vLLM is a specialized LLM inference engine optimized for throughput with PagedAttention and dynamic batching, while Triton is a general-purpose inference server supporting multiple model types and frameworks with broader deployment flexibility. vLLM achieves 24x higher throughput for LLMs, but Triton handles diverse workloads including computer vision and traditional ML models.
vLLM vs Triton Inference Server — Side-by-side comparison on A Versus B
High-throughput LLM inference engine with paged attention and easy-to-use Python API.
General-purpose inference server supporting multiple frameworks and model types
Primary Use Case. Throughput for LLMs (tokens/sec). Supported Model Types. Memory Efficiency. Setup Complexity. Multi-GPU Distribution. Production Maturity
Choose vLLM if you're deploying LLMs and need maximum throughput, lower latency, and minimal operational overhead—it's purpose-built for this exact use case. Choose Triton if you're running a heterogeneous model ecosystem (LLMs + vision models + classical ML), need vendor-agnostic deployment across frameworks, or require deep integration with Kubernetes/cloud platforms.
vLLM: 2800 | Triton Inference Server: N/A
vLLM: 10.5 | Triton Inference Server: N/A
vLLM: 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | Triton Inference Server: N/A
vLLM: 256 | Triton Inference Server: N/A
vLLM: 80-120 ms | Triton Inference Server: N/A
Throughput (tokens/second, batch size 32)
vLLM: ~1200 tok/s | Triton Inference Server: N/A
vLLM: 8 GB | Triton Inference Server: N/A
vLLM: 5-6 GB (with optimization) | Triton Inference Server: N/A
Setup Time (from download to first inference)
vLLM: 30 minutes | Triton Inference Server: N/A
vLLM: Unlimited (HuggingFace) | Triton Inference Server: N/A
vLLM: 23,000+ | Triton Inference Server: 7,500+
vLLM: Limited, requires GPU | Triton Inference Server: N/A
Throughput (tokens/second, LLaMA 70B example)
vLLM: 1,500+ | Triton Inference Server: N/A
vLLM: ~4x reduction | Triton Inference Server: N/A
vLLM: Primarily PyTorch/Transformers (limited) | Triton Inference Server: N/A
GitHub Stars (community adoption metric)
vLLM: 21,000+ | Triton Inference Server: N/A
vLLM: 40 GB (with PagedAttention) | Triton Inference Server: N/A
vLLM: High (requires separate instances) | Triton Inference Server: N/A
Batch Size Improvement (via memory savings)
vLLM: 4x larger batches possible | Triton Inference Server: N/A
vLLM: 15-30 (built-in helpers) | Triton Inference Server: N/A
vLLM: 12,500 tokens/sec | Triton Inference Server: N/A
Memory Usage (KV cache, 7B model, batch=1)
vLLM: 8.2 GB (with PagedAttention) | Triton Inference Server: N/A
vLLM: 2 (LLM-specific) | Triton Inference Server: 6+ frameworks
vLLM: 380 ms | Triton Inference Server: N/A
vLLM: Low (Python API) | Triton Inference Server: High (YAML+Protobuf)
vLLM: No native ensemble; requires external orchestration | Triton Inference Server: N/A
vLLM: ~1,200+ organizations (LLM-focused) | Triton Inference Server: N/A
vLLM: ~24,000 | Triton Inference Server: N/A
vLLM: ~8,000-12,000 | Triton Inference Server: N/A
vLLM: 50-60ms | Triton Inference Server: N/A
vLLM: NVIDIA, AMD, Intel, CPU (4 platforms) | Triton Inference Server: N/A
vLLM: 500+ with auto-optimization | Triton Inference Server: N/A
vLLM: 50-60% (Paged Attention) | Triton Inference Server: N/A
vLLM: 5-10 minutes | Triton Inference Server: N/A
vLLM: Free (open-source) | Triton Inference Server: N/A
vLLM: 25,000 tokens/sec | Triton Inference Server: N/A
vLLM: 120-420 minutes (2-7 days with infrastructure) | Triton Inference Server: N/A
Cost per Million Tokens (A100, on-demand)
vLLM: $0.12 | Triton Inference Server: N/A
vLLM: 1,000+ models | Triton Inference Server: N/A
vLLM: Inference-only, no native training | Triton Inference Server: N/A
vLLM: Community-dependent (typically 99.0%+) | Triton Inference Server: N/A
vLLM: User-managed (CUDA, Docker, scaling) | Triton Inference Server: N/A
vLLM: 25,000+ stars, weekly updates | Triton Inference Server: N/A
vLLM: ~5,000-15,000 (on A100 with batching) | Triton Inference Server: ~200-500 (baseline without vLLM backend)
vLLM: 25-45% of baseline | Triton Inference Server: 100% (standard attention)
vLLM: LLMs only (30+ architectures) | Triton Inference Server: 8+ model categories with 10+ frameworks
vLLM: Native support, drop-in replacement | Triton Inference Server: Requires wrapper/plugin
Enterprise Maturity (years in production)
vLLM: 2 years (v1.0 in 2024) | Triton Inference Server: 6 years (v1.0 in 2020)
vLLM: ~18-22 GB with continuous batching | Triton Inference Server: ~24-28 GB standard
vLLM: Community Helm charts available | Triton Inference Server: Official Helm charts and auto-scaling
Inference Throughput (RTX 4090, Llama 2 13B)
vLLM: ~700 tokens/sec | Triton Inference Server: N/A
vLLM: ~6.5 GB | Triton Inference Server: N/A
vLLM: 25-40 minutes | Triton Inference Server: N/A
vLLM: 6 GB | Triton Inference Server: N/A
vLLM: 4+ (CUDA, ROCm, CPU, TPU, custom) | Triton Inference Server: N/A
vLLM: Yes (native continuous batching) | Triton Inference Server: N/A
vLLM: OpenAI-compatible API | Triton Inference Server: N/A
vLLM: 80% reduction | Triton Inference Server: 30% reduction
vLLM: 3 (basic) | Triton Inference Server: 8+ (advanced)
vLLM: Native (tensor parallelism) | Triton Inference Server: Supported (requires config)
vLLM: Basic (throughput, latency) | Triton Inference Server: Advanced (Prometheus, tracing)
Inference Throughput (LLaMA 2 70B, A100 GPU)
vLLM: 25,000 tokens/sec (batch 256) | Triton Inference Server: N/A
vLLM: 45 GB (with PagedAttention) | Triton Inference Server: N/A
vLLM: 20-30 minutes (self-hosted) | Triton Inference Server: N/A
Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
vLLM: $0.25 (self-hosted, amortized) | Triton Inference Server: N/A
vLLM: 500+ community models | Triton Inference Server: N/A
vLLM: User-managed (Docker, K8s, VMs) | Triton Inference Server: N/A
vLLM: No SLA (community support) | Triton Inference Server: N/A
vLLM: Community (GitHub issues) | Triton Inference Server: N/A
vLLM: ~60% | Triton Inference Server: N/A
vLLM: 10-23x vs standard | Triton Inference Server: N/A
vLLM: 15+ formats (HF, GGUF, AWQ, GPTQ, etc) | Triton Inference Server: N/A
vLLM: 5-10 minutes for basic LLM service | Triton Inference Server: 20-30 minutes with model config setup
vLLM: Via API wrapper | Triton Inference Server: N/A
vLLM: Community-based | Triton Inference Server: N/A
vLLM: 2-3 weeks | Triton Inference Server: N/A
Inference Throughput (tokens/sec, Llama 2 7B)
vLLM: 2,000-3,500 tokens/sec | Triton Inference Server: N/A
vLLM: 50-150ms | Triton Inference Server: N/A
vLLM: 1 (LLMs only) | Triton Inference Server: N/A
vLLM: 24-28 GB | Triton Inference Server: N/A
vLLM: GPTQ, AWQ, INT4, FP8, INT8 | Triton Inference Server: N/A
vLLM: 92% linear scaling | Triton Inference Server: N/A
vLLM: 500-2,000 concurrent | Triton Inference Server: N/A
vLLM: 2-3 minutes | Triton Inference Server: N/A
vLLM: 3,200 tokens/sec | Triton Inference Server: N/A
vLLM: 45 ms | Triton Inference Server: N/A
vLLM: 30+ architectures | Triton Inference Server: N/A
vLLM: 1-2 commands | Triton Inference Server: N/A
vLLM: 9-11 GB | Triton Inference Server: N/A
vLLM: 22,000+ stars | Triton Inference Server: N/A
vLLM: 5 methods (GPTQ, AWQ, SqueezeLLM, etc.) | Triton Inference Server: N/A
How to choose between vLLM and Triton Inference Server
vLLM vs Triton Inference Server — Structured Comparison Data
Machine-readable comparison dataset for vLLM vs Triton Inference Server. Contains 94 attribute comparisons, 5 FAQ pairs.
vLLM vs Triton Inference Server — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for vLLM vs Triton Inference Server.
Yes. Triton added official vLLM backend support in v2.34+. This combines Triton's multi-model orchestration with vLLM's LLM optimization, allowing you to run heterogeneous workloads where LLM inference uses vLLM and other models use appropriate backends.
Does vLLM support vision-language models?
vLLM added experimental support for vision-language models (LLaVA, Qwen-VL) in v0.4, but optimization is focused on the language component. For production vision inference, Triton with TensorRT backend is more mature.
What's the latency difference between vLLM and Triton for a single request?
For a single LLM request: vLLM ~150-300ms, Triton with vLLM backend ~200-350ms (slight overhead for routing). For batched requests (10+), vLLM achieves 50x lower latency per token due to continuous batching versus traditional request queuing.
Which should I use for an LLM-only service?
Use vLLM. It requires less configuration, deploys faster (5-10 minutes), consumes 20-30% less GPU memory, and delivers 24x higher throughput. Triton adds unnecessary complexity if you're not running multiple model types.
Can I do multi-model inference with vLLM?
vLLM is single-model-focused per process. For multi-model inference, run multiple vLLM instances (one per model) or use Triton with vLLM backend to orchestrate multiple models with shared resources and load balancing.
How to Choose Between vLLM and Triton Inference Server
Primary Use Case: vLLM has Large Language Models only, while Triton Inference Server has Multi-model and multi-framework.
Consider: Throughput for LLMs (tokens/sec)
Throughput for LLMs (tokens/sec): vLLM has 24x baseline (with PagedAttention), while Triton Inference Server has Baseline performance.
Supported Model Types: vLLM has LLMs (Llama, GPT, Mistral, etc.), while Triton Inference Server has LLMs, CNNs, RNNs, transformers, traditional ML.
Memory Efficiency: vLLM has PagedAttention reduces KV cache by 55-75%, while Triton Inference Server has Standard attention mechanisms.
Setup Complexity: vLLM has Minimal, LLM-specific configuration, while Triton Inference Server has Moderate, requires model config files.
Multi-GPU Distribution: vLLM has Tensor parallelism, pipeline parallelism, while Triton Inference Server has Multiple backends (vLLM, TensorRT, PyTorch).
Production Maturity: vLLM has Stable since v0.4 (2024), strong LLM ecosystem, while Triton Inference Server has Mature since 2020, enterprise-tested.