Selecting GPU Instances for Distributed Proving: A Decision-Making Analysis
In message [msg 1536] of a complex multi-session coding project, the AI assistant acts as a fleet operator for a distributed GPU proving cluster, analyzing hardware offers from the vast.ai marketplace and deciding which machines to deploy. This message is a window into the real-world operational decisions required to run a cost-effective proof-of-replication (PoRep) proving pipeline—decisions that blend hardware expertise, economic modeling, and lessons learned from previous failures. The message is reproduced below in full:
Good opportunities I see:
>
1. #28145242 — 1x RTX 5060 Ti, $0.093/hr (mid:10400, Texas) — Super cheap, only needs 12 proofs/hr. But Xeon E5-2683 v4 is very old (Broadwell), only 16 cores, PCIe 3.1. Might be too slow.
>
2. #32506780 — 1x RTX 5070 Ti, $0.201/hr (mid:39238, Quebec) — Decent PCIe (13.1), 744GB RAM, 28 cores, only needs 26/hr. Good value if it benchmarks well.
>
3. #29988427 — 2x RTX 5060 Ti, $0.215/hr (mid:37415, UK) — EPYC 7K62, 48 cores, PCIe 13.5, needs 27/hr.
>
4. #31639237 — 1x RTX PRO 4000, $0.228/hr (mid:55891, Norway) — Same machine type that failed benchmark before. But this one has EPYC 7B13, 64 cores, PCIe 26.6. Different machine from the previous failure (that was mid:55891 which... actually wait, this IS the same machine_id). Previous failure was on a different machine though (the one that reported 0 proofs was mid:55891? Let me check).
>
5. #32676813 — 1x RTX 3090, $0.242/hr (mid:5468, Japan) — EPYC 7C13, 64 cores, PCIe 24.8. Good specs but DL only 534 Mbps, param download will be slow.
>
Let me deploy a few promising ones. I'll pick a diverse set: [bash] # Deploy RTX 5070 Ti in Quebec - good specs, good value 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\": 32506780}'" {"ok":true,"instance_id":32735739,"message":"Started. {'success': True, 'new_contract': 32735739, 'instance_api_key': '[REDACTED]'}"}
The assistant is not merely executing a scripted workflow; it is reasoning aloud about trade-offs, drawing on accumulated operational experience, and making judgment calls under uncertainty. This article examines the message in depth—the context that produced it, the decision framework the assistant applies, the assumptions embedded in its reasoning, and the knowledge that flows into and out of this single deployment action.
The Operational Context
This message arrives after an intensive period of platform hardening and operational debugging. In the preceding messages, the assistant had just fixed a critical data integrity bug in the host_perf database table. The table used INSERT ... ON CONFLICT ... DO UPDATE with a primary key of (machine_id, gpu_name, num_gpus), and it was blindly overwriting benchmark scores with whatever result arrived most recently. This caused a real loss: an RTX 4090 on machine 15136 had achieved 61.4 proofs per hour in one benchmark run, but a second instance running concurrently on the same physical machine completed later with only 46.2 proofs/hr and overwrote the higher score ([msg 1524]). The assistant fixed this by changing the ON CONFLICT clause to use MAX(bench_rate), ensuring that the best observed performance is always retained ([msg 1527]).
Earlier benchmarks had established a baseline of expected performance across different GPU models. An RTX 4090 reached 61.4 proofs/hr and passed its minimum rate threshold of 59. An RTX PRO 4000 reached 55.1 proofs/hr. An RTX 3090 reached 35.6 proofs/hr. But a surprising failure stood out: an RTX 5090—NVIDIA's most powerful consumer GPU, with 24 GB of GDDR7 memory and the Blackwell architecture—managed only 25.9 proofs/hr before being destroyed for failing its minimum rate threshold of 55 ([msg 1521]). This result was particularly instructive because it demonstrated that raw GPU compute power does not guarantee proving performance. The assistant has internalized this lesson, and it shows in the hardware analysis of this message.
With two instances currently running (the RTX 3090 at 35.6/hr and the RTX 4090 at 61.4/hr), the assistant queries the offers API and receives a list of available machines. Message [msg 1536] is its analysis of the top candidates and the deployment of one of them.## The Decision Framework: How the Assistant Evaluates GPU Offers
The assistant's analysis of each offer reveals a sophisticated multi-criteria decision framework. It does not simply sort by price or by GPU model; instead, it evaluates each candidate holistically, weighing GPU capability, CPU architecture, PCIe bandwidth, RAM, geographic location, and the derived minimum proof rate required for profitability.
Offer #1: RTX 5060 Ti at $0.093/hr
This is the cheapest offer on the list, at less than ten cents per hour. The minimum rate needed to break even—given a target cost of $0.008 per proof—is only 12 proofs per hour. The assistant immediately identifies the risk: the Xeon E5-2683 v4 is a Broadwell-era CPU from 2016, with only 16 cores and PCIe 3.1 bandwidth. The phrase "Might be too slow" reflects a nuanced understanding that proving performance depends on CPU throughput for parameter generation, Merkle tree construction, and data movement, not just GPU tensor cores. A machine with an ancient CPU and narrow PCIe bus could bottleneck the GPU, causing it to underperform despite the low price.
Offer #2: RTX 5070 Ti at $0.201/hr (Selected for Deployment)
This is the offer the assistant ultimately chooses. The RTX 5070 Ti is a mid-range Blackwell GPU with 16 GB of GDDR7 memory. The machine has 744 GB of RAM, 28 CPU cores (Xeon Platinum 8273CL), and PCIe 13.1 bandwidth. The required minimum rate is 26 proofs per hour. The assistant's assessment—"Good value if it benchmarks well"—is cautiously optimistic. It recognizes that benchmark results are the ultimate arbiter; no amount of spec analysis can substitute for empirical measurement. The Quebec location is also favorable for latency to the controller host.
Offer #3: 2× RTX 5060 Ti at $0.215/hr
This dual-GPU configuration in the UK uses an EPYC 7K62 processor with 48 cores and PCIe 13.5. The assistant notes it needs 27 proofs per hour. The multi-GPU setup is interesting because previous results with multi-GPU machines have been disappointing: a 4× RTX 5060 Ti machine achieved only 38.8 proofs/hr ([msg 1524]), far below what four GPUs should theoretically deliver. This suggests that the current proving pipeline does not scale linearly across multiple GPUs, or that resource contention (CPU, memory bandwidth, PCIe) becomes the limiting factor. The assistant does not explicitly mention this history, but the decision not to deploy this offer implies that skepticism.
Offer #4: RTX PRO 4000 at $0.228/hr
This offer triggers a moment of genuine confusion. The assistant recognizes the machine ID (55891) from a previous failure—an RTX PRO 4000 that reported zero proofs and was marked as a bad host. But the assistant hesitates: "Previous failure was on a different machine though (the one that reported 0 proofs was mid:55891? Let me check." This self-interruption is revealing. The assistant is trying to recall whether the failed machine had the same machine_id or a different one. The machine_id in vast.ai's system identifies a physical host, not a GPU. A single machine can host multiple GPU instances. The assistant is correctly reasoning that a previous failure on the same physical machine might indicate systemic issues (bad CPU, insufficient RAM, network problems) that would affect any GPU on that host. But it cannot immediately resolve the question and moves on without deploying this offer.
Offer #5: RTX 3090 at $0.242/hr in Japan
This offer has excellent specs—an EPYC 7C13 processor, 64 cores, PCIe 24.8—but the assistant flags a critical weakness: download speed of only 534 Mbps. The proving pipeline requires downloading large parameter files (multiple gigabytes) before benchmarking can begin. A slow download means the instance will spend hours in the params_done state before the benchmark even starts, increasing the risk of timeout or preemption. The assistant has seen this before: an RTX 5000Ada in Japan took significantly longer to complete its lifecycle ([msg 1522]). The assistant declines to deploy this offer.
The Deployment Decision
After this analysis, the assistant deploys exactly one instance: the RTX 5070 Ti in Quebec. The choice is instructive. The assistant does not deploy the cheapest option (#1), the most powerful option (#5), or the multi-GPU option (#3). It selects the offer that appears to have the best balance of GPU capability, supporting hardware (CPU, PCIe, RAM), geographic proximity, and reasonable price. The deployment command is straightforward—a single curl POST to the vast-manager's deploy API—but the decision behind it is the product of hundreds of lines of prior conversation, multiple benchmark cycles, database schema fixes, and operational debugging.
Assumptions and Potential Blind Spots
The assistant's reasoning rests on several assumptions that deserve scrutiny. First, it assumes that the minimum rate threshold (derived from dph_total / 0.008) is a reliable profitability boundary. The $0.008 per proof target was established earlier in the project as a rough economic benchmark, but it may not account for variable costs like data transfer, storage, or the overhead of managing failed instances. If the true cost per proof is higher, some of these "profitable" offers could be money-losers.
Second, the assistant assumes that benchmark results from one instance generalize to another instance on the same machine model. The RTX 4090 on machine 15136 achieved 61.4 proofs/hr, but a second instance on the same machine achieved only 46.2 proofs/hr due to resource contention. The assistant's fix to keep the MAX benchmark score is a reasonable heuristic, but it may overestimate the performance achievable when multiple instances run concurrently on the same host.
Third, the assistant does not consider the time value of money or opportunity cost. Deploying an instance that takes hours to download parameters and complete benchmarking consumes time during which the machine could be proving. The RTX 3090 in Japan was rejected for slow download speed, but the assistant does not quantify how much that delay costs.
Fourth, the assistant assumes that the bad_hosts table is accurate. Earlier in the session ([msg 1519]), the assistant had preemptively added entries to bad_hosts for machines that looked suspicious, which caused some newly deployed instances to be killed immediately. There is a risk of false positives—marking a machine as bad based on incomplete or misleading data.
Knowledge Flow: Inputs and Outputs
The input knowledge required to understand this message is substantial. One must know the vast.ai marketplace structure (offers, instances, machine IDs, GPU models, CPU architectures), the economics of Filecoin proving (proofs per hour, cost per proof, minimum rate thresholds), the technical requirements of the CuZK proving pipeline (parameter download, PCIe bandwidth sensitivity, CPU bottlenecks), and the operational history of this specific project (previous benchmark failures, the RTX 5090 anomaly, the bad_hosts system, the host_perf table fix).
The output knowledge created by this message is equally significant. The deployment of the RTX 5070 Ti will produce a new benchmark result that either confirms or challenges the assistant's assessment. If the machine achieves a high proofs-per-hour rate, it validates the decision framework and provides a new data point for evaluating similar offers. If it fails—either by underperforming or by encountering infrastructure issues—it will add to the bad_hosts table and refine the assistant's future decisions. Each deployment is simultaneously an operational action and an experiment that generates knowledge about the proving pipeline's behavior on diverse hardware.
The Thinking Process
The assistant's reasoning in this message is notable for its transparency and its willingness to express uncertainty. The self-correction about machine ID 55891—"actually wait, this IS the same machine_id"—shows the assistant thinking in real time, catching its own error as it writes. The phrase "Let me check" is a genuine expression of incomplete knowledge; the assistant cannot immediately determine whether the previous failure was on the same physical host and decides to err on the side of caution by not deploying.
The structure of the analysis also reveals a prioritization scheme. The assistant lists offers in order of increasing price, but its commentary focuses on risk factors rather than cost. The cheapest offer is rejected for being "too slow." The most expensive offer is rejected for slow download speed. The middle-priced offers receive the most serious consideration. This suggests that the assistant is optimizing for reliability and throughput, not just cost minimization—a sophisticated stance for a system that could have been programmed to simply pick the cheapest option.
Conclusion
Message [msg 1536] captures a moment of operational decision-making in a distributed proving system. The assistant evaluates five GPU offers from the vast.ai marketplace, applies a multi-factor decision framework informed by prior benchmark data, hardware knowledge, and economic constraints, and deploys one instance. The message is a microcosm of the larger project: it blends infrastructure management, data-driven reasoning, and real-time debugging into a single coherent action. The deployment of the RTX 5070 Ti is not an end point but a beginning—the start of another experiment that will feed back into the system's understanding of what makes a profitable proving machine.