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vLLM vs TGI vs TensorRT-LLM: Production LLM Serving in 2026
vLLM vs TGI (Text Generation Inference) vs TensorRT-LLM — three production-grade LLM serving stacks compared on throughput, latency, ease of deployment, hardware support, and feature set.
Published June 29, 2026 · Updated June 29, 2026 · By Local LLM Team
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) |
See also
Frequently Asked Questions
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.