Benchmark text models

Use local-ai benchmark to compare text inference through a running LocalAI server. Install and configure the models first. The command sends sequential, non-streaming requests to /v1/chat/completions and reports full request latency and end-to-end completion tokens per second.

Pass one or more configured model names. To compare backends, configure separate model aliases with the desired backend, then pass those aliases. Backend names alone are not model names. The command does not install, discover, or unload models; the server’s normal loading and eviction settings still apply.

For example, with models named text-llama-cpp and text-vllm configured:

local-ai benchmark text-llama-cpp text-vllm --runs 5 --warmup 1 \
  --prompt 'Explain how a rainbow forms.' --max-tokens 128

To save settings, per-model summaries, and every measured sample:

local-ai benchmark text-llama-cpp text-vllm --json > benchmark.json

For an authenticated server, set LOCALAI_API_KEY or API_KEY in the environment. The API key is excluded from the JSON report. Use --endpoint for a remote server or reverse proxy:

local-ai benchmark text-llama-cpp --endpoint https://localai.example.org/proxy/v1

The endpoint accepts a server root, an optional /v1 suffix, and trailing slashes. A reverse proxy path prefix is preserved. Redirects are refused. Credentials in the URL, query strings, and fragments are rejected.

Arguments and flags

Every request sets temperature to 0 and streaming to false. Interrupting the command cancels the active request. A failed request stops the benchmark with a model and run error; results are written only after every model succeeds.

Reading the results

Each model has minimum, mean, and maximum latency across measured requests. Latency runs from sending the request through parsing the complete response. It includes transport, queueing, prompt processing, generation, and response parsing. This command does not measure time to first token.

End-to-end completion tokens per second is the sum of server-reported completion tokens divided by the sum of full request durations. It is not decode-only speed or a substitute for llama-bench kernel measurements. If any measured response omits completion token usage, throughput is null in JSON and N/A in the table. Reported zero tokens remain zero. Missing prompt or completion counts remain null in each JSON sample; the command never estimates tokens from text length.

Warmups run separately for each model and do not appear in measurements. They can absorb model loading time, but repeated prompts can also benefit from prompt caching. With --warmup 0, measured requests can include model loading. Other clients and server queueing can affect results; compare under similar load.

Keep hardware, quantization, context size, backend settings, and prompt consistent when comparing engines. Different model tokenizers can report different token counts for the same text, and models can stop before --max-tokens. Temperature zero does not guarantee identical output across models or engines. The JSON settings describe the benchmark requests, not the server’s full model configuration; record that configuration alongside the report.