# Run models on your hardware

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Source documentation checked: 2026-10-09 (UTC). Directory digest: `d6020b185c1948e1d2ea6265edcb6d00574ca822925016219babede247d38cdd`.

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## Run models on your hardware

Plan a model configuration that fits available memory and can be measured on the actual machine.

Decision question: Which exact model configuration fits the machine and meets the required throughput?

When to revisit: Before choosing hardware or deploying a model, and after model, context, concurrency or runtime changes.

Agent research task: Separate memory estimates from measured compatibility. Propose a bounded benchmark using the exact hardware, model variant, context and runtime; do not claim a fit from parameter count alone.

Compare:

- processor and accelerator
- RAM and accelerator memory
- model digest and quantization
- context and concurrency
- runtime revision and measured throughput

Sources:

- [MLPerf Inference](https://mlcommons.org/benchmarks/inference-datacenter/): Inference system results with workload, hardware and submission rules. Read the official results and their applicable benchmark round and division. Compare matching scenarios and quality targets. A datacenter result does not prove performance on a laptop.
- [llama.cpp benchmark tool](https://github.com/ggml-org/llama.cpp/blob/master/tools/llama-bench/README.md): A local measurement procedure for prompt processing and token generation under explicit runtime settings. Public tool documentation. Downloading a model or running a benchmark needs separate permission. Record hardware, model digest, quantization, backend, context, batch settings and software revision.
- [Ollama model library](https://ollama.com/library): Model pages and available variants for an Ollama-compatible setup. Browse the published model pages. Availability in a library is not proof of access through a particular account. Check the model's own licence and variant. A listed size is not a complete working-memory estimate.

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