to systematically deploy diverse hardware configurations to build a robust performance database. The host_perf table in SQLite stores benchmark results keyed by machine_id, gpu_name, and num_gpus, and the assistant had just fixed a bug where lower scores were overwriting higher ones on the same machine ([msg 1527]).

Message [msg 1538] is the third deployment in a deliberate sampling strategy. Earlier in the same minute, the assistant deployed an RTX 5070 Ti in Quebec ($0.201/hr, offer 32506780) and a 2× RTX 5060 Ti setup in the UK ($0.215/hr, offer 29988427) — see [msg 1536] and [msg 1537]. Now, the assistant reaches for the cheapest option available: a single RTX 5060 Ti in Texas at $0.093 per hour.

The Decision-Making Process: Why This Machine?

The selection of offer 28145242 was not random. In [msg 1535], the assistant queried the offers API and sorted by price, then manually inspected the top candidates. The RTX 5060 Ti at $0.093/hr was the cheapest offer in the entire listing. The comment in the bash command — "ultra low cost, let's see if it can hit 12/hr" — reveals the precise reasoning.

The minimum rate calculation is straightforward: $0.093 / $0.008 ≈ 11.6, rounded up to 12 proofs per hour. This is an extremely low bar compared to the RTX 4090's minimum of 59 or the RTX 5090's minimum of 55. The assistant is essentially asking: can the cheapest available GPU, with the lowest performance threshold, deliver acceptable results?

But the machine's specs raise red flags. The offer details from [msg 1535] show:

Assumptions and Implicit Risks

Every deployment carries assumptions, and this one is no exception. Several implicit beliefs underpin the decision:

The deploy API works correctly. The assistant had just deployed two other instances via the same endpoint ([msg 1536] and [msg 1537]) and received successful responses. The API had been recently updated to reject offers on known-bad hosts before creating instances (see chunk 0 summary). The assistant assumes that machine 10400 is not in the bad_hosts table, or if it is, the API will reject it gracefully.

The benchmark will complete and report results. The assistant's logging infrastructure had been hardened — benchmark error reporting was improved, logs were being shipped to the manager's log-push API. But the assistant does not verify that the instance actually starts benchmarking after deployment. It assumes the vast-manager lifecycle (setup → params → benchmark → bench_done) will execute correctly.

The RTX 5060 Ti can achieve 12 proofs per hour. This is the central hypothesis being tested. The assistant has no prior data for this GPU model. The closest reference point is the 4× RTX 5060 Ti setup (machine 37011) that achieved 38.8 proofs per hour total, or roughly 9.7 per GPU. A single RTX 5060 Ti would need to exceed that per-GPU rate. The assistant is betting that a dedicated single-GPU machine, without contention from three sibling GPUs, can outperform the per-GPU average of the multi-GPU setup.

The cheap price justifies the risk. At $0.093/hr, even a failed benchmark costs only a few cents. The assistant is willing to burn small amounts of capital to gather data points. This is a deliberate, low-cost exploration strategy.

The geographic location (Texas) won't cause issues. Unlike the Japan-based instance that suffered from slow parameter downloads ([msg 1523]), Texas has good connectivity (DL: 2152 Mbps). The assistant assumes parameter download time won't be a bottleneck.

Potential Mistakes and Oversights

The most significant oversight is the lack of a check against the bad_hosts table. Earlier in this segment (see chunk 0 summary), the assistant noted that "some were immediately killed due to pre-emptively added entries in the bad_hosts table." If machine 10400 had been pre-emptively blacklisted — perhaps due to a previous failed benchmark or manual intervention — this deployment would be immediately terminated, wasting time and the $0.093/hr rental cost.

The assistant also does not verify that the instance has sufficient disk space, that the Docker image is pulled successfully, or that the curio daemon starts without errors. These are handled by the vast-manager's entrypoint script, but the assistant places full trust in that infrastructure.

Another subtle issue: the assistant had just fixed the host_perf table to keep the highest benchmark score ([msg 1527]), but this fix was deployed only moments before ([msg 1529]). The new binary was pushed and the service restarted. If the fix introduced any regression — a crash, a database schema mismatch, a race condition — the benchmark result from this instance might be lost or mishandled. The assistant does not verify the service health after restart beyond checking systemctl status.

Input Knowledge Required

To fully understand this message, one needs knowledge of several interconnected systems:

  1. The vast.ai marketplace: How GPU instances are rented, how offers are structured, and how contracts are created. The offer_id (28145242) maps to a specific listing with known hardware specs and pricing.
  2. The vast-manager API: The /api/deploy endpoint that accepts an offer ID and creates a vast.ai contract, then registers the instance in the manager's internal database.
  3. The economic model: The minimum rate calculation (dph / 0.008) that determines whether an instance is profitable. The $0.008 figure represents the assumed revenue per proof — a critical business parameter.
  4. The benchmark pipeline: The lifecycle from deployment through parameter download, warmup (PCE extraction), and multi-proof benchmarking that produces a proofs-per-hour rate.
  5. The GPU performance landscape: Knowledge that RTX 5060 Ti is a mid-range GPU, that PCIe bandwidth and CPU cores matter for proof generation, and that single-GPU configurations often outperform multi-GPU setups on a per-GPU basis due to reduced contention.

Output Knowledge Created

This message produces several forms of knowledge:

  1. A new instance record: Instance 32735749 is now tracked in the vast-manager database, with state transitions (setup → params → benchmark → running or killed) that will be logged and observable.
  2. A potential host_perf record: If the benchmark completes successfully, machine 10400 will receive a performance score that expands the training data for predicting which hardware configurations are profitable.
  3. A test of the deployment pipeline: Each successful deployment validates that the vast-manager's deploy API, the entrypoint script, the Docker image, and the benchmark harness all function correctly end-to-end.
  4. A data point for the RTX 5060 Ti: This GPU model had not been tested before. Even a failed benchmark provides useful information — it might reveal that the minimum rate is unachievable, that the machine is blacklisted, or that some other environmental factor prevents successful benchmarking.

The Broader Significance

Message [msg 1538] is a microcosm of the engineering approach throughout this session. The assistant is not building a static system; it is building a learning system — one that deploys, measures, learns, and adjusts. Each deployment is an experiment. The comment "let's see if it can hit 12/hr" captures the scientific mindset: formulate a hypothesis, design an experiment, collect data, and update the model.

This approach is particularly valuable in the GPU cloud market, where hardware heterogeneity is extreme. Two machines with the same GPU model can produce wildly different results depending on CPU, RAM, PCIe topology, and even the phase of the moon (or rather, the contention from neighboring instances on the same physical host). The only way to build a reliable performance model is to gather empirical data across diverse configurations.

The $0.093/hr RTX 5060 Ti represents the low-cost frontier of this exploration. If it succeeds, it opens the door to ultra-cheap proof generation. If it fails, the cost of learning that lesson is negligible. Either way, the assistant's understanding of the performance landscape improves.

Conclusion

A single curl command, a single deployment — but message [msg 1538] carries the weight of a carefully considered experimental design. It reflects the assistant's systematic approach to building a distributed GPU proving system: deploy broadly, measure rigorously, and let data drive decisions. The RTX 5060 Ti in Texas may succeed or fail, but the knowledge gained will inform the next deployment, and the next, until the performance database is rich enough to predict profitability at a glance.

In the end, this message is not about deploying a GPU. It is about the engineering philosophy of learning through measurement — a philosophy that transforms a simple bash command into a scientific instrument.