LLM Text Generation Inference

A Rust and gRPC server for large language models text generation inference.

Features

• Quantization with bitsandbytes
• Dynamic bathing of incoming requests for increased total throughput
• Safetensors weight loading
• 45ms per token generation for BLOOM with 8xA100 80GB

Officially supported models

• BLOOM
• BLOOM-560m

Other models are supported on a best-effort basis using AutoModelForCausalLM.from_pretrained(<model>, torch_dtype=torch.float16, device_map="auto").

Load Tests for BLOOM

See k6/load_test.js

	avg	min	med	max	p(90)	p(95)	RPS
Original code	8.9s	1s	9.12s	16.69s	13.7s	14.26s	5.9
New batching logic	5.44s	959.53ms	5.28s	13.12s	7.78s	8.92s	9.08

Install

make install

Run

BLOOM 560-m

make run-bloom-560m

BLOOM

First you need to download the weights:

make download-bloom

make run-bloom # Requires 8xA100 80GB

You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:

make run-bloom-quantize # Requires 8xA100 40GB

Test

curl 127.0.0.1:3000/generate \
    -v \
    -X POST \
    -d '{"inputs":"Testing API","parameters":{"max_new_tokens":9}}' \
    -H 'Content-Type: application/json'

Develop

make server-dev
make router-dev

TODO:

• Add tests for the server/model logic
• Backport custom CUDA kernels to Transformers
• Install safetensors with pip