AI Model Serving Architecture Comparison: vLLM vs Triton vs TensorRT-LLM vs SGLang vs Ollama

Choosing the right inference engine is one of the most consequential infrastructure decisions for AI products. With a dozen competing frameworks, each optimizing for different workloads, this guide provides a structured comparison to help you choose.

Evaluation Criteria

We compare across six dimensions: throughput (tokens/sec), latency (TTFT and inter-token latency), memory efficiency, model support, ease of use, and production readiness.

vLLM

Best for: General-purpose LLM serving with maximum throughput.

vLLM has emerged as the most popular open-source LLM serving framework, and for good reason. Its PagedAttention mechanism borrowed from OS virtual memory management, eliminates KV-cache memory fragmentation and dramatically improves GPU utilization.

• Throughput: 2-4x faster than HuggingFace Transformers due to continuous batching.
• Model support: Extensive — supports most HuggingFace models including Llama, Mistral, Qwen, Gemma, Phi, and custom architectures.
• Hardware: NVIDIA GPU only (CUDA). Limited AMD support via ROCm.
• Key features: Continuous batching, PagedAttention, speculative decoding, embedding mode, OpenAI-compatible API.
• Maturity: Very high — used by LMSYS (Chatbot Arena), Berkeley, and numerous production deployments.

TensorRT-LLM (NVIDIA)

Best for: Maximum performance on NVIDIA hardware for enterprise production.

NVIDIA official inference compiler that builds optimized execution graphs for specific GPU architectures:

• Throughput: 1.5-4x faster than vLLM on equivalent hardware due to kernel fusion and graph optimization.
• Model support: Limited but growing — Llama 2/3, GPT, Falcon, GPT-NeoX, Baichuan, ChatGLM, Qwen, Phi.
• Hardware: NVIDIA only. Requires specific GPU architecture (SM 80+ for full features).
• Key features: FP8/INT4 quantization, graph optimization, in-flight batching, multi-GPU/multi-node serving.
• Maturity: High — NVIDIA-backed, well-documented, LTS releases.

NVIDIA Triton Inference Server

Best for: Multi-model serving across diverse model types (not just LLMs).

Triton is a model-agnostic serving framework supporting TensorFlow, PyTorch, ONNX, TensorRT, Python backends, and custom backends:

• Throughput: Comparable to TensorRT-LLM when using TensorRT backend; model-specific performance varies.
• Model support: Universal — any framework, any model format.
• Hardware: NVIDIA GPU, x86 CPU, ARM CPU.
• Key features: Multi-model serving, model ensembles, dynamic batching, model versioning, monitoring, Kubernetes integration.
• Maturity: Very high — the industry standard for non-LLM model serving.

SGLang

Best for: Complex generation tasks requiring prefix caching and structured output.

SGLang (Structured Generation Language) excels at generation patterns common in compound AI systems and agentic workflows:

• Throughput: Competitive with vLLM on standard benchmarks; superior on prefix-heavy workloads.
• Model support: Llama, Mistral, Gemma, Qwen, DeepSeek, and others.
• Hardware: NVIDIA GPU (CUDA).
• Key features: RadixAttention (automatic prefix caching), structured generation with regex/grammar, parallel sampling, OpenAI-compatible API.
• Maturity: Medium-high — rapidly growing, born from UC Berkeley research (same lab as vLLM).

Ollama

Best for: Local development, prototyping, and personal use.

Ollama has democratized local LLM inference by wrapping llama.cpps single-binary simplicity with a curated model registry:

• Throughput: Low compared to GPU server frameworks. Designed for single-user, not production serving.
• Model support: 1000+ models in official registry, GGUF format only.
• Hardware: CPU, Apple Silicon (Metal), NVIDIA GPU, AMD GPU. Runs on laptops and Raspberry Pi.
• Key features: One-command model download, built-in model registry, REST API, local chat UI, easy GPU offloading.
• Maturity: High for local use; not designed for production serving.

Head-to-Head Comparison

Feature	vLLM	TensorRT-LLM	Triton	SGLang	Ollama
Max Throughput	★★★★	★★★★★	★★★★	★★★★	★★
Latency (TTFT)	★★★	★★★★★	★★★★	★★★★	★★
Memory Efficiency	★★★★★	★★★★	★★★	★★★★	★★★★★
Model Support	★★★★★	★★★	★★★★★	★★★	★★★★
Ease of Setup	★★★★	★★	★★★	★★★	★★★★★
Production Readiness	★★★★★	★★★★★	★★★★★	★★★★	★★
Multi-GPU/Node	★★★★★	★★★★★	★★★★★	★★★★	★
Structured Output	★★★	★★	★	★★★★★	★

Recommendations

• General purpose serving: vLLM — best balance of throughput, model support, and ease of use.
• Maximum raw performance on NVIDIA: TensorRT-LLM — if you have NVIDIA-exclusive infrastructure and can invest in per-model compilation.
• Multi-model serving: Triton — when you need to serve LLMs alongside traditional ML models, embeddings, and recommendation models.
• Agentic workflows: SGLang — prefix caching and structured output make it ideal for multi-turn agent applications.
• Local development: Ollama — unbeatable for getting started, prototyping, and running models on consumer hardware.

Conclusion

There is no single best inference engine — the optimal choice depends on your hardware, model requirements, and serving patterns. For most teams starting out, vLLM offers the best default. As workloads mature and patterns emerge, migrate to the framework that optimizes for your specific bottleneck: raw speed (TensorRT-LLM), multi-model serving (Triton), or structured generation (SGLang).