LLM Inference Servers Compared: vLLM vs TGI vs SGLang vs Triton (2026)

Your model is only as fast as the server running it.

You can spend weeks fine-tuning a model to perfection, then lose half that performance to a poorly configured inference server. The difference between vLLM and a naive HuggingFace Transformers deployment? Up to 24x throughput on identical hardware.

That gap translates directly to GPU costs. A server that handles 4x more requests means you need 4x fewer GPUs. At $2-3 per H100 hour, the math adds up fast.

This comparison covers the four inference servers that matter in 2026: vLLM, SGLang, TGI, and Triton. We'll look at real benchmarks, setup complexity, and which server fits which workload. No theoretical maximums or vendor marketing. Just data from production deployments.

One major update before we start: TGI entered maintenance mode in December 2025. Hugging Face now recommends vLLM or SGLang for new deployments. If you're running TGI in production, it still works. But plan your migration.

Quick Decision Matrix

Use Case	Best Choice	Why
High-concurrency API serving	vLLM	PagedAttention handles 100+ concurrent requests efficiently
Multi-turn chat, agents	SGLang	RadixAttention achieves 85-95% cache hit rates on shared context
Existing TGI deployment	Keep TGI (for now)	Stable, but migrate to vLLM/SGLang for new projects
NVIDIA-only enterprise stack	Triton + TensorRT-LLM	Maximum performance with full NVIDIA optimization
Local development/prototyping	Ollama	Simplest setup, not for production scale
Batch inference on H100	SGLang or LMDeploy	29% faster than optimized vLLM in recent benchmarks

vLLM: The Production Standard

vLLM came out of UC Berkeley's Sky Computing Lab and quickly became the default choice for production LLM serving. The core innovation is PagedAttention, which borrows virtual memory concepts from operating systems to manage the KV cache.

Traditional inference engines allocate one contiguous memory block per request. This wastes 60-80% of KV cache memory through fragmentation. PagedAttention breaks the cache into small, fixed-size blocks (typically 16 tokens) that can be stored anywhere in GPU memory. The result: under 4% memory waste and significantly larger batch sizes.

Key performance numbers:

• 14-24x higher throughput than HuggingFace Transformers
• 2.2-3.5x higher throughput than early TGI versions
• 85-92% GPU utilization under high concurrency
• Scales linearly up to 100-150 concurrent requests before plateauing

vLLM also implements continuous batching. Instead of waiting for a batch to fill before processing, it dynamically adds incoming requests and removes completed ones. This keeps the GPU busy and reduces time-to-first-token latency for interactive applications.

What vLLM does well:

• Memory efficiency through PagedAttention
• Continuous batching for high throughput
• Automatic prefix caching for repeated prompts
• Multi-GPU support via tensor and pipeline parallelism
• OpenAI-compatible API out of the box
• Broad hardware support (NVIDIA, AMD, Intel, TPU)

Where vLLM falls short:

Recent benchmarks on H100 hardware show SGLang and LMDeploy outperforming vLLM by roughly 29% on batch inference workloads. Even with FlashInfer enabled, vLLM peaks around 12,500 tokens/second while SGLang hits 16,200 tokens/second on Llama 3.1 8B.

The gap comes from architectural differences. vLLM maintains a flexible, plugin-based architecture for compatibility across hardware. SGLang and LMDeploy co-design their attention mechanisms with specific kernel assumptions, trading flexibility for raw speed.

For most production deployments, vLLM remains the safe choice. It's battle-tested, well-documented, and handles diverse workloads reliably. But if you're optimizing for maximum throughput on specific hardware, benchmark SGLang against your actual workload.

Quick start:

pip install vllm
vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000

For production deployments with fine-tuned models, you can point vLLM at your custom weights and serve them with the same OpenAI-compatible API.

SGLang: The Multi-Turn Specialist

SGLang emerged from LMSYS (the team behind Chatbot Arena) with a different focus: optimizing for complex, multi-step LLM programs rather than simple request-response patterns.

The headline feature is RadixAttention. Where vLLM's prefix caching requires manual configuration for specific patterns, RadixAttention automatically discovers and exploits KV cache reuse opportunities using a radix tree data structure.

Cache hit rates by workload type:

Workload	vLLM PagedAttention	SGLang RadixAttention
Few-shot learning	15-25%	85-95%
Multi-turn chat	10-20%	75-90%
Code analysis	5-15%	60-80%
Single requests	0%	0%

For conversational AI and agent workflows where requests share dynamic context (chat history, agent templates, few-shot examples), RadixAttention delivers 10-20% performance improvements over vLLM. On workloads with heavy prefix reuse, SGLang achieves up to 6.4x higher throughput than baseline systems.

SGLang also excels at structured outputs. Its compressed finite state machine enables faster constrained decoding for JSON, function calls, and other formatted responses. If you're building agentic AI applications, SGLang's native support for tool calling and structured generation reduces implementation complexity.

Recent performance highlights:

• Zero-overhead CPU scheduler in v0.4 achieves 95-98% GPU utilization
• Day-one support for DeepSeek V3/R1 with model-specific optimizations
• 16,215 tokens/second on Llama 3.1 8B (H100), beating vLLM by 29%
• Stable per-token latency (4-21ms) across varying load patterns

What SGLang does well:

• Automatic KV cache reuse via RadixAttention
• Structured output generation with xGrammar
• Multi-modal support (images, video)
• Cache-aware scheduling that prioritizes shared-prefix requests
• Native support for multi-LoRA serving

Where SGLang falls short:

SGLang's advantages shrink dramatically for single-turn, independent requests with no shared context. If your workload is pure batch inference with unique prompts, the radix tree overhead provides no benefit.

The ecosystem is also younger than vLLM's. Documentation is thinner, community support is smaller, and edge cases may require more debugging. For teams prioritizing stability over peak performance, vLLM's maturity matters.

Quick start:

pip install sglang
python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --port 8000

TGI: Maintenance Mode Reality

Text Generation Inference was Hugging Face's production inference server, built in Rust and Python with strong observability features (Prometheus metrics, OpenTelemetry tracing) baked in.

As of December 11, 2025, TGI entered maintenance mode.

Hugging Face explicitly recommends vLLM or SGLang for new deployments. The TGI repository now accepts only minor bug fixes and documentation improvements. No new features are coming.

This doesn't mean TGI is broken. If you have stable TGI deployments running in production, they'll continue working. The observability and safety features remain strong. But the writing is on the wall: eventual deprecation is coming.

Migration path:

Hugging Face provides a migration guide for moving Inference Endpoints from TGI to vLLM:

1. Create a new Inference Endpoint with the same model, selecting vLLM as the engine
2. Use the same hardware configuration
3. Test with sample calls
4. Redirect traffic once validated

Both vLLM and SGLang support OpenAI-compatible APIs, so client-side changes are minimal. The main work is infrastructure: updating deployment scripts, adjusting monitoring dashboards, and validating performance under your specific load patterns.

When to stay on TGI:

• Stable production workloads with no performance issues
• Heavy reliance on TGI-specific features (watermarking, specific quantization methods)
• No engineering bandwidth for migration in the near term

When to migrate now:

• Planning new deployments
• Hitting performance limits
• Need features that TGI won't receive (speculative decoding improvements, new model architectures)

For teams evaluating self-hosted inference options, the TGI maintenance announcement simplifies the decision. vLLM and SGLang are the active development paths.

Triton: Enterprise-Grade Complexity

NVIDIA Triton Inference Server (renamed NVIDIA Dynamo Triton in March 2025) takes a different approach. Where vLLM and SGLang are LLM-focused serving engines, Triton is a general-purpose inference platform that supports multiple frameworks (PyTorch, TensorFlow, ONNX, TensorRT) and model types.

For LLM workloads, Triton typically runs with the TensorRT-LLM backend. This means compiling your model into TensorRT engines, which delivers maximum performance on NVIDIA hardware but requires significant setup investment.

What Triton offers:

• Multi-model serving (run LLMs, embedding models, and rerankers on one server)
• Dynamic batching across different model types
• Ensemble pipelines (chain models together)
• Production-grade monitoring and metrics
• Enterprise support through NVIDIA AI Enterprise

Performance characteristics:

TensorRT-LLM, when properly tuned, achieves the lowest single-request latency on NVIDIA GPUs. NVIDIA reports up to 14x reduction in time-to-first-token compared to baseline implementations on H100/GH200 hardware.

However, benchmarks comparing Triton's vLLM backend against standalone vLLM show Triton adds overhead. At higher QPS, the Triton server takes significantly more time due to response packaging overhead in the backend layer.

When Triton makes sense:

• Enterprise environments already committed to NVIDIA infrastructure
• Multi-model serving requirements (LLM + embedding + reranker pipelines)
• Need for model ensembles and complex inference graphs
• MLOps teams with experience tuning TensorRT engines

When to skip Triton:

• Single LLM serving without complex pipelines
• Teams without dedicated MLOps resources
• Need for hardware portability (AMD, Intel, etc.)
• Rapid iteration and model updates (TensorRT compilation adds friction)

The setup complexity is real. Building TensorRT engines, configuring model repositories, and tuning batch parameters requires expertise. For teams focused on rapid model iteration and deployment, the compilation overhead can slow down development cycles significantly.

Head-to-Head Benchmarks

Benchmark numbers vary by model, hardware, and workload. These figures come from recent independent testing, not vendor marketing materials.

Batch inference throughput (Llama 3.1 8B, H100 80GB, 1000 ShareGPT prompts):

Engine	Tokens/Second	Relative Performance
SGLang	16,215	Baseline
LMDeploy	16,132	-0.5%
vLLM (FlashInfer)	12,553	-22.6%
vLLM (default)	~10,000	-38%

Concurrent request handling (vLLM vs TGI):

Metric	vLLM	TGI
GPU utilization (high concurrency)	85-92%	68-74%
Concurrent requests before saturation	100-150	50-75
Memory reduction vs baseline	19-27%	~15%

Multi-turn conversation performance (SGLang vs vLLM):

SGLang's RadixAttention provides approximately 10-20% better performance on multi-turn workloads with shared context. The advantage comes from automatic prefix caching that vLLM requires manual configuration to achieve.

Time-to-first-token consistency:

In one benchmark running 500 prompts, MAX (a newer engine) completed in 50.6 seconds, SGLang in 54.2 seconds, and vLLM in 58.9 seconds. More notably, vLLM showed higher variance: its p99 TTFT was 80% larger than competitors, indicating less consistent latency under load.

For workloads requiring predictable latency (user-facing chat applications), SGLang's tighter latency distribution may matter more than raw throughput.

How to Choose: Decision Framework

Start with your workload type:

High-concurrency API (100+ concurrent users): vLLM. PagedAttention handles memory efficiently at scale, and the ecosystem is mature. Unless benchmarks on your specific model show SGLang winning by 20%+, the operational simplicity of vLLM wins.

Conversational AI, agents, RAG pipelines: SGLang. RadixAttention's automatic prefix caching provides real savings when requests share context. The 10-20% performance improvement compounds into significant cost reduction over time.

Batch inference (offline processing): SGLang or LMDeploy. Recent H100 benchmarks show 29% throughput advantage over vLLM. For batch jobs where latency doesn't matter, maximize tokens per GPU-hour.

Enterprise multi-model pipelines: Triton. If you're serving LLMs alongside embedding models and rerankers in ensemble pipelines, Triton's architecture fits. Accept the setup complexity in exchange for unified infrastructure.

Local development: Ollama. It's not a production server, but for testing prompts and prototyping, nothing beats ollama run llama3.2.

Then consider your constraints:

Hardware flexibility: vLLM supports NVIDIA, AMD, Intel, and TPU. SGLang supports NVIDIA, AMD, and TPU. Triton is NVIDIA-focused. If you might switch hardware, vLLM's portability matters.

Team expertise: vLLM has the largest community and most documentation. SGLang is growing but smaller. Triton requires dedicated MLOps knowledge.

Model update frequency: If you're iterating on fine-tuned models weekly, TensorRT compilation overhead becomes painful. vLLM and SGLang handle weight updates without recompilation.

When Infrastructure Complexity Outweighs DIY

Self-hosted inference gives you control. You choose the hardware, optimize the configuration, and own the full stack. For teams with MLOps expertise and predictable workloads, this makes sense.

But the complexity adds up. You're managing:

• GPU provisioning and scaling
• Model weight distribution and versioning
• Inference server configuration and tuning
• Monitoring, alerting, and debugging
• Security and access controls
• Compliance requirements (SOC 2, GDPR, HIPAA)

For teams focused on building AI applications rather than infrastructure, platforms like Prem handle this complexity. You fine-tune models through a drag-and-drop interface, run evaluations with LLM-as-judge scoring, and deploy to your own infrastructure (AWS VPC or on-premise) with one click.

The tradeoff is flexibility versus speed. Self-hosted vLLM gives you maximum control. A managed platform gives you faster iteration cycles and lets your team focus on model quality rather than server tuning.

Prem specifically targets enterprises that need data sovereignty (Swiss jurisdiction, zero data retention, cryptographic verification) alongside production-grade inference. If compliance is a constraint, the platform approach eliminates the security engineering burden.

For teams that want both control and convenience, Prem's models export to standard formats. You can serve fine-tuned models locally with vLLM when you need maximum customization, and use the platform when you need rapid deployment.

FAQ

Which inference server is fastest in 2026?

For batch inference on H100 hardware, SGLang and LMDeploy lead with roughly 16,200 tokens/second on Llama 3.1 8B. vLLM follows at around 12,500 tokens/second with FlashInfer enabled. For multi-turn conversations with shared context, SGLang's RadixAttention provides 10-20% improvement over vLLM.

Should I migrate away from TGI?

For new projects, yes. Hugging Face recommends vLLM or SGLang. For existing stable deployments, there's no urgency, but plan a migration path. TGI will receive only minor bug fixes going forward.

Is Triton worth the setup complexity?

Only if you need multi-model serving, ensemble pipelines, or are already invested in NVIDIA's enterprise stack. For single-model LLM serving, vLLM or SGLang deliver comparable or better performance with significantly simpler setup.

How much GPU memory do I need?

A 7B parameter model in FP16 requires roughly 14GB for weights. A 70B model needs about 140GB. Add overhead for KV cache: at high concurrency, plan for 5-10x model size. vLLM's PagedAttention reduces this overhead by 19-27% compared to traditional serving.

Can I switch inference servers without changing my application code?

vLLM, SGLang, and TGI all support OpenAI-compatible APIs. If your application uses the standard /v1/chat/completions endpoint, switching servers requires only changing the base URL.

What about smaller models for edge deployment?

For edge deployment, llama.cpp offers maximum portability (CPU, Apple Silicon, AMD GPUs via Vulkan). Ollama builds on llama.cpp with a simpler interface. Neither matches vLLM/SGLang throughput, but they run where those servers can't.

How do I benchmark my specific workload?

vLLM includes benchmark_serving.py for serving benchmarks. SGLang provides similar tooling. Run tests with your actual prompt distribution, not synthetic workloads. ShareGPT traces are commonly used for realistic chat workload simulation.