TGI excels in deploying and serving large language models with high-performance optimizations like Tensor Parallelism and distributed tracing, catered to production use cases. Beam offers a streamlined developer experience with ultrafast boot times and instant autoscaling, ideal for teams needing rapid experimentation and scalability.
Best for
TGI is the better choice when production deployment of LLMs is required, especially for enterprises needing robust features like distributed tracing and continuous batching.
Best for
Beam is the better choice when small teams or startups need quick scalability and ease of use for running experiments and training models with serverless infrastructure.
Key Differences
Verdict
TGI is ideal for larger organizations looking to deploy and manage LLMs in a reliable production environment with comprehensive support for tracing and metrics. Beam suits smaller, agile teams needing fast, scalable infrastructure to accelerate their AI projects. Choose TGI for stability and Beam for speed and flexibility.
TGI
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
text-generation-inference documentation and get access to the augmented documentation experience text-generation-inference is now in maintenance mode. Going forward, we will accept pull requests for minor bug fixes, documentation improvements and lightweight maintenance tasks. Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5. Text Generation Inference implements many optimizations and features, such as: Text Generation Inference is used in production by multiple projects, such as:
Beam
Run sandboxes, inference, and training with ultrafast boot times, instant autoscaling, and a developer experience that just works.
Run sandboxes, inference, and training with ultrafast boot times, instant autoscaling, and a developer experience that just works.
TGI
-67% vs last weekBeam
-50% vs last weekTGI
Beam
TGI
Beam
TGI
Beam
Only in TGI (9)
TGI
Beam
TGI
Beam
TGI
llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "gg
llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "ggml-org-gemma-4-26b-a4b-gguf" \ --custom-api-key "llama.cpp" \ --secret-input-mode plaintext \
Beam
TGI
Beam
TGI is better suited for high-performance model serving, especially with optimizations like Tensor Parallelism.
TGI uses a tiered pricing model, which may offer more options for customization than Beam's straightforward approach.
TGI likely has stronger community support due to its larger company size and open-source focus.
While not explicitly designed to work together, TGI and Beam can be used in complementary roles within a broader AI infrastructure.
Beam is generally easier to get started with due to its serverless architecture and developer-friendly features.
