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https://github.com/fixie-ai/ai-benchmarks?tab=readme-ov-file
Benchmarking suite for popular AI APIs
https://github.com/fixie-ai/ai-benchmarks?tab=readme-ov-file
Last synced: about 1 month ago
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Benchmarking suite for popular AI APIs
- Host: GitHub
- URL: https://github.com/fixie-ai/ai-benchmarks?tab=readme-ov-file
- Owner: fixie-ai
- License: mit
- Created: 2023-09-29T21:39:53.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-30T02:32:56.000Z (about 1 month ago)
- Last Synced: 2024-10-30T05:06:20.913Z (about 1 month ago)
- Language: Python
- Size: 12 MB
- Stars: 71
- Watchers: 3
- Forks: 13
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-foundation-model-leaderboards - ai-benchmarks - benchmarks contains a handful of evaluation results for the response latency of popular AI services. | (Model Ranking / Text)
README
# ai-benchmarks
This repo contains a handful of utilities for benchmarking the response latency of popular AI services, including:
Large Language Models (LLMs):
- OpenAI GPT-3.5, GPT-4 (from OpenAI or Azure OpenAI service)
- Anthropic Claude 3, Claude 2, Claude Instant
- Google Gemini Pro and PaLM 2 Bison
- Llama2 and 3 from several different providers, including
- Anyscale
- Azure
- Cerebras
- Cloudflare
- Groq
- OctoAI
- OVHcloud
- Perplexity
- Together
- Mixtral 8x7B from several different providers, including
- Anyscale
- Azure
- Groq
- OctoAI
- OVHcloud
- Perplexity
Embedding Models:
- Ada-002
- Cohere
Text-to-Speech Models (TTS):
- ElevenLabs
- PlayHT
## Leaderboard
See [thefastest.ai](https://thefastest.ai) for the current leaderboard.
### Test methodology
- Tests are run from a set of distributed benchmark runners.
- Input requests are relatively brief, typically about 1000 tokens, and ask for a brief output response.
- Max output tokens is set to 20, to avoid distortion of TPS values from long outputs.
- A warmup connection is made to remove any connection setup latency.
- The TTFT clock starts when the HTTP request is made and stops when the first token result is received in the response stream.
- For each provider, three separate inferences are done, and the best result is kept (to remove any outliers due to queuing etc).
## Initial setup
This repo uses [Poetry](https://python-poetry.org/) for dependency management. To install the dependencies, run:
```
pip install poetry
poetry install --sync
```
## Running benchmarks
To run a benchmark, first set the appropriate environment variable (e.g., OPENAI_API_KEY, ELEVEN_API_KEY) etc, and then run
the appropriate benchmark script.
### LLM benchmarks
To generate LLM benchmarks, use the `llm_benchmark.py` script. For most providers, you can just pass the model name and the script will figure out what API endpoint to invoke. e.g.,
```
poetry run python llm_benchmark.py -m gpt-3.5-turbo "Write me a haiku."
```
However, when invoking generic models like Llama2, you'll need to pass in the base_url and api_key via the -b and -k parameters, e.g.,
```
poetry run python llm_benchmark.py -k $OCTOML_API_KEY -b https://text.octoai.run/v1 \
-m llama-2-70b-chat-fp16 "Write me a haiku."
```
Similarly, when invoking Azure OpenAI, you'll need to specify your Azure API key and the base URL of your Azure deployment, e.g.,
```
poetry run python llm_benchmark.py -b https://fixie-westus.openai.azure.com \
-m gpt-4-1106-preview "Write me a haiku."
```
See [this script](https://github.com/fixie-ai/ai-benchmarks/blob/main/llm_benchmark_suite.sh) for more examples of how to invoke various providers.
#### Options
```
usage: llm_benchmark.py [-h] [--model MODEL] [--temperature TEMPERATURE] [--max-tokens MAX_TOKENS] [--base-url BASE_URL]
[--api-key API_KEY] [--no-warmup] [--num-requests NUM_REQUESTS] [--print] [--verbose]
[prompt]
positional arguments:
prompt Prompt to send to the API
optional arguments:
-h, --help show this help message and exit
--model MODEL, -m MODEL Model to benchmark
--temperature TEMPERATURE, -t TEMPERATURE Temperature for the response
--max-tokens, -T MAX_TOKEN Max tokens for the response
--base-url BASE_URL, -b BASE_URL Base URL for the LLM API endpoint
--api-key API_KEY, -k API_KEY API key for the LLM API endpoint
--no-warmup Don't do a warmup call to the API
--num-requests NUM_REQUESTS, -n NUM_REQUESTS Number of requests to make
--print, -p Print the response
--verbose, -v Print verbose output
```
#### Output
By default a summary of the requests is printed:
```
Latency saved: 0.01 seconds <---- Difference between first response time and fastest reponse time
Optimized response time: 0.14 seconds <---- fastest(http_response_time - http_start_time) of N requests
Median response time: 0.15 seconds <---- median(http_response_time - http_start_time) of N requests
Time to first token: 0.34 seconds <---- first_token_time - http_start_time
Tokens: 147 (211 tokens/sec) <---- num_generated_tokens / (last_token_time - first_token_time)
Total time: 1.03 seconds <---- last_token_time - http_start_time
```
You can specify -p to print the output of the LLM, or -v to see detailed timing for each request.
### TTS benchmarks
To generate TTS benchmarks, there are various scripts for the individual providers, e.g.,
```
python elevenlabs_stream_benchmark.py "Haikus I find tricky, With a 5-7-5 count, But I'll give it a go"
```
#### Playing audio
By default, only timing information for TTS is emitted. Follow the steps below to actually play out the received audio.
First, install `mpv` via
```
brew install mpv
```
Then, just pass the -p argument when generating text, e.g.,
```
python playht_benchmark.py -p "Well, basically I have intuition."
```
You can use the -v parameter to select which voice to use for generation.