Text Generation Inference - AI Inference Platforms Tool

Overview

Text Generation Inference (TGI) is an open-source inference server and toolkit from Hugging Face for serving and deploying large language models (LLMs). It implements a high-performance Rust-based runtime with first-class Python and gRPC clients, focusing on production-ready text generation: low-latency streaming, batching, and efficient memory use for large models. The project emphasizes inference optimizations such as tensor parallelism to shard model weights across multiple GPUs, KV-cache support for fast autoregressive sampling, and configurable generation parameters. TGI is designed to work with Hugging Face model formats and the Hub, enabling easy deployment of popular causal models (e.g., LLaMA-style, Mistral, GPT-family forks) and custom checkpoints. The repo documents server configuration, model-loading options, and APIs for synchronous and streaming generation. According to the GitHub repository, the project is actively maintained and intended for both on-premises and cloud deployments, enabling teams to self-host inference with control over scaling and latency characteristics (https://github.com/huggingface/text-generation-inference).

GitHub Statistics

• Stars: 10,720
• Forks: 1,249
• Contributors: 144
• License: Apache-2.0
• Primary Language: Python
• Last Updated: 2026-01-08T14:02:49Z
• Latest Release: v3.3.7

Key Features

• Rust-based high-performance server for production-grade inference
• Python client and gRPC interfaces for easy integration and low-latency calls
• Tensor parallelism to shard large models across multiple GPUs
• Streaming generation and KV-cache to reduce latency for autoregressive calls
• Direct integration with Hugging Face Hub model formats and checkpoints
• Configurable batching, sampling parameters, and model-loading controls

Example Usage

Example (python):

from text_generation import Client

# Connect to a local TGI server (default port 8080)
client = Client("http://localhost:8080")

# Simple generation call
resp = client.generate(
    "Write a short tagline for an eco-friendly coffee shop:",
    max_new_tokens=50,
    temperature=0.8
)

# The client returns a structured response; print text of first completion
print(resp.generations[0][0].text)

# For long-running or streamed responses, the same client supports streaming APIs.

Pricing

Free to self-host; the project is open-source (Apache-2.0) on GitHub. Hosted inference or managed services from third parties may incur charges.

Benchmarks

Supported protocols: gRPC, HTTP/REST, Python client, Rust server APIs (Source: https://github.com/huggingface/text-generation-inference)

Parallelism: Tensor parallelism for multi-GPU sharding and scalable inference (Source: https://github.com/huggingface/text-generation-inference)

License: Open-source (Apache-2.0) (Source: https://github.com/huggingface/text-generation-inference)

Key Information

• Category: Inference Platforms
• Type: AI Inference Platforms Tool