vLLM vs TGI: LLM Serving Framework Comparison 2026

vLLM vs TGI is the first decision most teams hit when moving an LLM from a notebook to production — and the wrong choice costs you real money at AWS us-east-1 GPU rates.

Both frameworks serve transformer models over an OpenAI-compatible HTTP API. Both support continuous batching, tensor parallelism, and quantization. The difference is where each one wins — and those differences matter when you're paying $3.00–$32.77/hr for A10G to H100 instances.

What you'll learn:

• How vLLM and TGI differ architecturally and operationally
• Which framework wins on throughput, latency, and model support
• Concrete Docker deployment commands for each
• When to switch — and the exact config flags that matter

Time: 12 min | Difficulty: Intermediate

vLLM vs TGI: TL;DR

Choose vLLM if: You need maximum tokens/second throughput, multi-LoRA serving, or support for a newer model architecture not yet in TGI.

Choose TGI if: You're pulling gated models from Hugging Face Hub, want HF's built-in auth and safety layers, or your team already runs HF Inference Endpoints.

What We're Comparing

This comparison covers the self-hosted deployment path — running your own inference server on bare-metal or cloud VMs (AWS, GCP, Lambda Labs). Both frameworks expose an OpenAI-compatible /v1/completions and /v1/chat/completions endpoint, so your application code is portable between them.

Test environment: NVIDIA A100 80GB SXM, CUDA 12.3, Ubuntu 22.04, Llama 3.1 8B and 70B.

vLLM Overview

vLLM, released by the Sky Computing Lab at UC Berkeley in 2023, introduced PagedAttention — a memory management technique that treats the KV cache like virtual memory pages. This eliminates KV cache fragmentation, which is the main reason naive serving wastes 40–60% of GPU memory.

Left: vLLM's PagedAttention maps KV cache into non-contiguous pages, maximizing GPU memory use. Right: TGI's continuous batching queues requests into a dynamic token budget.

Strengths

• Highest throughput — PagedAttention means more requests fit in VRAM simultaneously. In most public benchmarks, vLLM processes 2–4× more tokens/second than a naive server on the same hardware.
• Multi-LoRA serving — Load dozens of LoRA adapters at once with --enable-lora. Each request can target a different adapter. TGI requires a full server restart to swap.
• Broad architecture support — Llama, Mistral, Qwen, Gemma, Phi, Falcon, DeepSeek, Mixtral MoE, and 300+ others. New models typically land in vLLM within days of release.
• FP8 quantization — Native FP8 on H100s cuts memory in half with near-zero accuracy loss. TGI does not yet support FP8 natively.

Weaknesses

• Memory footprint — PagedAttention pre-allocates all available VRAM by default. On a shared machine, this starves other processes. Tune with --gpu-memory-utilization 0.85.
• Cold-start time — vLLM compiles CUDA graphs on first run. Expect 2–5 minutes before the first request is served, depending on model size.
• No built-in model auth — vLLM does not natively handle HF token auth for gated models. You must pass --hf-token or pre-download the weights.

vLLM Docker — Quick Start

# Llama 3.1 8B, single A100, OpenAI-compatible endpoint
docker run --runtime nvidia --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 8000:8000 \
  --ipc=host \
  vllm/vllm-openai:latest \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --tensor-parallel-size 1 \         # increase for multi-GPU
  --gpu-memory-utilization 0.90 \    # leave 10% for CUDA overhead
  --max-model-len 8192 \             # cap context to control KV cache size
  --enable-lora                      # optional: multi-LoRA support

Test it:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Llama-3.1-8B-Instruct",
    "messages": [{"role": "user", "content": "Explain PagedAttention in one sentence."}],
    "max_tokens": 128
  }'

Expected output: JSON response in <1s on A100. First-request latency is higher due to CUDA graph capture.

TGI (Text Generation Inference) Overview

Text Generation Inference is Hugging Face's production inference server, written in Rust (HTTP layer) with Python/PyTorch backends. It was built to power api-inference.huggingface.co and HF Inference Endpoints — so it's been battle-tested at scale.

TGI's key differentiator is deep Hugging Face Hub integration. It handles token auth for gated models, respects Hub model cards, and natively supports HF's safety classifiers and watermarking tools.

Strengths

• HF Hub native — Pass a model ID and your HF_TOKEN and TGI pulls weights, tokenizer config, and generation config automatically. No manual weight management.
• Prefill/decode disaggregation — In newer TGI versions, prefill and decode phases can run on separate GPU pools, reducing time-to-first-token under heavy load.
• Speculative decoding — TGI supports draft-model speculative decoding out of the box with --speculate N. This cuts latency 30–50% for greedy/low-temperature workloads.
• Structured output — Native JSON schema enforcement via --grammar-constrained-decoding. vLLM supports this too, but TGI's implementation is more stable for complex schemas.
• Smaller attack surface — The Rust HTTP layer handles auth, rate limiting, and payload validation before any Python code runs.

Weaknesses

• Narrower model support — TGI maintains its own model implementation list. Newer or less popular architectures (some Qwen variants, smaller research models) may not be supported. Check the TGI supported models list before committing.
• Single LoRA only — TGI supports one LoRA adapter per running server. Multi-tenant LoRA serving requires multiple containers.
• No FP8 native — bitsandbytes NF4 and GPTQ/AWQ are available, but FP8 (H100 native) is not yet supported.

TGI Docker — Quick Start

# Llama 3.1 8B, single GPU, with HF token for gated model access
docker run --gpus all \
  -e HF_TOKEN=hf_your_token_here \
  -p 8080:80 \
  -v ~/.cache/huggingface:/data \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id meta-llama/Llama-3.1-8B-Instruct \
  --num-shard 1 \                    # tensor parallel degree
  --max-input-length 4096 \
  --max-total-tokens 8192 \
  --quantize bitsandbytes-nf4        # optional: 4-bit for VRAM reduction

Test it:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tgi",
    "messages": [{"role": "user", "content": "Explain continuous batching in one sentence."}],
    "max_tokens": 128
  }'

Head-to-Head: Throughput, Latency, and DX

Throughput (tokens/second)

Measured with 100 concurrent requests, prompt length 512 tokens, output length 256 tokens, Llama 3.1 8B on A100 80GB:

vLLM wins on throughput — PagedAttention plus automatic prefix caching (--enable-prefix-caching) keeps GPU utilization higher under concurrent load.

Latency (time-to-first-token)

At low concurrency (1–4 requests), TGI is competitive and sometimes faster due to its speculative decoding path. At high concurrency (50+ requests), vLLM's superior memory management wins.

Conclusion: For chatbots and interactive UIs where users expect fast first tokens at low concurrency — TGI is competitive. For batch inference APIs or high-concurrency services — vLLM is the clear choice.

Developer Experience

Multi-GPU: Tensor Parallelism

Both frameworks support tensor parallelism for models that don't fit on a single GPU.

vLLM — 4-GPU setup (Llama 3.1 70B):

docker run --runtime nvidia --gpus all \
  -p 8000:8000 --ipc=host \
  vllm/vllm-openai:latest \
  --model meta-llama/Llama-3.1-70B-Instruct \
  --tensor-parallel-size 4 \         # spreads across 4 GPUs automatically
  --gpu-memory-utilization 0.92

TGI — 4-GPU setup (Llama 3.1 70B):

docker run --gpus all -p 8080:80 \
  -e HF_TOKEN=hf_your_token_here \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id meta-llama/Llama-3.1-70B-Instruct \
  --num-shard 4                      # TGI's tensor parallel flag

Both approaches are equivalent in complexity. vLLM's --tensor-parallel-size and TGI's --num-shard do the same job — split the model weights across N GPUs.

Cloud Pricing Context (AWS us-east-1, March 2026)

On H100 instances, vLLM's FP8 support gives you an effective 2× capacity advantage over TGI — the H100's FP8 tensor cores are underutilized in TGI's current release.

Which Should You Use?

Pick vLLM when:

• Throughput is your primary metric (batch APIs, embeddings pipelines, high-concurrency chat)
• You need multi-LoRA: serving multiple fine-tuned variants from one GPU pool
• Your model is new or unusual — if it's on Hugging Face Hub, vLLM likely supports it
• You're running H100s and want FP8 efficiency

Pick TGI when:

• You're deploying gated HF models (Llama, Gemma, Phi) and want zero-friction auth
• You need speculative decoding to minimize latency for a single tenant
• Your team is already using HF Inference Endpoints and wants a consistent API
• You need structured JSON output with complex grammars — TGI's grammar constraints are more battle-tested

Neither is wrong. The OpenAI-compatible API means you can switch without changing your application code — just update the base_url in your client.

FAQ

Q: Can I run vLLM and TGI on consumer GPUs like RTX 4090? A: Yes. Both run on any CUDA-capable GPU with 16GB+ VRAM. For a 7B model in INT4, an RTX 4090 (24GB) works well. Use --quantize bitsandbytes-nf4 in TGI or --quantization awq in vLLM to fit larger models.

Q: Does vLLM support Hugging Face gated models? A: Yes — pass --hf-token hf_your_token at startup or set HF_TOKEN as an environment variable. vLLM will authenticate to the Hub during weight download.

Q: What is the minimum VRAM for Llama 3.1 70B? A: In BF16, 70B requires ~140GB VRAM — four A100 80GB or two H100 80GB GPUs. In INT4 (AWQ/GPTQ), it fits on four A10G 24GB GPUs (~96GB total), which costs ~$5.67/hr on AWS.

Q: Can vLLM or TGI serve embedding models alongside LLMs? A: vLLM added embedding model support in v0.4+. Run a separate vLLM instance with your embedding model (e.g. intfloat/e5-mistral-7b-instruct) on the same host, different port. TGI is generation-only and does not serve embedding models.

Q: Does switching from TGI to vLLM require application code changes? A: No. Both expose identical OpenAI-compatible endpoints. Update base_url in your OpenAI SDK client and the rest of your code is unchanged.

Tested on vLLM v0.6.x and TGI v2.4.x, CUDA 12.3, Ubuntu 22.04, A100 80GB SXM