Local LLM Deployment Benchmarking Guide for Small Teams
Run a repeatable benchmark for local LLM deployments. Compare throughput, latency, memory usage, and cost across models and quantizations so your team can choose one default instead of switching tools every week.
Why benchmark local LLMs instead of trusting leaderboards
Leaderboards measure average quality. Small teams care about actual workflow behavior: how fast a draft completes, how much RAM a model uses while another app runs, and whether quantization changes the result enough to matter. Those numbers are hardware-dependent, prompt-dependent, and often different from public benchmarks.
Start from your real workload, not a model card. Pick one representative task, run it on two quantizations, and measure tokens per second, time to first token, and memory usage. That single dataset is more useful than five benchmark tables.
If you want background on why local inference is worth evaluating, start with local LLM deployment on macOS before adding a benchmark layer.
What to measure
Keep the metric set small. More metrics create more noise and make decisions slower. Measure only what changes a deployment choice.
| Metric | What it tells you | Practical target |
|---|---|---|
| Time to first token | Interactive feel for chat and assistance | < 1.5s for single-turn tasks |
| Tokens per second | Sustained throughput for generation | > 20 t/s on 7B Q4 for Mac |
| Peak RAM usage | Whether the model fits daily workflows | Leave headroom for other apps |
| Prompt fidelity | Whether quantization changed behavior | Compare outputs on a fixed test set |
| Cold start time | How long the model takes after a pause | Useful for scripts and jobs |
The benchmark is only useful if it runs on the same hardware you use daily. Cloud or another team's numbers do not transfer.
A minimal benchmark workflow
The goal is a repeatable local run, not a perfect benchmark suite. Use one model, one quantization, one prompt set, and record the numbers. Add complexity later if the decision still feels uncertain.
- Install Ollama or llama.cpp and confirm the runtime version.
- Choose one model family and two quantizations, such as Q4_K_M and Q5_K_M.
- Create a small fixed prompt set that mirrors real tasks.
- Run each prompt five times and record time to first token and tokens per second.
- Compare memory usage and output quality, then choose one default.
If you want to compare against a hosted API cost model, see local LLM versus cloud API cost comparison.
Interpreting the results
Small differences in tokens per second rarely change the user experience. Large differences in time to first token do. If two quantizations differ by less than 2 t/s, choose the one with better instruction following and lower memory usage.
For small teams, the useful rule is simple: run one model by default, keep one fallback model for longer tasks, and benchmark again only when hardware or model availability changes.
For hardware sizing and team rollout, see local LLM deployment guide for small teams.
Operational limits and notes
Benchmarks decay. A model that performs well today may change after a runtime update, a quantization toolchain change, or a new prompt shape. Re-run the benchmark after major changes, not on a fixed calendar. Also remember that local inference is only part of the system. Network fallbacks, prompt routing, and evaluation sets matter more in production than raw speed.