Serving large language models in production is a different engineering problem from training them. Training optimizes for GPU utilization over hours or days on a fixed batch of data. Serving must handle variable-length requests arriving concurrently, minimize time-to-first-token for interactive applications, maximize tokens-per-second for batch workloads, and do all of this while keeping GPU memory within bounds. The naive approach — load the model, run inference one request at a time — leaves 80–90% of GPU capacity unused. Purpose-built serving frameworks exist to close that gap.
vLLM, HuggingFace’s Text Generation Inference (TGI), and NVIDIA’s Triton Inference Server are the three frameworks that come up most in production LLM deployments. They are not equivalent alternatives — they solve different parts of the problem and make different trade-offs. Understanding what each one actually does is the prerequisite for choosing correctly.
The LLM Serving Problem
LLM inference has two distinct phases with different computational profiles. The prefill phase processes the entire input prompt in parallel, computing attention over all input tokens simultaneously — it is compute-bound and runs efficiently on GPU. The decode phase generates output tokens one at a time, each requiring a forward pass through the full model — it is memory-bandwidth-bound because each token generation reads the entire model weight matrix from GPU memory, which takes time proportional to model size regardless of how much compute the GPU has.
The KV cache compounds this. During decoding, attention needs the key and value projections for every previously generated token. These are cached in GPU memory to avoid recomputation. For long sequences or large batches, the KV cache can consume as much GPU memory as the model weights themselves. Managing KV cache memory efficiently is the central challenge of LLM serving, and it is where the frameworks diverge most significantly.
The other core challenge is batching. Batching multiple requests together amortizes the cost of reading model weights across requests, dramatically improving throughput. But LLM requests have variable input and output lengths — naive static batching (wait for N requests, process them together, wait for all to finish) is inefficient because short requests sit idle waiting for long ones to complete before the batch is released.
vLLM
vLLM, released by UC Berkeley in 2023, introduced PagedAttention — the technique that made it immediately dominant for high-throughput LLM serving. PagedAttention manages the KV cache the way an operating system manages virtual memory: it divides the KV cache into fixed-size pages and allocates them non-contiguously as requests need them. Requests no longer need to reserve a contiguous chunk of GPU memory proportional to their maximum sequence length upfront. Pages are allocated on demand and freed when sequences complete.
The practical consequence is much higher GPU memory utilization and significantly less memory waste. Traditional serving frameworks waste 60–80% of reserved KV cache memory because sequences rarely use their full allocated capacity. vLLM’s paged allocation reduces this waste to under 4% in typical workloads, which translates directly into the ability to run larger batches and achieve higher throughput.
vLLM pairs PagedAttention with continuous batching (also called iteration-level scheduling). Rather than processing a fixed batch until all sequences complete, vLLM’s scheduler adds new requests to the batch at each decoding step as existing requests finish. The GPU is always processing the maximum batch size the memory allows, not waiting for a fixed batch to drain. Combined, PagedAttention and continuous batching give vLLM throughput advantages of 2–4x over naive serving implementations on typical workloads.
vLLM has broad model support — essentially any model in the HuggingFace Transformers library with a supported attention implementation, plus many custom architectures added by the community. It supports AWQ and GPTQ quantization natively, speculative decoding, and multi-GPU tensor parallelism via a straightforward tensor_parallel_size parameter. The OpenAI-compatible REST API it exposes out of the box means existing applications can point at a vLLM server with minimal changes.
vLLM’s weaknesses are real. It is Python-heavy and has meaningful per-request overhead for very short requests where the scheduling machinery costs more than the inference itself. Memory fragmentation under adversarial request patterns can cause the scheduler to pause and defragment, introducing latency spikes. Because PagedAttention requires custom CUDA kernels, adding support for new attention variants requires kernel work, not just Python changes.
Text Generation Inference (TGI)
HuggingFace’s TGI is a Rust-based serving framework optimized for the models HuggingFace supports officially. Where vLLM prioritizes throughput via PagedAttention, TGI prioritizes latency and production reliability. It was the production serving backend for HuggingFace’s Inference API before vLLM’s rise, and it shows in its design: TGI has better support for model sharding across GPUs with tensor parallelism, a more mature health-check and monitoring story, and tighter integration with the HuggingFace Hub for model loading.
TGI also implements continuous batching, added after vLLM demonstrated its value. Its KV cache management is less sophisticated than PagedAttention — it uses a static pre-allocation approach — but for many workloads the difference in practice is smaller than benchmarks suggest, particularly when request length distributions are predictable.
TGI’s Rust core gives it lower per-request latency overhead than vLLM’s Python scheduler for short-context, latency-sensitive applications. The gRPC and HTTP endpoints are well-documented, and token streaming support is reliable and battle-tested. For teams already using the HuggingFace ecosystem, TGI’s integration with Hub authentication, model cards, and deployment tooling reduces operational friction.
The tradeoff is model support breadth. TGI officially supports a curated list of architectures — long and covering all major model families (Llama, Mistral, Falcon, BLOOM, T5, and many others) — but adding an unsupported architecture requires contributing to TGI’s Rust codebase rather than adding a Python config. For teams running standard model families this is not a constraint. For teams running custom architectures or experimental models, it is.
TGI supports Flash Attention 2, GPTQ quantization, and bitsandbytes 8-bit and 4-bit inference. Tensor parallelism works well across 2–8 GPUs on a single node. For very large models requiring multi-node serving, TGI’s support is less mature than vLLM’s.
Triton Inference Server
NVIDIA’s Triton Inference Server is a fundamentally different kind of tool. vLLM and TGI are LLM-specific serving frameworks — they understand transformer architectures, KV caches, and token generation. Triton is a general-purpose model serving platform that can serve any model in any framework: PyTorch, TensorFlow, ONNX, TensorRT, JAX, and custom C++ backends. LLM serving in Triton is built on top of this general infrastructure using the Triton backend for TensorRT-LLM.
TensorRT-LLM is NVIDIA’s optimized LLM inference library that fuses operations, generates architecture-specific CUDA kernels, and applies NVIDIA-specific optimizations like FP8 quantization on Hopper GPUs (H100, H200). Triton combined with TensorRT-LLM is the combination NVIDIA benchmarks cite when claiming peak inference performance numbers. On H100 hardware specifically, the optimized kernels in TensorRT-LLM can outperform vLLM and TGI on raw throughput by 20–40% on standard benchmarks.
The cost is operational complexity. Deploying a model with Triton and TensorRT-LLM requires compiling the model into a TensorRT engine — a process that takes 30 minutes to several hours depending on model size, is hardware-specific (an engine compiled for A100 does not run on H100), and must be repeated when the model is updated. The Triton model repository structure, ensemble pipelines for preprocessing and postprocessing, and gRPC client setup have a steep learning curve compared to vLLM’s single Python command.
Triton makes sense in a specific context: you are running on NVIDIA’s latest hardware, you need maximum throughput, your model is stable, and you have engineering capacity to manage TensorRT compilation and Triton’s deployment model. In that context, the performance advantage justifies the complexity. For most teams, it does not.
Head-to-Head Comparison
Throughput: For sustained batch inference on standard hardware (A100, A10G), vLLM and TGI are competitive, with vLLM typically ahead by 10–30% on mixed-length workloads due to PagedAttention’s memory efficiency. Triton combined with TensorRT-LLM leads on H100 hardware with optimized compilation, sometimes by 30–50% on throughput benchmarks. For interactive workloads with small batch sizes, the differences narrow — all three can saturate a single GPU’s capability for real-time generation.
Latency: TGI has lower overhead per request than vLLM for short-context, latency-sensitive serving due to its Rust core. vLLM’s Python scheduling adds 1–5ms of overhead per request, which is negligible for most LLM response times but measurable in high-frequency API serving. Triton has very low latency overhead but TensorRT engine compilation means cold-start times of minutes.
Ease of deployment: vLLM is the easiest — pip install and one Python command gives you an OpenAI-compatible endpoint. TGI requires Docker and slightly more configuration but has good documentation and a mature deployment story. Triton requires TensorRT engine compilation, model repository setup, and understanding of Triton’s ensemble and backend model. For getting something running in under an hour, vLLM wins clearly.
Model support: vLLM has the broadest community-supported model list and adds new architectures quickly. TGI officially supports fewer architectures but covers all major production families. Triton combined with TensorRT-LLM supports the architectures NVIDIA has written optimized kernels for — Llama, Mistral, GPT-J, Falcon, and others — but lags on community models.
Quantization: vLLM supports AWQ, GPTQ, and FP8 on compatible hardware. TGI supports GPTQ and bitsandbytes. Triton combined with TensorRT-LLM supports FP8 on H100 natively — a meaningful advantage on Hopper hardware since FP8 provides near-INT8 memory efficiency with better quality than INT4 methods.
Multi-GPU and Multi-Node Serving
For models that do not fit on a single GPU, tensor parallelism splits the model across multiple GPUs. All three frameworks support this. vLLM’s tensor_parallel_size parameter handles single-node multi-GPU transparently. For multi-node serving — distributing a model across multiple machines — vLLM uses Ray for distributed coordination, which adds operational complexity but works reliably for models up to 405B parameters. TGI uses its own tensor parallel implementation and works well across 2–8 GPUs on a single node; multi-node TGI is less commonly deployed in production. Triton delegates to TensorRT-LLM for model parallelism, which supports both tensor and pipeline parallelism and is the most mature option for very large multi-node deployments.
Pipeline parallelism — splitting model layers across GPUs rather than splitting each layer — can improve throughput for extremely large models by reducing inter-GPU communication volume. Only TensorRT-LLM has robust pipeline parallelism support. For 70B and smaller models on A100 or H100 hardware, tensor parallelism is sufficient and simpler to operate.
The Decision Framework
Use vLLM when you want the best throughput-to-operational-complexity ratio. It is the right default choice for the majority of LLM serving use cases: standard model families, mixed request workloads, teams without dedicated ML infrastructure engineers. The PagedAttention advantage is real and the OpenAI-compatible API makes integration straightforward. If you are not sure which to use, start here.
Use TGI when you are already in the HuggingFace ecosystem and want tighter Hub integration, when latency per request matters more than throughput and the lower Rust overhead is worth it, or when TGI’s more mature production monitoring and health-check story is important to your operations team.
Use Triton combined with TensorRT-LLM when you are running on H100 or newer NVIDIA hardware, need maximum throughput, have a stable model that will not change frequently, and have engineering capacity to manage TensorRT compilation. Also consider it if you are serving multiple model types from the same infrastructure — vision models, embedding models, and LLMs — since Triton’s general-purpose nature makes it efficient to co-host them without running separate serving stacks.
One practical note: vLLM and TGI are not mutually exclusive across your infrastructure. Many production setups use vLLM for high-throughput batch inference pipelines and TGI for latency-sensitive interactive endpoints, with the same model weights loaded in both. Match the framework to the latency and throughput requirements of the specific workload it is serving.