vLLM vs TGI: which self-hosted inference server should you deploy?

Quick verdict

Choose vLLM when you want an Apache 2.0 licensed inference server you can deploy anywhere — bare metal, VM, or container — with minimal setup. Choose TGI when your team is already Hugging Face-native, you are deploying via container orchestration, and you want first-class Hub model integration including gated model access. Review the TGI license carefully before using it in a managed inference product.

Comparison matrix

PagedAttention: what vLLM does differently

The core vLLM architectural innovation is PagedAttention — a KV cache memory management system modeled on OS virtual memory paging. Traditional inference servers allocate contiguous blocks of GPU memory per request, which leads to fragmentation and limits the number of concurrent requests that can be batched. PagedAttention allows the KV cache to be stored in non-contiguous memory blocks and reallocated dynamically across requests.

In practice this matters most under two conditions: long-context requests where the KV cache per request is large, and high-concurrency workloads where many requests compete for the same GPU memory pool. Under heavy load, vLLM memory efficiency translates directly into higher sustainable throughput before CUDA out-of-memory errors occur.

TGI: when the Hugging Face ecosystem is the deciding factor

TGI is the inference engine that Hugging Face operates its own Inference API and Inference Endpoints on. This tight ecosystem integration means TGI gets first-class support for gated HF Hub models, token streaming via Server-Sent Events, and Hub-specific authentication flows. If your team already uses the HF Hub as the canonical model registry, TGI model loading, tokenizer resolution, and model card metadata integration will feel native.

TGI Docker-first deployment model is an advantage in container-native environments — Kubernetes, ECS, Cloud Run — where standardized container images and Helm charts reduce operational overhead. It becomes a minor friction point for teams deploying directly to bare-metal servers or simple VMs where Docker adds an unnecessary abstraction layer.

Licensing: the critical difference for commercial deployments

The vLLM Apache 2.0 license imposes no commercial restrictions. You can run it as a managed inference API, embed it in a SaaS product, or distribute it as part of an on-prem enterprise bundle without any licensing agreement.

TGI uses the Hugging Face Optimized Inference License. It permits self-hosting and research but restricts using TGI itself as the engine to offer inference-as-a-service commercially without entering a Hugging Face agreement. If you are building a product where LLM inference is a billable service you provide to customers — even internally across business units — review the TGI license with legal before deploying it as the serving layer.

Choose vLLM when

• You want a permissively licensed inference server with no commercial use restrictions.
• You are deploying on bare metal, VMs, or any environment where pip install is simpler than Docker.
• Your workload involves long-context requests or very high concurrency where PagedAttention memory management delivers measurable throughput gains.
• You need FP8 quantization for NVIDIA Hopper GPUs (H100, H200) or advanced speculative decoding.
• You are building a managed LLM inference product and need Apache 2.0 freedom to redistribute or offer as a service.

Choose TGI when

• Your team is already Hugging Face-native and uses the Hub as your model registry with gated model access.
• You deploy via Docker, Kubernetes, or a container-native cloud and want a standardized, production-tested container image.
• You need ExllamaV2 quantization format support, which TGI includes and vLLM does not.
• You are deploying to Hugging Face Inference Endpoints, where TGI is the backend engine.
• You are not building a managed inference product and the license restrictions are therefore not a blocker.

Production deployment checklist

1. Confirm your GPU VRAM can hold the model at your target quantization — 7B at Q4_K_M needs ~4 GB; 70B at Q4_K_M needs ~40 GB minimum.
2. Benchmark both servers with your actual traffic pattern: request concurrency, prompt length distribution, and output token volume matter more than synthetic throughput numbers.
3. For vLLM: set --max-model-len and --gpu-memory-utilization conservatively on first deploy to avoid OOM crashes under burst traffic.
4. For TGI: review the license file before adding it to a commercial product; confirm the specific model license is compatible with your use case.
5. Set up Prometheus metrics scraping on both servers; both expose /metrics endpoints with request queue depth, latency percentiles, and token throughput.
6. Test autoscaling under load before production — inference servers are stateful and scale-down requires draining in-flight requests cleanly.

Check your hardware first

Before choosing an inference server architecture, confirm your GPU VRAM and system RAM can support your target model and quantization level. Throughput comparisons between vLLM and TGI are meaningless if the hardware cannot hold the model without swapping.

Limitations of this comparison

• Both projects release updates frequently — quantization support, multi-GPU strategies, and latency characteristics can change across versions.
• Published benchmarks often test peak throughput, not the tail latency and queue behavior that dominate real production cost profiles.
• Hardware generation matters significantly: H100 (FP8 native) vs A100 vs consumer RTX GPUs produce very different throughput curves on the same model and server.
• License terms can change. Verify the current license in the project repository before deployment.

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