LLM Serving Framework Comparison: vLLM vs SGLang vs TGI vs TensorRT-LLM

Contents

• LLM Serving Framework Comparison: Overview
• Framework Comparison Table
• Architecture & Design Philosophy
• Performance Benchmarks
• Memory Efficiency
• Model Support & Compatibility
• Production Readiness
• Deployment Complexity
• Use Case Recommendations
• FAQ
• Related Resources
• Sources

LLM Serving Framework Comparison: Overview

LLM Serving Framework Comparison is the focus of this guide. Four frameworks, four trade-offs.

vLLM: throughput. SGLang: structured output. TensorRT-LLM: raw speed on NVIDIA. TGI: simplicity.

At scale, vLLM and TensorRT-LLM beat vanilla serving by 2-3x. SGLang handles structured JSON. TGI ships fast.

No winner everywhere. Pick based on the bottleneck.

Framework Comparison Table

Data from framework benchmarks and official documentation (March 2026).

Architecture & Design Philosophy

vLLM: Throughput-First

vLLM's architecture centers on PagedAttention, a GPU memory management technique borrowed from operating system paging. Instead of allocating fixed KV cache blocks for each sequence, vLLM manages variable-sized blocks dynamically. This reduces memory fragmentation and increases batch capacity.

Result: vLLM servers handle 40-60% larger batch sizes than baseline implementations. Throughput jumps from 800 tok/s to 2,100 tok/s on a single H100.

The tradeoff: vLLM requires model-specific optimization. Custom kernels, quantization plugins, and scheduling logic need tuning. Out-of-the-box performance is good. Optimized performance (2,100+ tok/s) requires engineering investment.

SGLang: Structured Generation & Control Flow

SGLang adds a programming language layer on top of inference. Instead of sending raw prompts, teams write SGLang programs that define logic: "if this token matches X, branch to Y; generate structured JSON; constrain output format to Z."

The value: eliminating post-processing. Structured generation happens during decoding. Native support for regex constraints, JSON schema validation, and conditional logic.

Performance: SGLang's architecture is newer and less optimized for pure throughput. Single-GPU throughput trails vLLM by 15-20%. But structured output support eliminates request overhead on the application side.

TensorRT-LLM: GPU-Native Compilation

TensorRT-LLM compiles LLM inference to CUDA kernels. Similar to how TensorRT handles computer vision inference: convert the model graph to optimized CUDA operations, fuse layers, and compile to GPU machine code.

Advantage: Maximum raw performance. TensorRT-LLM hits 2,400 tok/s on H100 (highest in this comparison). Memory efficiency is exceptional.

Disadvantage: Setup is complex. Requires model-specific compilation steps. Adding a new quantization method or model variant means recompiling. Deployment pipeline is slower (2-4 hours to compile a 70B model).

TGI (Text Generation Inference): Production-Grade Defaults

Hugging Face TGI trades some peak throughput for operator simplicity. Written in Rust for safety and performance. Comes with sensible defaults: automatic quantization, integrated LoRA, model preloading, request queuing.

TGI is the path of least resistance. Deploy with a single Docker command. No tuning required. Throughput is respectable (1,600 tok/s) but trails vLLM. Latency under batch conditions is slightly higher.

For teams prioritizing shipping over optimization, TGI wins. For teams needing every percent of performance, vLLM or TensorRT-LLM required.

Performance Benchmarks

Throughput: Single Model, Varying Batch Sizes

Test: Serving Llama 2 70B on a single NVIDIA H100 PCIe.

Batch Size 1 (latency-sensitive):

• vLLM: 320 tok/s
• SGLang: 280 tok/s
• TensorRT-LLM: 380 tok/s
• TGI: 250 tok/s

TensorRT-LLM leads at low batch, where latency dominates. vLLM competitive. TGI trails (expected; not compiled for latency).

Batch Size 32 (throughput-optimized):

• vLLM: 2,100 tok/s
• SGLang: 1,800 tok/s
• TensorRT-LLM: 2,400 tok/s
• TGI: 1,600 tok/s

TensorRT-LLM edges out vLLM. The gap widens at larger batches due to TensorRT's kernel optimization. SGLang and TGI both capable but trailing by 15-25%.

Batch Size 128 (batch processing):

• vLLM: 2,050 tok/s (slight throughput decline due to scheduling overhead)
• SGLang: 1,750 tok/s
• TensorRT-LLM: 2,350 tok/s
• TGI: 1,580 tok/s

At very large batches, vLLM and TensorRT-LLM reach saturation. Memory bandwidth becomes the limiter (H100 has 3.35 TB/s bandwidth ceiling). Additional batch size doesn't improve throughput.

Latency Percentiles (Batch Size 32)

TensorRT-LLM wins on latency consistency. vLLM middle ground. TGI has higher variance (Rust async scheduling adds overhead).

Cost Per Million Tokens

Assuming H100 rental at $1.99/hr on RunPod.

Pure throughput (batch=32, all requests 128 tokens):

• vLLM: 1M tokens in 478 GPU-seconds = $0.000267/1M tokens
• SGLang: 1M tokens in 556 GPU-seconds = $0.000311/1M tokens
• TensorRT-LLM: 1M tokens in 416 GPU-seconds = $0.000233/1M tokens
• TGI: 1M tokens in 625 GPU-seconds = $0.000349/1M tokens

TensorRT-LLM lowest cost per token. vLLM competitive. SGLang and TGI both 30-50% more expensive due to lower throughput.

For high-volume inference, TensorRT-LLM ROI is compelling if setup cost is amortized across millions of tokens.

Memory Efficiency

KV Cache Consumption (Llama 2 70B, batch=32, context=4K tokens)

vLLM with PagedAttention:

• KV cache: 12-14GB (dynamic allocation)
• Model weights (fp8): 35GB
• Activation buffers: 3GB
• Total: ~50-52GB

TensorRT-LLM:

• KV cache (pre-allocated, chunked): 10-12GB
• Model weights: 35GB
• CUDA graph cache: 2GB
• Total: ~47-49GB

TGI:

• KV cache: 15-16GB (fixed allocation)
• Model weights: 35GB
• Buffers: 4GB
• Total: ~54-55GB

SGLang:

• KV cache: 13-15GB
• Model weights: 35GB
• Interpreter overhead: 4GB
• Total: ~52-54GB

TensorRT-LLM is most memory-efficient. Enables fitting additional requests on VRAM-constrained hardware (L40 48GB variant).

Memory Overhead on Smaller GPUs

On a 24GB L4:

• vLLM can serve Llama 2 7B with batch 8. Cannot fit 70B.
• TGI can serve 7B with batch 4. 70B quantized (4-bit) tight, batch 1 only.
• TensorRT-LLM can squeeze 70B (8-bit) at batch 1 on 24GB (just fits).
• SGLang struggles on 24GB due to interpreter overhead.

For multi-GPU setups, tensor parallelism is the fix. But single-GPU serving on budget GPUs: TensorRT-LLM is tightest.

Model Support & Compatibility

Supported Model Families (as of March 2026)

vLLM:

• Llama 2/3, Mistral, Mixtral
• Phi, Qwen, Yi
• DeepSeek, Grok
• ~50 total model variants

SGLang:

• Llama 2/3
• Mistral
• Phi
• Qwen
• ~25 total (narrower support, improving)

TensorRT-LLM:

• Llama 2/3
• Mistral
• Phi
• GPT-J, Falcon (older models)
• Custom models via C++ plugin layer
• ~30 officially supported

TGI:

• All Hugging Face Hub models (800+)
• Includes above plus smaller/specialized models
• Widest compatibility

Quantization Support

vLLM: GPTQ, AWQ, GGML, custom fp8/fp16. Plug-and-play with quantized HF models.

TensorRT-LLM: INT4, INT8, fp8. Quantization requires recompilation. Fewer out-of-the-box options.

TGI: GPTQ, AWQ, native fp8. Good balance. Quantization layers abstracted away.

SGLang: Limited quantization support. Primary focus on unquantized models. Fp8 partial.

For teams with existing quantized models, vLLM and TGI are drop-in replacements. TensorRT-LLM requires custom integration.

Production Readiness

Stability & Maintenance

vLLM: Active development (weekly releases). Ecosystem maturity: 2+ years in production at scale. Bugs surface quickly, fixes ship fast. API is stable (v0.8 has backwards compatibility guarantees).

TensorRT-LLM: Commercial support from NVIDIA. Slower release cycle (quarterly major versions). Rock-solid when deployed to supported configs. Less community testing on edge cases.

TGI: Backed by Hugging Face. Stable API. Multi-year production deployment at Hugging Face inference endpoints (millions of requests/day).

SGLang: Newer (launched mid-2024). Rapid iteration. API changes between versions. Less battle-tested in production. Higher risk for long-running services.

Monitoring & Observability

vLLM: Prometheus metrics (throughput, latency, cache hit rate). OpenTelemetry tracing. Decent log output.

TGI: Similar metrics export. Simpler logs (Rust simplicity). Good for containerized environments.

TensorRT-LLM: Lower-level CUDA metrics. Requires custom instrumentation for business metrics (throughput, cost).

SGLang: Minimal monitoring. Observability is a gap.

For production deployments (SLAs, alerts, dashboards), vLLM and TGI are ahead.

Deployment Complexity

Single GPU, Single Model

vLLM: pip install vllm && python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-70b-chat-hf. Done. 2 minutes. Exposes OpenAI-compatible API.

TGI: docker run ghcr.io/huggingface/text-generation-inference:latest --model-id meta-llama/Llama-2-70b-chat. Done. 5 minutes.

SGLang: Install vLLM base, install SGLang layer, write SGLang program, python server.py. 15-30 minutes.

TensorRT-LLM: Download TensorRT, build CUDA kernels for the model, compile model, copy weights, run inference. 2-4 hours for 70B.

Winner: vLLM and TGI for speed-to-serve. TensorRT-LLM for raw performance if developers have time.

Multi-GPU Distributed Inference

vLLM: Built-in tensor parallelism. Set --tensor-parallel-size 4. Handles communication, batching across GPUs. Works on NVLink (fast) and PCIe (slower but works).

TGI: Native tensor parallelism. Similar UX to vLLM.

TensorRT-LLM: Tensor parallelism supported. Requires model-specific compilation for each GPU count.

SGLang: Limited distributed support. Designed for single-GPU or multi-GPU on same machine (limited scaling).

Use Case Recommendations

High-Throughput Batch Inference (10M+ tokens/day)

Use TensorRT-LLM. Peak throughput (2,400 tok/s) minimizes GPU hours and cost. Compilation overhead is one-time.

Alternative: vLLM if faster iteration is needed. Trade 15% throughput for 10x faster experimentation.

Structured Output Requirement

Use SGLang. Native JSON schema constraints, regex guards, conditional branching. Eliminates post-processing errors.

Cost: 15% throughput penalty vs vLLM. For workloads where output quality matters (legal docs, code generation), the guarantee is worth it.

Multi-Model Serving (A/B testing, fallback models)

Use vLLM. Simplest multi-model UX. Router layer can distribute requests across model instances.

TGI works too. vLLM has better documentation for this pattern.

Lowest Total Cost of Ownership

Use TensorRT-LLM if model is fixed (not changing weekly). Setup cost amortized across thousands of hours of inference.

Use vLLM if model changes frequently or developers deploy many model variants. Lower setup, slightly higher per-token cost.

Easiest Deployment (minimal DevOps)

Use TGI. One Docker image, one environment variable for model ID, cloud-ready. Integrates with Kubernetes, modal, Replicate.

Onboarding: 30 minutes. Maintenance: minimal.

Research & Rapid Experimentation

Use vLLM. Pip install, iterate, swap models. Easy debugging. Large community.

Custom Hardware (AMD ROCm, IPU, TPU)

Use TGI (ROCm support). vLLM (experimental ROCm). TensorRT-LLM is NVIDIA-only.

FAQ

Which framework gives the absolute best throughput?

TensorRT-LLM at 2,400 tok/s on H100 (pure throughput test). But the 2-4 hour compilation cost and limited model support matter. For practical deployments, vLLM at 2,100 tok/s is 88% of peak, 100x simpler to set up.

Can I switch from vLLM to TensorRT-LLM later?

Yes, but it's not plug-and-play. Your serving code (request routing, batching, caching) needs rewrite. Model serving APIs differ. Plan for 1-2 weeks of migration work.

Does SGLang replace vLLM?

No. SGLang is built on vLLM. You're running vLLM internally, plus SGLang layer. It's vLLM + structured programming. Different use case, not a replacement.

What about smaller frameworks (MLC-LLM, LMDeploy, Ollama)?

MLC-LLM: Compiles to WebAssembly and native CUDA. Portable. Slower than vLLM/TensorRT-LLM (15-20% throughput penalty).

LMDeploy: Chinese open-source. Similar architecture to vLLM. Slightly lower throughput, less English documentation.

Ollama: Consumer-grade. Good for laptop inference. Not suitable for production serving.

For production, stick to the big four (vLLM, SGLang, TensorRT-LLM, TGI).

Can I use multiple frameworks in one deployment?

Yes. Route different models to different frameworks. Example: Route structured-output requests to SGLang, batch processing to TensorRT-LLM. Requires reverse proxy logic (nginx, Envoy).

Complexity rises. Only worth it if performance difference is critical (>20% cost savings).

What's the Python version requirement?

vLLM: Python 3.8+ TGI: Python 3.9+ (via pip), Rust binary no Python needed TensorRT-LLM: Python 3.9+ SGLang: Python 3.10+

Older projects: vLLM is most compatible.

How often should I upgrade?

vLLM: Monthly patches, quarterly major releases. Upgrade every 2-3 major versions (6 months). Critical bugs patched immediately.

TensorRT-LLM: Quarterly releases. More conservative. Upgrade annually unless critical fix released.

TGI: Monthly releases. Stable API. Safe to upgrade monthly.

SGLang: Bi-weekly releases. Faster iteration. Higher risk of breaking changes. Plan for upgrade testing each month.

What's the break-even for TensorRT-LLM compilation time?

On H100 at $1.99/hr:

• Compilation cost: 3 hours × $1.99 = $6 (amortized)
• Throughput gain vs vLLM: 300 tok/s higher on batch=32
• On 1B tokens/day inference: saves ~12 GPU-hours/month = $24/month
• Payback: 1 week

If you're doing more than 1M tokens/day on a fixed model, TensorRT-LLM is economical. Below that, vLLM wins.

Related Resources

• LLM Inference Best Practices
• SGLang vs vLLM Detailed Comparison
• Best LLM Inference Engine for Production
• Deploy LLM to Production Guide
• DeployBase LLM Tools Directory

Sources

• vLLM GitHub Repository and Benchmarks
• SGLang Official Documentation
• NVIDIA TensorRT-LLM Repository
• Hugging Face Text Generation Inference
• NVIDIA H100 Performance Analysis
• DeployBase GPU Pricing Dashboard