AI Infra Bench
AI Infra Bench measures OpenAI-compatible inference endpoints, evaluates model outputs, and finds the highest load that satisfies a service-level objective. It is backend-independent and does not require a serving framework SDK.
Install
pip install ai-infra-bench
Python 3.10 or newer is required.
CLI
Send one request:
aib req --base-url http://127.0.0.1:30000 --prompt "Who are you?"
Run a random-token benchmark:
aib bench \
--base-url http://127.0.0.1:30000 \
--dataset random \
--input-len 1024 \
--output-len 256 \
--num-requests 100 \
--max-concurrency 16
Other commands cover dataset evaluation, logits and hidden-state comparison, metric plotting, and local Prometheus monitoring:
aib --help
SLO Search
SLO searches are configured in YAML. The command probes the configured range
and returns the highest max_concurrency or request_rate that satisfies every
condition:
endpoint:
base_url: http://127.0.0.1:30000
api_key: EMPTY
model: null
request:
payload:
messages:
- role: user
content: hello
benchmark:
num_requests: 100
warmup_requests: 5
max_concurrency: 32
request_rate: inf
search:
parameter: max_concurrency
min: 1
max: 64
conditions:
- metric: success_rate
operator: ">="
value: 0.99
- metric: p99_latency_ms
operator: "<"
value: 3000
Run it with:
aib slo examples/slo.yaml -o slo-result.yaml
Output
SLO runs write a YAML report with every probe, its metrics, and condition results. Request benchmarks can also write machine-readable JSON or JSONL metrics for later visualization.
Development
python -m pip install -e .
pytest
Licensed under the Apache License 2.0.
1 maintainer
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