--- title: "vLLM vs TGI: picking the engine that serves your open model" excerpt: "vLLM is the throughput-first inference engine; TGI is Hugging Face's batteries-included server. If you're self-hosting an open model, the choice is real. How to pick." date: "2026-07-08" lastModified: "2026-07-08" author: "Teo Deleanu" authorAvatar: "/team/teo.jpg" tags: ["Production AI", "AI Engineering", "Inference", "Infrastructure"] keywords: - "vLLM vs TGI" - "vLLM vs Text Generation Inference" - "self-hosting LLM inference" - "LLM inference server comparison" options: ["vLLM", "TGI"] verdict: "Pick vLLM when throughput and cost-per-token on your own GPUs are the point, and you can live closer to the metal. Pick TGI when you want a supported, batteries-included server that fits the Hugging Face ecosystem and you value an easy operational on-ramp over squeezing the last tokens per second." featured: false tldr: "vLLM and TGI both serve open-weight models on your own GPUs, but they optimize for different owners. vLLM is a throughput-first engine built around PagedAttention and continuous batching; its reason to exist is packing more concurrent requests onto a GPU. TGI is Hugging Face's production server: batteries included, tight ecosystem fit, and a gentler operational on-ramp. Neither choice matters until you've decided self-hosting is worth it at all — for most teams, a hosted API is cheaper than the GPU ops until real scale or data-residency forces the issue." keyTakeaways: - "vLLM's whole thesis is throughput: PagedAttention plus continuous batching pack more concurrent requests onto the same GPU, which is what actually moves cost-per-token when you self-host." - "TGI trades a little peak throughput for a supported, batteries-included server that slots into the Hugging Face ecosystem with less assembly." - "The real prerequisite question is whether to self-host at all. Below real scale, a hosted API usually beats the fully-loaded cost of running GPUs yourself." - "Both are moving targets. Treat any specific throughput number as stale on arrival and benchmark on your model, your prompt shape, and your GPUs before committing." faqs: - q: "Is vLLM faster than TGI?" a: "vLLM is designed throughput-first around PagedAttention and continuous batching, and that's its reason to exist. But both projects move fast and results depend heavily on your model, prompt shape, and GPU. Benchmark on your own workload rather than trusting a headline number." - q: "Should I self-host inference with vLLM or use a hosted API?" a: "For most teams below real scale, a hosted API beats the fully-loaded cost of running GPUs — you're not just paying for silicon, you're paying for the ops. Self-host when scale economics, data residency, or a specific open model force the issue. Then choose the engine." - q: "vLLM or TGI for production?" a: "vLLM if maximizing throughput and cost-per-token on your own GPUs is the goal and you can operate closer to the metal. TGI if you want a supported, ecosystem-friendly server with an easier operational on-ramp and can accept slightly less peak throughput." --- If you're comparing vLLM and TGI, you've already made the bigger decision: you're self-hosting an open-weight model on your own GPUs. That decision is the one worth pausing on, because it's more consequential than the engine you pick underneath it. We build [production AI systems](/blog/anatomy-of-a-multi-agent-platform) and route most LLM traffic through hosted APIs on purpose. Self-hosting inference is a real lever, but it's a lever you pull for a reason — scale economics, data residency, or a specific open model — not by default. ## The prerequisite question: should you self-host at all Running your own inference isn't just a GPU bill. It's autoscaling, GPU memory management, model loading, batching config, upgrades, and the page when a node OOMs at peak. Below real scale, a hosted API almost always beats the *fully-loaded* cost of doing this yourself, because the API price includes all the ops you'd otherwise staff. Self-host when the math flips: sustained high volume where cost-per-token on owned GPUs undercuts the API, a data-residency requirement that forbids sending prompts out, or an open model you specifically need. Once you're there — and only then — the engine choice becomes real. ## vLLM: throughput is the whole point vLLM exists to pack more concurrent requests onto a GPU. Its core ideas — PagedAttention for memory-efficient KV-cache handling, and continuous batching so new requests join in-flight instead of waiting for a batch to drain — are aimed squarely at throughput and, therefore, cost-per-token. If the reason you're self-hosting is economics at scale, this is the alignment you want: the engine's design goal is your business goal. The trade is that you're closer to the metal. You'll tune batching, watch GPU memory, and own more of the serving stack. ## TGI: batteries included, ecosystem fit TGI (Text Generation Inference) is Hugging Face's production server. It's batteries-included and slots cleanly into the Hugging Face ecosystem, which means a gentler on-ramp: less assembly to get a supported server answering requests. You may leave some peak throughput on the table relative to a fully-tuned vLLM deployment, but you gain an operational path that's easier to stand up and keep supported. For teams whose priority is "get a reliable self-hosted endpoint running without becoming inference-engine experts," that trade is often the right one. ## Don't trust the benchmark you read Both projects move fast, and published throughput numbers go stale between releases. Worse, they don't transfer: results depend on your model, your prompt shape (long context vs short, streaming vs batch), and your specific GPUs. Any comparison — including this one — that hands you a tokens-per-second figure is lying by omission. Benchmark on your own workload before you commit. This is the same discipline as [measuring quality with evals in CI](/blog/evals-as-ci): the only number that matters is the one from your system. ## Which should you pick Pick **vLLM** when throughput and cost-per-token on your own GPUs are the point and you can operate closer to the metal — the classic "we self-host because the scale economics demand it" case. Pick **TGI** when you want a supported, batteries-included server with an easy operational on-ramp and a clean fit with the Hugging Face ecosystem, and you'll accept slightly less peak throughput for that. And keep the [orchestration layer](/compare/airflow-vs-celery-vs-temporal) separate in your head: the engine serves tokens, but what schedules and retries the work around it is a different decision entirely.