vLLM vs SGLang vs TensorRT-LLM: Serving Comparison

vLLM vs SGLang vs TensorRT-LLM: direct winner by use case

Evaluating a vLLM vs SGLang vs TensorRT-LLM comparison requires matching your workload pattern, deployment architecture, and hardware infrastructure to the strengths of each framework.^{[source][source][source]} No single open-source inference engine fits every production deployment.

Choose vLLM as the default production choice for broad hardware portability, quick setup, and high-throughput multi-model serving.^[source] Its PagedAttention memory manager eliminates Key-Value (KV) cache fragmentation while supporting NVIDIA GPUs, AMD Instinct accelerators, and Google TPUs without requiring build steps.^[source]

Choose SGLang when building high-concurrency LLM pipelines, AI agent frameworks, or long-context applications with repeated system prompts.^[source] SGLang uses RadixAttention to automatically retain and reuse prefix cache sub-trees across multi-turn requests, alongside optimized FlashInfer backends for specialized architectures like DeepSeek Multi-Head Latent Attention (MLA).^[source]

Choose TensorRT-LLM when deploying stable, fixed model weights on dedicated NVIDIA GPU clusters where squeezing maximum hardware throughput and minimum latency justifies ahead-of-time (AOT) engine compilation.^[source] TensorRT-LLM compiles model architectures into hardware-specific CUDA graph engines with custom fused kernels, low-precision FP8 or FP4 execution, and native NVIDIA C++ runtime integration.^[source]

Side-by-side framework comparison

The table below outlines core architectural specifications, memory allocation mechanisms, compilation requirements, and hardware support across all three inference engines.

Memory management architecture: PagedAttention vs RadixAttention vs Paged KV

Managing GPU memory efficiently during autoregressive decoding is the single most important factor for high-throughput LLM serving. In traditional inference frameworks, Key-Value (KV) cache tensors required contiguous memory allocation for each sequence. This caused severe memory fragmentation, leaving up to 60 to 80 percent of GPU memory wasted during dynamic token generation.^[source]

PagedAttention (vLLM):
[Block 0] -> [Block 3] -> [Block 1] (Non-contiguous physical GPU VRAM blocks)

RadixAttention (SGLang):
Root Tree -> "System Prompt" -> "User Query A" -> "Response Branch 1"
                            -> "User Query B" -> "Response Branch 2"

TensorRT Paged KV (TensorRT-LLM):
Compiled CUDA Engine -> Fixed Paged KV Cache Pools + In-Flight Batching Scheduler

vLLM PagedAttention

vLLM solved KV cache fragmentation by introducing PagedAttention.^[source] Inspired by virtual memory and paging algorithms in computer operating systems, PagedAttention breaks down the KV cache of each sequence into fixed-size physical memory blocks. Physical blocks do not need to reside in contiguous VRAM addresses. Instead, vLLM maintains a virtual block table mapping logical tokens to non-contiguous physical pages.

This architecture enables dynamic memory allocation on demand. When a sequence generates new tokens, vLLM allocates new memory blocks only as needed. Additionally, PagedAttention allows multiple request streams or parallel decoding steps (such as beam search or parallel sampling) to share physical memory blocks safely, cutting KV memory overhead significantly.^[source]

SGLang RadixAttention

While vLLM block-level caching allows static prefix sharing, SGLang extends KV cache management by introducing RadixAttention.^[source] RadixAttention treats the entire KV cache as a dynamic radix tree (a space-optimized prefix tree data structure).

In RadixAttention, tokens across past and active requests map into tree nodes. When a new request arrives, SGLang performs a fast radix tree lookup to find the longest matching prefix already resident in GPU VRAM. If a match occurs (such as a shared system prompt, a retrieval-augmented generation context block, or a multi-turn chat history), SGLang reuses the existing KV cache sub-tree without running redundant prefill computations.^[source]

When GPU memory reaches capacity, RadixAttention uses a Least Recently Used (LRU) eviction policy combined with tree node pruning to evict unused branches while preserving shared parent nodes.^[source] For workflows with heavy prompt reuse, this dynamic radix tree approach eliminates redundant prefill passes.

TensorRT-LLM Paged KV cache

TensorRT-LLM combines paged KV cache pools with NVIDIA's In-Flight Batching (IFB) scheduler.^[source] In-Flight Batching (also known as continuous batching) manages requests at the iteration level rather than waiting for an entire batch of prompts to complete generation.

TensorRT-LLM pre-allocates contiguous memory pools for Paged KV blocks inside the compiled TensorRT execution graph.^[source] Because KV allocations are bound directly to custom CUDA kernels, memory transfers happen with minimal host-to-device synchronization overhead. However, unlike SGLang's dynamic radix tree, TensorRT-LLM's memory management is tightly coupled to the static dimensions defined during engine compilation.^[source]

For detailed hardware specifications and memory sizing recommendations when selecting GPUs for serving, review our LLM GPU VRAM requirements guide.

Execution path and compilation overhead

The operational difference between these three frameworks centers on how they translate high-level PyTorch model code into GPU execution kernels.

vLLM / SGLang Execution Flow:
PyTorch Model / Weights -> JIT Custom CUDA/Triton Kernels -> Dynamic Execution

TensorRT-LLM Execution Flow:
PyTorch Weights -> ONNX / TRT Conversion -> AOT Engine Compilation -> C++ Executable Runtime

vLLM just-in-time execution

vLLM operates as a native Python framework built on PyTorch, Triton, and custom C++/CUDA kernels.^[source] When launching a model server using vLLM, the engine inspects model weights, loads weights into VRAM, and compiles required Triton or CUDA kernels on demand during startup.^[source]

This Just-In-Time (JIT) approach allows developers to launch model servers in seconds using simple CLI commands:

python3 -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-70B-Instruct \
  --tensor-parallel-size 4

Because vLLM does not require ahead-of-time graph compilation, swapping model checkpoints, tweaking context window limits, or migrating between GPU architectures requires no build pipeline step.

SGLang runtime and FlashInfer integration

SGLang uses a fast Python/C++ server combined with the FlashInfer kernel library for accelerated attention computations.^[source] Similar to vLLM, SGLang loads models directly without a manual engine compilation phase:

python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-3.1-70B-Instruct \
  --tp 4 \
  --enable-flashinfer-mla

SGLang integrates specialized decoding kernels for structured outputs. Using compressed Finite State Machines (FSMs) and Jump-Forward decoding, SGLang parses JSON schemas or regex constraints directly within the decoding loop.^[source] This reduces token rejection overhead during structured output generation compared to standard speculative parsing.

TensorRT-LLM ahead-of-time engine compilation

TensorRT-LLM departs from Python-based runtimes by enforcing an Ahead-Of-Time (AOT) compilation phase.^[source] To deploy a model with TensorRT-LLM, engineers must run a multi-step compilation process using trtllm-build:

1. Download and convert PyTorch model checkpoints into intermediate weight formats.
2. Define target GPU compute capability, maximum batch size, maximum input sequence length, and tensor-parallel tensor counts.
3. Invoke trtllm-build to generate a compiled .engine binary optimized specifically for the target GPU architecture (e.g. NVIDIA H100 SXM vs NVIDIA H200).^[source]

trtllm-build \
  --checkpoint_dir ./llama-3.1-70b-tp4/ \
  --output_dir ./engine_outputs/ \
  --gemm_plugin float16 \
  --max_batch_size 64 \
  --max_input_len 4096 \
  --max_output_len 2048

The resulting binary engine contains fused CUDA kernels tailored exclusively to the target GPU SKU. Operations such as LayerNorm, Matrix Multiplication (GEMM), and Activation functions are fused into single CUDA graph dispatches.^[source] This eliminates Python runtime interpreter latency and host CPU scheduling bottlenecks.

However, this performance comes with high operational friction. If you decide to change the maximum sequence length from 4,096 to 8,192, or if you migrate from an H100 GPU to an H200 GPU, the existing binary engine cannot run. You must re-run the entire engine build pipeline. To compare instance options before committing to engine builds, see our guide on how to deploy LLMs on GPU clouds.

Multi-GPU parallelism and advanced model architectures

As model sizes exceed single GPU memory capacities, serving frameworks must split model layers across multiple GPUs using Tensor Parallelism (TP), Pipeline Parallelism (PP), or Expert Parallelism (EP).

Model Parallelism Strategies:
Tensor Parallelism (TP)   -> Split Linear Layer Weights Across GPUs within Single Node (NVLink)
Pipeline Parallelism (PP) -> Split Model Layers Sequentially Across Nodes
Expert Parallelism (EP)   -> Route Sparse Mixture-of-Experts (MoE) Tokens to Specific GPUs

Tensor Parallelism and MoE routing

• vLLM: Implements ColumnParallelLinear and RowParallelLinear layers with Ray or Megatron-LM backends.^[source] It supports Mixtral and DeepSeek Mixture-of-Experts (MoE) models with parallel expert routing across multiple GPUs.^[source]
• SGLang: Implements optimized MoE routing kernels and specialized backends for DeepSeek architectures.^[source] SGLang includes native support for DeepSeek Multi-Head Latent Attention (MLA), using FlashInfer and FlashAttention-3 backends to compress KV cache representations without speed penalties on Hopper and Blackwell GPUs.^[source]
• TensorRT-LLM: Delivers custom C++ NCCL communication plugins fused directly into TensorRT execution graphs.^[source] Inter-GPU communications over NVIDIA NVLink or NVSwitch execute with minimal host latency, making TensorRT-LLM performant for large-scale multi-node cluster deployments.^[source]

Support for low-precision quantization

All three frameworks support low-precision execution to shrink VRAM requirements and accelerate memory bandwidth throughput:

• vLLM: Native support for AWQ, GPTQ, FP8 (E4M3), and INT8 quantization schemes.^[source]
• SGLang: Optimized FP8 and INT4 execution paths integrated into FlashInfer kernels.^[source]
• TensorRT-LLM: Complete integration with NVIDIA TensorRT Model Optimizer (ModelOpt), supporting FP8 execution on Hopper GPUs and FP4 micro-scaling precisions on Blackwell (B200) GPUs.^[source]

To evaluate GPU memory specs across different cloud providers, use the GPU lookup tool to inspect available VRAM configurations.

Hardware compatibility and ecosystem lock-in

Hardware compatibility represents a key division line when choosing an inference engine.

vLLM Hardware Ecosystem:
[NVIDIA GPUs] --- [AMD ROCm / MI300X] --- [Google TPU] --- [Intel XPU / CPU]

SGLang Hardware Ecosystem:
[NVIDIA GPUs] --- [AMD ROCm / MI300X]

TensorRT-LLM Hardware Ecosystem:
[NVIDIA GPUs exclusively] (Ampere, Hopper, Blackwell)

Multi-vendor hardware support

vLLM provides the broadest hardware support matrix in the AI ecosystem.^[source] It natively targets:

• NVIDIA GPUs via CUDA.
• AMD Instinct GPUs (such as MI300X) via AMD ROCm.^[source]
• Google Cloud TPUs (v4, v5e, v5p, v6e) via PyTorch/JAX integrations.^[source]
• Intel XPUs and CPU execution targets.^[source]

This vendor independence protects infrastructure teams from single-hardware vendor lock-in. If cloud availability for NVIDIA H100 GPUs becomes constrained, teams running vLLM can migrate workloads to AMD MI300X instances without changing their serving framework software layer. For more details on AMD cloud options, consult our AMD MI300X cloud rentals guide.

SGLang supports both NVIDIA CUDA and AMD ROCm backends.^[source] While its primary development targets NVIDIA Hopper and Blackwell chips, its PyTorch and FlashInfer integration allows it to execute on AMD Instinct accelerators as well.^[source]

NVIDIA hardware lock-in

TensorRT-LLM is built exclusively for NVIDIA hardware.^[source] Because TensorRT-LLM relies on deep hardware-software integration, such as custom Tensor Core instructions, FP8/FP4 hardware transformer engines, and NVIDIA NCCL primitives, compiled engines cannot run on non-NVIDIA accelerators.^[source]

Choosing TensorRT-LLM commits your operational stack to NVIDIA GPUs. While this lock-in grants access to peak performance on NVIDIA silicon, it prevents switching to alternative accelerators if cloud costs or hardware shortages mandate multi-vendor cloud strategies.

Cost efficiency and hardware utilization

Evaluating the cost efficiency of an LLM serving engine requires analyzing how effectively each framework converts GPU cloud rental spend into active token generation.

Deploying an 8x NVIDIA H100 node on cloud infrastructure requires continuous GPU spend. If an inference engine suffers from memory fragmentation or CPU dispatch bottlenecks, GPUs sit idle between request tokens, driving up the cost per generated million tokens.

To compare current GPU rental rates across cloud providers before choosing your infrastructure tier, inspect the H100 vs H200 cloud pricing comparison and our guide on how to optimize GPU cloud costs.

Maximizing throughput per dollar

• vLLM Cost Efficiency: Delivers solid cost efficiency for general production workloads.^[source] PagedAttention ensures high memory utilization, allowing high concurrency per instance. Its quick deployment reduces developer labor hours spent maintaining build scripts.
• SGLang Cost Efficiency: Outperforms alternative engines in prompt-heavy workloads like AI agents and RAG applications.^[source] By achieving high RadixAttention prefix cache hit rates, SGLang skips prefill compute steps for repeated prompts. This reduces total GPU execution time per request, directly lowering cloud compute bills for context-heavy applications.^[source]
• TensorRT-LLM Cost Efficiency: Delivers high raw throughput per GPU instance on fixed NVIDIA deployments.^[source] By fusing CUDA kernels and stripping Python interpreter overhead, TensorRT-LLM maximizes token output per second on expensive H100 or H200 nodes. However, the engineering labor required to maintain AOT engine build pipelines must be factored into total operating expenses.

Category decisions: when to choose each framework

Choose vLLM if:

• You need a production-ready serving layer that installs quickly with standard Python toolchains.^[source]
• Your infrastructure strategy requires hardware portability across NVIDIA GPUs, AMD Instinct accelerators, or Google TPUs.^[source]
• You host multiple model architectures that change frequently during rapid product iteration.
• Your team wants a standard OpenAI-compatible HTTP API server without managing C++ build tools.^[source]

Choose SGLang if:

• Your application relies heavily on shared prompts, multi-turn chat history, or AI agent workflows where RadixAttention prefix caching eliminates prefill computation.^[source]
• You serve DeepSeek-V3 or DeepSeek-R1 models and want native FlashInfer MLA optimizations.^[source]
• You require fast, structured JSON generation using compressed Finite State Machine decoding.^[source]
• You want high concurrency and dynamic cache management on both NVIDIA and AMD GPUs.^[source]

Choose TensorRT-LLM if:

• You run enterprise-scale production serving with fixed model weights on dedicated NVIDIA GPU clusters.^[source]
• Squeezing maximum throughput and lowest latency from expensive NVIDIA Hopper or Blackwell hardware is your top infrastructure goal.^[source]
• Your engineering team has the capacity to build and maintain automated C++ engine compilation pipelines.^[source]
• You require deep integration with NVIDIA TensorRT Model Optimizer for FP8 or FP4 quantization.^[source]

Who should choose each LLM serving framework

AI Startups building multi-model API services

Choose vLLM. Early-stage startups need deployment agility. vLLM allows engineers to launch, test, and swap open-source models (such as Llama 3.1, Qwen 2.5, or Mistral) in minutes without spending hours compiling hardware engines for every new experiment.^[source]

Infrastructure Teams building AI Agent platforms

Choose SGLang. Agentic workflows generate repeated system prompts, tool definitions, and long conversation histories. SGLang's RadixAttention retains these shared context blocks in VRAM across thousands of user interactions, maximizing cache hit rates and minimizing response latency.^[source]

Enterprise AI Platforms with fixed production models

Choose TensorRT-LLM. Enterprise teams serving millions of daily API requests on stable, long-term models benefit from TensorRT engine compilation. The upfront engineering investment in trtllm-build pipelines pays off by maximizing token throughput per server node.^[source]

Multi-Cloud ML Teams seeking vendor independence

Choose vLLM or SGLang. If your procurement strategy involves switching between NVIDIA H100 instances and AMD MI300X rentals based on cloud provider spot pricing and availability, vLLM and SGLang offer multi-vendor compatibility that prevents software lock-in.^{[source][source]} To explore dedicated inference host options, read our best GPU cloud for inference guide.

Alternatives to vLLM, SGLang, and TensorRT-LLM

If none of these three frameworks fit your operational requirements, consider these alternative open-source inference engines:

• TGI (Text Generation Inference): Developed by Hugging Face, TGI is a production-grade containerized serving solution with native support for watermarking, token streaming, and dynamic batching.
• Ollama: Built on llama.cpp, Ollama provides an easy-to-use desktop and local server manager for running LLMs on consumer hardware, macOS metal, and single-GPU instances.
• LMDeploy: Maintained by the OpenMMLab team, LMDeploy offers dynamic batching and engine optimization for NVIDIA GPUs with TurboMind and PyTorch execution runtimes.
• vLLM-Omni / Speculative Engines: Specialized speculative decoding setups that combine small draft models with main verification models to accelerate inter-token generation speed.

Pros and cons

Methodology and sources

GPU Picks compiled this comparison using official technical documentation, architectural specifications, and published developer guides from the vLLM Project, SGLang Project, and NVIDIA Corporation.^{[source][source][source]} GPU Picks did not run hands-on latency testing, synthetic benchmark suites, or paid provider uptime trials.

Inference framework performance varies based on hardware generation, batch sizes, sequence lengths, tensor parallelism configurations, and quantization parameters. Infrastructure teams should evaluate candidate frameworks using their target production prompt distributions. Read our complete editorial methodology to inspect our source verification standards.

Frequently asked questions