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SALT19 Model Memory Fit Calculator
x402
SALT19 Model Memory Fit Calculator is a paid API for AI agents from api.salt19.com, paid per call via x402, $0.05/call, status down (last checked 2026-09-15).
Estimates whether an LLM model will fit in a given VRAM budget, returning a FIT/NO-FIT verdict with estimated memory breakdown and headroom.
Price / call$0.05
Success—
StatusDown
How this service works
SALT19 is an independent applied AI systems lab behind EvoMind governed cognition, the MCP-native Agent Utility Grid, the ARCS research community, AeroClear UAS flight intelligence, and practical software for real-world work.
Output
A JSON object containing a FIT or NO-FIT verdict, estimated model weight in GiB, estimated total VRAM usage in GiB (including overhead), headroom in GiB remaining after loading, and a list of known modeling limitations (e.g. KV cache and architecture-specific buffers are not modeled).
Example use cases
Pre-deployment VRAM feasibility check
“I'm about to pull a 13B parameter model quantized to 8-bit onto a GPU with 16 GiB VRAM. Can you check if it will actually fit and tell me how much headroom I'd have left?”
Consumer GPU hobby build planning
“I want to self-host a 7B model at 4-bit quantization on my gaming PC which has 8 GiB of VRAM. Will it fit, and what's the estimated memory breakdown?”
Enterprise multi-GPU sizing for 70B model
“We're evaluating whether a 70B parameter model at 8-bit quantization fits on an 80 GiB A100 with a runtime overhead ratio of 0.15 — can you run the memory fit calculation?”
Technical details
Endpoint
POSThttps://api.salt19.com/v1/model-memory-fitWhen to prefer
Choose this endpoint when you need a fast, programmatic check of whether a specific quantized LLM will fit in a known VRAM budget before downloading or deploying the model. It is purpose-built for LLM memory estimation with quantization awareness, unlike generic hardware calculators. Prefer it over manual formulas when you need structured output (verdict + headroom) that an agent can act on automatically.
Side effects
- Invalid or out-of-range vram_gb (below 0.1 or above 10000) causes validation error
- parameter_count_b below 0.01 or above 10000 rejected
- quantization_bits outside 1–32 range rejected
- runtime_overhead_ratio outside 0–2 range rejected
- Missing required fields may return 400 or partial computation
- Estimate may be inaccurate for models with non-standard architectures due to KV cache and buffer exclusions
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| vram_gb | number | |
| parameter_count_b | number | |
| quantization_bits | number | |
| runtime_overhead_ratio | number |
Output
Schema · Response
{
"type": "json",
"example": {
"verdict": "FIT",
"vram_gib": 11,
"limitations": [
"KV cache and architecture-specific buffers are not modeled"
],
"headroom_gib": 7.25,
"estimated_total_gib": 3.75,
"estimated_weight_gib": 3.26
}
}Tags
x402
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