Which is fastest: vLLM, TGI, or TensorRT-LLM?

TensorRT-LLM is fastest on NVIDIA GPUs, delivering 10-20% higher throughput than vLLM. However, vLLM achieves 85% of the performance with significantly less operational complexity. TGI has fallen behind since March 2026 and now sits in maintenance mode for new features.

Which has the simplest setup?

vLLM has the simplest setup in 2026: one Docker container, one model flag, one OpenAI-compatible endpoint. TGI is similar but requires more configuration for advanced features. TensorRT-LLM requires a model compilation step that takes 20-60 minutes per model.

Can I use any of these with a local Ollama or Mullama daemon?

No. vLLM, TGI, and TensorRT-LLM are production serving stacks, not consumer tools. They use safetensors and require GPUs with significant VRAM (typically 24GB+). For local desktop or hobbyist use, Ollama or Mullama is a better fit.

What about hardware support?

vLLM supports NVIDIA, AMD (ROCm), and Google TPU. TGI supports NVIDIA and AMD. TensorRT-LLM is NVIDIA-only.

Which is best for a startup serving <100 concurrent users?

vLLM. It has the best balance of performance and operational simplicity for the <100 GPU range. TensorRT-LLM is overkill until you have 50+ GPUs and dedicated ML infrastructure engineers.

vLLM vs TGI vs TensorRT-LLM: Production LLM Serving in 2026

Q: Which is best for a startup serving <100 concurrent users?

vLLM. It has the best balance of performance and operational simplicity for the <100 GPU range. TensorRT-LLM is overkill until you have 50+ GPUs and dedicated ML infrastructure engineers.

Disclosure: Some tools on this page are built by our team (Cognisoc). We hold our own tools to the same tests as everyone else's, and a customer's acceptance criteria outrank any wish to recommend them. Editorial policy.

vLLM, TGI (Text Generation Inference), and TensorRT-LLM are the three production-grade options for serving LLMs at scale in 2026. This comparison helps you pick the right one based on your throughput needs, hardware, and operational capacity.

Overview

	vLLM	TGI	TensorRT-LLM
Language	Python	Rust	Python + C++
First release	2023	2022	2023
Backed by	UC Berkeley + community	Hugging Face	NVIDIA
Model format	safetensors	safetensors	TensorRT engine
Continuous batching	✓	✓	✓
PagedAttention	✓ (introduced)	✓	✓
Speculative decoding	✓	✓	✓
Multi-GPU	✓	✓	✓ (best)
Multi-node	✓	✓	✓ (best)
Hardware	NVIDIA, AMD, TPU	NVIDIA, AMD	NVIDIA only
OpenAI-compatible API	✓	✓	✓
License	Apache 2.0	Apache 2.0	Apache 2.0

Throughput

On NVIDIA H100 80GB, serving Llama 3.1 70B at batch 32:

Runtime	Tokens/sec/GPU	Notes
TensorRT-LLM	4,200	Reference implementation, requires 60-min model compilation
vLLM	3,500	~83% of TensorRT-LLM, no compilation
TGI	2,800	Maintenance mode since March 2026

On consumer GPUs (RTX 4090, 24GB), the gap is smaller and vLLM is essentially at parity with TensorRT-LLM for sub-7B models.

Latency

Time-to-first-token (TTFT) for a single request:

Runtime	TTFT (ms)	Notes
vLLM	80-150	Best cold-start, no compilation
TGI	100-180	Similar to vLLM
TensorRT-LLM	50-120	Best warm latency due to compiled graph

Operational complexity

	vLLM	TGI	TensorRT-LLM
Container image	✓	✓	✓
Model compilation	✗	✗	required (20-60 min)
Config file size	small	medium	large
Deployment to k8s	straightforward	straightforward	needs GPU operator
Monitoring	Prometheus	Prometheus	custom
Documentation quality	excellent	good	good but dense
Community size	largest	medium	NVIDIA-only

When to use each

Choose vLLM when…

You are deploying a new LLM service and want the best balance of performance, simplicity, and ecosystem support.
You want the largest community of users who can help debug issues.
Your hardware is NVIDIA, AMD, or TPU.
You do not have a dedicated ML infrastructure team but you need to serve at scale.

Choose TGI when…

You are already on the Hugging Face ecosystem (Hub, Inference Endpoints, etc.) and want the simplest integration.
You need specific Hugging Face features like text-generation-inference’s quantization or token streaming behavior.
You are running on NVIDIA or AMD hardware.
Caveat: TGI entered maintenance mode in March 2026. New features are landing slowly. Plan for vLLM as your long-term option.

Choose TensorRT-LLM when…

You have dedicated ML infrastructure engineers and need the maximum throughput per GPU.
You are running on NVIDIA only and willing to invest in the compilation pipeline.
You have a stable model set (the compilation overhead only pays off if you run the same model for weeks).
You are running on Hopper or Blackwell generation hardware where TensorRT-LLM’s compiled kernels extract the most performance.

Setup examples

vLLM

docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest \
  --model meta-llama/Llama-3.1-70B-Instruct \
  --tensor-parallel-size 4

TGI

docker run --gpus all -p 8080:80 \
  -v $PWD/data:/data \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id meta-llama/Llama-3.1-70B-Instruct \
  --num-shard 4

TensorRT-LLM

# 1. Convert model (one-time, 20-60 min)
trtllm-build --checkpoint_dir ./llama-70b-checkpoint \
  --output_dir ./engine \
  --max_batch_size 32

# 2. Serve
trtllm-serve ./engine --port 8000

Decision matrix

Your situation	Use
New production LLM service in 2026	vLLM
Consumer / desktop / laptop	Ollama or Mullama (not these three)
NVIDIA only, dedicated ML infra team, max throughput	TensorRT-LLM
Already on Hugging Face ecosystem, OK with maintenance mode	TGI
AMD GPUs	vLLM
TPU	vLLM
<100 concurrent users	vLLM
100-1000 concurrent users	vLLM or TensorRT-LLM
1000+ concurrent users	TensorRT-LLM (with vLLM as fallback)
Compiling a new model every day	vLLM (no compilation)

Concurrency bands are rough guides, not capacity figures. The right engine and the number of GPUs depend on model size, quantization, context length, and your latency targets.