# Model Can It Fit

> Estimate whether a Hugging Face decoder-only LLM, MoE, or VLM fits in Intel GPU VRAM for a quantization, context length, concurrency, runtime, and tensor-parallel setting. Use for memory-fit or max-model-len planning before launch. Reports weights, KV cache, activations, framework overhead, and first mitigation. Not for diffusion. Memory-only — does NOT predict throughput, tokens/sec, latency, or runtime config; route those to bench/deploy/recommend skills.

## Facts
- Page: https://tashan.sh/capability/plugin-intel-gpu-ai-skills-model-can-it-fit
- tashan id: plugin:intel/gpu-ai-skills/model-can-it-fit
- Source: https://github.com/intel/gpu-ai-skills
- Type: plugin
- Category: ai
- tashan score: 45.0 / 100
- Adoption: 7.0
- Upkeep: 100.0
- Freshness: 99.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: Apache-2.0
- Official: no

## Install

```sh
/plugin marketplace add intel/gpu-ai-skills
/plugin install model-can-it-fit@intel-model-skillpack
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-09-13 by tashan (https://tashan.sh) from public evidence. Scorer s5.