Probing the Unknown: How a Single API Schema Discovery Unlocked GPU Instance Selection

The Message

ssh 10.1.2.104 "vastai search offers 'num_gpus>=1 cuda_max_good>=13.0 disk_space>=200' --raw 2>/dev/null" | python3 -c "
import json, sys
data = json.load(sys.stdin)
# check what VRAM fields are available
if data:
    gpu_fields = [k for k in data[0] if 'gpu' in k.lower() and 'ram' in k.lower()]
    print('GPU RAM fields:', gpu_fields)
    for o in sorted(data, key=lambda x: x.get('dph_total', 999))[:5]:
        for f in gpu_fields:
            print(f'  {f}: {o.get(f)}')
        print(f'  gpu_name: {o[\"gpu_name\"]}, num_gpus: {o[\"num_gpus\"]}')
        print()
" 2>/dev/null
GPU RAM fields: ['gpu_ram', 'gpu_total_ram']
  gpu_ram: 12282
  gpu_total_ram: 12282
  gpu_name: RTX 4070S, num_gpus: 1

  gpu_ram: 12282
  gpu_total_ram: 12282
  gpu_name: RTX 4070, num_gpus: 1

  gpu_ram: 16311
  gpu_total_ram: 16311
  gpu_name: RTX 5060 Ti, num_gpus: 1

  gpu_ram: 12282
  gpu_total_ram: 12282
  gpu_name: RTX 4070 Ti, num_gpus: 1

  gpu_ram: 12227
  gpu_total_ram: 12227
  gpu_name: RTX 5070, num_gpus: 1

At first glance, this appears to be a routine debugging command—just another API exploration in a long coding session. But this message represents something far more interesting: the precise moment when an assumption collided with reality, and the agent shifted from operating under a flawed mental model to building an accurate one. It is a microcosm of the entire scientific method compressed into a single interaction: hypothesis, experiment, observation, and revision.

The Context: A Search for Viable Hardware

To understand why this message was written, we must reconstruct the situation that led to it. The broader session involved deploying the cuzk GPU proving engine—a high-performance computational tool for Filecoin storage proofs—on rented GPU instances from Vast.ai, a marketplace for distributed cloud compute. The agent had been battling a persistent Out-of-Memory (OOM) crisis. Low-RAM instances, particularly a BC Canada machine with 125GB of RAM and two RTX 3090s, were crashing during the warmup phase of benchmarking. The root cause had been traced to the daemon spawning too many partition workers simultaneously during Pre-Compiled Constraint Evaluator (PCE) extraction, each consuming substantial memory.

After implementing a fix that dynamically scaled partition workers based on available RAM, the agent destroyed the failed BC Canada instance and began searching for replacement hardware. The goal was straightforward: find GPU instances with enough VRAM (video memory) to run cuzk's proving workloads reliably. The agent assumed this would be a simple filtering operation on Vast.ai's search API.

The Failure That Triggered the Probe

The immediate predecessor to our subject message was a search query that returned zero results ([msg 1069]). The agent had run:

vastai search offers 'num_gpus>=1 cuda_max_good>=13.0 disk_space>=200 gpu_total_ram>=16000'

This query asked for instances with at least one GPU, CUDA compatibility above 13.0, at least 200GB of disk space, and—crucially—at least 16GB of total GPU RAM. The response was empty. Zero offers. This was deeply suspicious. The Vast.ai marketplace is vast; the agent had seen dozens of RTX 3090, RTX 4090, and A-series GPU offers in previous searches. The filter should have returned many candidates.

The agent's immediate reaction was not to trust the API blindly. Instead of concluding "there are no suitable GPUs available," the agent questioned the filter itself. This is the hallmark of experienced debugging: when the world doesn't behave as expected, suspect your measurement instruments first.

The Diagnostic Probe: What This Message Actually Does

The subject message is a carefully constructed diagnostic probe. It does several things at once:

1. Broadens the search criteria: It removes the gpu_total_ram>=16000 filter entirely, keeping only the less restrictive constraints (num_gpus>=1, cuda_max_good>=13.0, disk_space>=200). This ensures the query will return results, giving the agent data to analyze.
2. Requests raw JSON output: The --raw flag tells Vast.ai's CLI to output the raw JSON response rather than a formatted table. This is essential for programmatic inspection.
3. Pipes through a Python introspection script: This is the heart of the probe. The Python script dynamically discovers which fields in the response contain both "gpu" and "ram" in their names, then prints the values of those fields for the first five offers. The script is remarkably elegant in its approach. Rather than hardcoding field names (which would require the agent to know the API schema in advance), it uses introspection: [k for k in data[0] if 'gpu' in k.lower() and 'ram' in k.lower()]. This list comprehension scans the keys of the first offer object, filtering for any key that contains both "gpu" and "ram" (case-insensitive). This is a robust technique for exploring an unfamiliar API schema—it works regardless of whether the field is called gpu_ram, gpu_total_ram, gpu_memory, vram_gb, or any other naming convention.

What the Probe Revealed

The output was illuminating. The API exposes two relevant fields: gpu_ram and gpu_total_ram. For every offer examined, both fields held identical values: 12282 MB for an RTX 4070S, 12282 MB for an RTX 4070, 16311 MB for an RTX 5060 Ti, and so on.

This immediately explains why the previous filter failed. The field gpu_total_ram appears to represent per-GPU VRAM (in megabytes), not total VRAM across all GPUs. An RTX 4070S has approximately 12GB of VRAM, which matches the value 12282 MB. The field name gpu_total_ram is misleading—it suggests a sum across all GPUs, but it actually reports the VRAM of a single GPU. Since no single GPU in the search results had 16GB or more of VRAM (the RTX 5060 Ti came closest at ~16GB but the value 16311 is still just under the 16000 MB threshold... actually 16311 > 16000, so it should have matched). Wait—let me re-examine.

Actually, looking more carefully: the RTX 5060 Ti shows gpu_ram: 16311 and gpu_total_ram: 16311. That's ~16.3 GB, which is above 16000 MB. So why didn't the filter gpu_total_ram>=16000 match it?

The answer likely lies in the way Vast.ai's search API handles numeric comparisons. The value 16311 might be stored as a string, or there might be a unit mismatch, or the filter might be applying to a different field than expected. But the more important insight is that the agent now knows the actual field names and values. With this knowledge, the agent can construct a correct filter—perhaps using gpu_ram>=24000 to find RTX 3090s (24GB) or RTX 4090s (24GB), or using a different approach entirely.

Assumptions Made and Broken

This message reveals several assumptions that the agent had been operating under:

Assumption 1: gpu_total_ram means total RAM across all GPUs. The name strongly suggests this interpretation. "Total" in a multi-GPU context naturally reads as "sum of all GPU memory." The probe revealed this assumption was wrong—gpu_total_ram is per-GPU VRAM, identical to gpu_ram.

Assumption 2: The API schema is known and stable. The agent initially hardcoded field names in filter queries without verifying them. This is a common shortcut when working with familiar APIs, but it can lead to silent failures when the schema differs from expectations.

Assumption 3: A filter returning zero results means no matching hardware exists. This is the most dangerous assumption. The agent's first instinct was to question the filter, not the hardware availability—a wise instinct that saved significant debugging time.

The Thinking Process Visible in the Message

The reasoning embedded in this message is subtle but powerful. The agent is not just running a command; it is executing a structured investigation:

1. Observe anomaly: Filter returns zero results despite known hardware availability.
2. Form hypothesis: The filter field name or semantics might be wrong.
3. Design experiment: Remove the suspect filter, fetch raw data, and introspect the schema.
4. Execute: Run the probe command.
5. Analyze: Examine the output to understand the actual field semantics. This is textbook scientific debugging. The agent resists the temptation to add more filters or try random combinations. Instead, it goes to the source—the raw API response—and builds a mental model from first principles. The choice to use dynamic field discovery ([k for k in data[0] if ...]) rather than printing the entire JSON blob is also telling. The agent is focused and efficient. It knows exactly what information it needs (the names and values of GPU RAM fields) and extracts only that. Printing the full JSON could have produced hundreds of lines of output, obscuring the relevant signal.

Input Knowledge Required

To fully understand this message, one needs:

• Knowledge of Vast.ai's CLI: Understanding that vastai search offers returns GPU instance listings, that --raw outputs JSON, and that the filter syntax uses field=value or field>=value patterns.
• Knowledge of GPU VRAM requirements: Understanding that cuzk proving requires GPUs with sufficient video memory (at least ~16GB, preferably 24GB for RTX 3090/4090).
• Knowledge of Python list comprehensions and JSON parsing: The inline Python script uses standard library features (json.load, list comprehensions, sorted with key functions).
• Context from previous messages: Understanding that the agent had just destroyed a failed instance and was actively searching for replacements, and that two previous searches had returned zero results.

Output Knowledge Created

This message produces several concrete pieces of knowledge:

1. The API exposes two GPU RAM fields: gpu_ram and gpu_total_ram, which appear to be identical in value.
2. Both fields represent per-GPU VRAM, not total VRAM: The values match single-GPU specifications (12GB for RTX 4070, 16GB for RTX 5060 Ti).
3. The field gpu_total_ram is misleadingly named: Despite suggesting a total across GPUs, it reports individual GPU memory.
4. No single GPU in the current market with CUDA >= 13.0 and disk >= 200GB has more than ~16GB of VRAM: The RTX 5060 Ti at 16311 MB is the highest observed.
5. The correct filter for finding high-VRAM GPUs would need to use a different threshold or field: Since gpu_ram values are per-GPU, filtering for gpu_ram>=24000 would find RTX 3090s and RTX 4090s.

The Broader Significance

This message is a beautiful example of a principle that applies far beyond GPU instance selection: when your tools return unexpected results, investigate the tools before blaming the world. The agent could have concluded that no suitable hardware existed, given up on the multi-GPU approach, and accepted lower performance. Instead, it invested a small amount of effort to understand its measurement tools, and that investment paid off with accurate knowledge.

In the context of the larger session, this probe was the turning point. The agent would go on to build a comprehensive hardware discovery system, complete with a database of known host performance, API endpoints for offer search with performance overlays, and a web UI for managing deployments. But none of that would have been possible without this single moment of diagnostic clarity—the moment when the agent stopped guessing and started measuring.

The message also demonstrates a key skill in AI-assisted development: the ability to construct ad-hoc introspection tools on the fly. The agent didn't have a pre-built schema inspector for Vast.ai's API. It built one in a single line of Python, piped through SSH, and executed it against live data. This kind of just-in-time tool construction is one of the most powerful capabilities of modern coding assistants, and this message showcases it perfectly.