vLLM vs TGI: Inference Throughput and Cost per Token Benchmarked
A comparison of the vLLM and Text Generation Inference serving engines, covering batching, memory handling, throughput and latency tradeoffs, and cost per token.
The serving engine you put in front of a model is one of the biggest determinants of inference cost. Two of the most widely used open engines are vLLM and Text Generation Inference, often shortened to TGI. Both turn a model and a GPU into a production inference service, both implement modern techniques to raise throughput, and both directly shape your cost per token. Because cost per token is roughly the GPU cost divided by tokens produced, the engine that squeezes more tokens from the same hardware wins on price. This guide compares the two on the dimensions that matter.
Why the serving engine drives cost
A naive inference loop processes one request at a time and leaves the GPU underused. Modern engines change that with two key ideas. Continuous batching lets new requests join an in flight batch instead of waiting for the current one to finish, keeping the GPU full. Paged attention manages the memory used for attention state in small reusable blocks, which reduces waste and lets the engine fit more concurrent requests. Together these techniques can multiply throughput on the same GPU, which is exactly what lowers cost per token.
vLLM: throughput first
vLLM was designed around high throughput serving. It popularized paged attention and pairs it with aggressive continuous batching, which lets it pack many concurrent requests onto a GPU and sustain high token output under load. For workloads with lots of simultaneous requests, vLLM tends to deliver strong throughput and therefore competitive cost per token. It supports a wide range of models and has become a common default when raw efficiency is the priority.
- Strengths: high throughput under concurrency, efficient memory use, broad model support, strong cost per token at scale.
- Consider: tuning matters, and the best results come from matching configuration to your traffic.
TGI: a production serving toolkit
Text Generation Inference is a serving stack that also implements continuous batching and efficient attention, wrapped in a production oriented package with features that ease deployment and operations. It emphasizes being a complete serving solution, with attention to integration, observability, and a smooth path to running models in production. Its throughput is competitive, and for many teams the surrounding operational features are as important as raw token rate.
- Strengths: production oriented features, solid throughput, good operational ergonomics, broad model support.
- Consider: peak throughput in any given comparison depends heavily on versions, models, and settings.
Throughput, latency, and the tradeoff
Raw throughput is only half the story. There is a tradeoff between throughput and latency. Packing more requests into a batch raises total tokens per second but can increase the time any single user waits. The right balance depends on your use case: a chat product cares about responsiveness and time to first token, while a batch pipeline cares almost entirely about total throughput. Both engines expose controls to tune this balance, and the cost optimal setting differs by workload.
| Dimension | vLLM | TGI |
|---|---|---|
| Primary emphasis | Throughput efficiency | Production serving toolkit |
| Continuous batching | Yes | Yes |
| Efficient attention memory | Paged attention | Efficient attention handling |
| Operational features | Growing | Strong focus |
| Best fit | Maximize tokens per dollar | Integrated production deployment |
How to benchmark fairly
Published comparisons go stale quickly because both engines improve fast, and results swing with the model, the GPU, the sequence lengths, and the concurrency level. The only trustworthy benchmark is one you run on your own workload. A fair test holds these constant across engines:
- Same model, same GPU, same precision and quantization.
- Realistic input and output lengths drawn from your traffic.
- A concurrency level that matches your expected load.
- Both throughput, in tokens per second, and latency, including time to first token.
- Cost per token derived from throughput and the GPU hourly rate.
Memory and the cost of long context
A subtle but important driver of cost is how each engine manages the attention state, sometimes called the key value cache, that grows with every token in a sequence. Long prompts and long outputs consume more of this memory, and when it runs out, the engine must limit how many requests run concurrently, which lowers throughput and raises cost per token. Engines that manage this memory efficiently, by allocating it in small reusable blocks rather than reserving large contiguous chunks, can fit more concurrent requests on the same GPU. If your workload involves long contexts, retrieval augmented prompts, or lengthy generations, pay close attention to how each engine handles this, because it can dominate your effective cost.
Prefix caching and shared context
Many real workloads share a long common prefix across requests, such as a fixed system prompt or a shared document. Both engines have moved toward reusing the computed state for such shared prefixes, so those tokens are processed once rather than for every request. When a large fraction of your input is shared, this feature can cut compute substantially and is worth testing explicitly, since the savings depend heavily on your prompt structure. For retrieval heavy applications with stable instructions, prefix reuse is one of the larger available wins.
Choosing between them
If your overriding goal is the lowest cost per token under high concurrency, vLLM is a natural first choice given its throughput focus. If you value an integrated, production ready serving experience with strong operational features and you can accept very competitive rather than absolutely peak throughput, TGI is a strong fit. Many teams trial both with the same model and traffic, then choose on the combination of measured cost per token, latency under load, and how well the engine fits their deployment workflow.
Operational fit and ecosystem
Throughput is not the only thing that decides total cost. The effort to deploy, monitor, and maintain the engine is a recurring cost in engineering time. Consider how each engine fits your existing stack: how it integrates with your orchestration and autoscaling, what metrics it exposes for observability, how it handles model loading and updates, and how active its community and release cadence are. An engine that is slightly slower but far easier to operate in your environment can be cheaper overall once you count the human hours. Conversely, if you have the expertise to tune aggressively, an engine that rewards tuning with higher throughput pays back that investment in lower per token cost.
Hardware support is another practical filter. Confirm that your chosen engine runs efficiently on the GPUs you actually have or plan to rent, and that it supports the precision and quantization formats you intend to use. An engine that does not accelerate your hardware's low precision modes leaves savings on the table. Because both projects move quickly, re check support when you upgrade GPUs or change models, since capabilities that were missing last quarter may have landed.
vLLM and TGI both exist to do the same job, extract more useful tokens from each GPU so your inference bill falls. vLLM leans into throughput and memory efficiency, while TGI leans into being a complete production serving solution, and both are excellent. Do not trust a leaderboard from last quarter. Benchmark with your own model, traffic, and latency targets, translate throughput into cost per token, and let those numbers pick the engine.