Is vLLM or TGI faster?

vLLM is the throughput leader on most workloads. The PagedAttention KV-cache scheme it pioneered is the reason continuous batching works as well as it does. TGI has closed most of the gap and is competitive on per-request latency, but on tokens/second/$ vLLM still wins typical benchmarks by 5-20%.

Which has better integration with Hugging Face?

TGI, by design — it's the official serving framework for HF Inference Endpoints. Loading any HF Hub model is a one-line command and any HF-published quantization works out of the box. vLLM also loads HF models cleanly but the integration is less first-class.

Can I run vLLM in production?

Yes, and most large open-weight deployments do. Most major model labs publish vLLM-compatible serving recipes for their open releases. The Apache 2.0 license, OpenAI-compatible API, and proven scale make it the default for self-hosted LLM inference.

Which supports more model architectures?

vLLM has the broader and more recent architecture support — every major open-weight release (DeepSeek V4, Qwen 3, Llama 4, Gemma 4) ships day-one vLLM compatibility. TGI tends to lag by 1-3 weeks on brand-new architectures but eventually catches up.

Should I pick TGI if I deploy on Hugging Face Inference Endpoints?

Yes. TGI is the native serving runtime for HF Inference Endpoints, so picking TGI eliminates a layer of abstraction. If you're on AWS / GCP / your own GPUs, vLLM is usually the better default for performance and architecture coverage.

Updated May 6, 2026

vLLM vs TGI (May 2026): Side-by-Side Comparison

TL;DR: vLLM wins for throughput and broadest model coverage (PagedAttention origin). TGI wins for tight Hugging Face integration and the official HF Inference Endpoints runtime.

Spec comparison

Spec	vLLM	TGI
License	Apache 2.0	Apache 2.0 (HFOIL post-2024 settled)
Maintainer	UC Berkeley + community	Hugging Face
Continuous batching	Yes (PagedAttention origin)	Yes
OpenAI-compatible API	Yes (built-in)	Yes (1.4+)
Quantization (FP8/INT8/INT4)	AWQ, GPTQ, FP8, INT8	AWQ, GPTQ, FP8, EETQ
Speculative decoding	Yes	Yes (Medusa)
Tensor parallel	Yes	Yes
Pipeline parallel	Yes	Partial
Multi-LoRA serving	Yes (S-LoRA)	Yes
Best for	Throughput, GPU efficiency	HF integration, simpler ops

Feature matrix

Capability	vLLM	TGI
Continuous batching	✓	✓
PagedAttention KV-cache	✓	~
OpenAI-compatible /v1/chat/completions	✓	✓
Tool / function calling	✓	✓
JSON-schema constrained output	✓	✓
Speculative decoding	✓	✓
Multi-LoRA hot-swap	✓	✓
FP8 / INT8 / INT4 quantization	✓	✓
Tensor parallel	✓	✓
Pipeline parallel	✓	~
Pre-built Docker image	✓	✓
Native HF Hub model loading	✓	✓
Built-in observability metrics	✓	✓
Vision / multimodal models	✓	~
Cloud-managed offering (TGI: HF Inference Endpoints)	✗	✓
Largest community / contrib	✓	~

Cost analysis (Llama 4 70B equivalent)

Setup (4× H100)	vLLM throughput	TGI throughput	$/1M tokens
FP16 baseline	~3,200 tok/s	~2,800 tok/s	~$0.40
FP8 + speculative	~6,800 tok/s	~6,200 tok/s	~$0.20
AWQ INT4	~5,400 tok/s	~5,000 tok/s	~$0.25
Burst (256 concurrent)	~9,500 tok/s	~7,800 tok/s	~$0.14 (vLLM)

Numbers are representative ranges from public 2025-26 benchmarks; your numbers will vary by request shape.

When vLLM wins

vLLM wins for raw throughput and architecture breadth. PagedAttention — vLLM's origin contribution to the field — is still the gold standard for KV-cache management; the throughput edge it gives compounds for any high-concurrency workload. Day-one support for new model architectures (DeepSeek V4, Qwen 3, Llama 4) means you do not wait weeks for compatibility. Multi-LoRA serving with S-LoRA lets you serve dozens of fine-tuned variants on a single GPU pool with negligible overhead — critical for any platform that ships per-customer adapters. The Apache 2.0 license, OpenAI-compatible API surface, and the largest contributor base in the open-source LLM serving space make it the safe default for self-hosted inference at scale. If you are running 10+ GPUs and care about tokens/second/$, vLLM is the right pick.

When TGI wins

TGI wins for teams already inside the Hugging Face ecosystem. It is the native runtime for HF Inference Endpoints — pick TGI and you eliminate one layer of abstraction in production. Quantizations published on HF Hub are guaranteed to load. The Medusa speculative decoding integration is well-tuned and easy to enable. Operational simplicity is the other moat: TGI ships fewer knobs, has a cleaner default config for single-GPU and single-node deployments, and the docs are first-rate. For teams whose primary scale is a handful of GPUs and whose primary concern is "does this just work," TGI is the simpler choice. The HF Inference Endpoints managed offering is also the easiest production path if you want to outsource ops entirely. The performance gap to vLLM has narrowed; for most workloads it is not the deciding factor.

The common combination

Most production teams pick one for a given fleet, not both. The common combination is something like: vLLM on your owned GPU fleet for primary inference, TGI via HF Inference Endpoints for burst capacity or for low-volume specialty models you do not want to operate. Both expose an OpenAI-compatible API, so sticking either behind a router is straightforward. Teams running mixed open-weight + closed-frontier workloads route through a single gateway — see our OpenRouter vs Anthropic Direct comparison. For the routing layer that fronts whatever you self-host, the Swfte router drops in cleanly.

How to choose

If you deploy on HF Inference Endpoints, pick TGI. The native integration removes friction.
If you deploy on owned GPU fleets at any meaningful scale, default to vLLM for throughput.
Benchmark both on your real model + request shape. Public numbers are not a substitute for your traffic.
Decide quantization first — FP8 is usually the sweet spot in May 2026; both support it.
Plan for multi-LoRA day one. Both support it; rebuilding a fleet later is expensive.
Front whichever you pick with an OpenAI-compatible router so the inference framework is swappable.