vLLM vs Triton: LLM Inference Comparison 2026
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
High-throughput LLM inference engine with paged attention and easy-to-use Python API.
Teams deploying production LLM services needing maximum throughput and minimal latency (chatbots, API services, content generation)
Triton Inference Server
General-purpose inference server supporting multiple frameworks and model types
Enterprise teams with mixed workloads, complex inference pipelines, and need for standardized deployment across CPU/GPU/TPU infrastructure
Quick Answer
AI SummaryvLLM 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.
Our Verdict
AI-assistedChoose 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.
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Choose vLLM if
Best pickTeams deploying production LLM services needing maximum throughput and minimal latency (chatbots, API services, content generation)
Choose Triton Inference Server if
Enterprise teams with mixed workloads, complex inference pipelines, and need for standardized deployment across CPU/GPU/TPU infrastructure
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Key Differences at a Glance
- Primary Use Case:✓ Triton Inference Server wins(Multi-model and multi-framework vs Large Language Models only)
- Throughput for LLMs (tokens/sec):✓ vLLM wins(24x baseline (with PagedAttention) vs Baseline performance)
- Supported Model Types:✓ Triton Inference Server wins(LLMs, CNNs, RNNs, transformers, traditional ML vs LLMs (Llama, GPT, Mistral, etc.))
Key Facts & Figures
72 numeric metrics compared
| Metric | vLLM | Triton Inference Server | Ratio |
|---|---|---|---|
| Peak Throughput (13B model, V100)(tokens/second) | 2800 | — | — |
| Memory Usage (13B model, batch=32)(GB) | 10.5 | — | — |
| Time to First Token (p99 latency)(milliseconds) | 45 | — | — |
| Setup Time (from install to inference)(minutes) | 5 | — | — |
| GPU Platform Support Count(platforms) | 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | — | — |
| Maximum Concurrent Requests(requests) | 256 | — | — |
| Time to First Token (ms)(milliseconds) | 80-120 ms | — | — |
| Throughput (tokens/second, batch size 32)(tokens/sec) | ~1200 tok/s | — | — |
| Minimum RAM Required(GB) | 8 GB | — | — |
| GPU Memory for 7B Model(GB) | 5-6 GB (with optimization) | — | — |
| Setup Time (from download to first inference)(minutes) | 30 minutes | — | — |
| GitHub Stars(stars) | 23,000+ | 7,500+ | |
| Throughput (tokens/second, LLaMA 70B example)(tokens/sec) | 1,500+ | — | — |
| KV Cache Memory Usage Reduction(x factor) | ~4x reduction | — | — |
| GitHub Stars (community adoption metric)(stars) | 21,000+ | — | — |
| Minimum GPU Memory (LLaMA 70B, 1 GPU)(GB) | 40 GB (with PagedAttention) | — | — |
| Batch Size Improvement (via memory savings)(x multiplier) | 4x larger batches possible | — | — |
| Distributed Parallelism Setup Time(minutes to configure) | 15-30 (built-in helpers) | — | — |
| Token Throughput (A100-40GB, 7B model)(tokens/sec) | 12,500 tokens/sec | — | — |
| Memory Usage (KV cache, 7B model, batch=1)(GB) | 8.2 GB (with PagedAttention) | — | — |
| Supported Model Frameworks(count) | 2 (LLM-specific) | 6+ frameworks | |
| P99 Latency (7B model, batch=32)(milliseconds) | 380 ms | — | — |
| Production Users (Estimated)(organizations) | ~1,200+ organizations (LLM-focused) | — | — |
| GitHub Stars (as of 2026)(stars) | ~24,000 | — | — |
| Throughput (tokens/sec on A100)(tokens/second) | ~8,000-12,000 | — | — |
| Per-Token Latency (Llama 2 70B)(milliseconds) | 50-60ms | — | — |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms) | — | — |
| Pre-optimized Model Count(models) | 500+ with auto-optimization | — | — |
| Memory Usage Reduction (vs PyTorch)(percent) | 50-60% (Paged Attention) | — | — |
| Setup Time (Basic Deployment)(minutes) | 5-10 minutes | — | — |
| Inference Throughput (single A100 GPU)(tokens/second) | 25,000 tokens/sec | — | — |
| Setup Time (basic inference)(minutes) | 120-420 minutes (2-7 days with infrastructure) | — | — |
| Cost per Million Tokens (A100, on-demand)(USD) | $0.12 | — | — |
| Supported Models (major open-source)(count) | 1,000+ models | — | — |
| Enterprise SLA Uptime(percent) | Community-dependent (typically 99.0%+) | — | — |
| Community & Documentation(GitHub stars) | 25,000+ stars, weekly updates | — | — |
| LLM Throughput (tokens/sec)(tokens/sec) | ~5,000-15,000 (on A100 with batching) | ~200-500 (baseline without vLLM backend) | |
| KV Cache Memory Usage(% of baseline) | 25-45% of baseline | 100% (standard attention) | |
| Supported Model Categories(count) | LLMs only (30+ architectures) | 8+ model categories with 10+ frameworks | |
| Enterprise Maturity (years in production)(years) | 2 years (v1.0 in 2024) | 6 years (v1.0 in 2020) | |
| GPU Memory for 13B LLM(GB) | ~18-22 GB with continuous batching | ~24-28 GB standard | |
| Inference Throughput (RTX 4090, Llama 2 13B)(tokens/second) | ~700 tokens/sec | — | — |
| Memory Usage (Llama 2 7B, quantized)(GB) | ~6.5 GB | — | — |
| Installation Time (from zero)(minutes) | 25-40 minutes | — | — |
| Minimum VRAM for Llama 2 7B(GB) | 6 GB | — | — |
| Number of Supported GPU Backends(count) | 4+ (CUDA, ROCm, CPU, TPU, custom) | — | — |
| LLM Throughput Improvement(x faster than baseline) | 24x | 6x | |
| Memory Usage (KV Cache)(% reduction vs standard) | 80% reduction | 30% reduction | |
| Enterprise Deployment Features(feature count) | 3 (basic) | 8+ (advanced) | |
| Inference Throughput (LLaMA 2 70B, A100 GPU)(tokens/sec) | 25,000 tokens/sec (batch 256) | — | — |
| Memory Usage (LLaMA 2 70B)(GB) | 45 GB (with PagedAttention) | — | — |
| Deployment Time(seconds) | 20-30 minutes (self-hosted) | — | — |
| Cost per 1M Tokens (LLaMA 2 70B, On-Demand)(USD) | $0.25 (self-hosted, amortized) | — | — |
| Model Support (Open-Source LLMs)(models) | 500+ community models | — | — |
| GPU Memory Reduction vs Baseline(%) | ~60% | — | — |
| Throughput Improvement (Batching)(x improvement) | 10-23x vs standard | — | — |
| Supported Model Formats(formats) | 15+ formats (HF, GGUF, AWQ, GPTQ, etc) | — | — |
| Time to Deploy (minutes)(minutes) | 5-10 minutes for basic LLM service | 20-30 minutes with model config setup | |
| Latest Version Release Cycle(weeks) | 2-3 weeks | — | — |
| Inference Throughput (tokens/sec, Llama 2 7B)(tokens/sec) | 2,000-3,500 tokens/sec | — | — |
| Time to First Token (p50 latency)(milliseconds) | 50-150ms | — | — |
| Model Type Support Count(categories) | 1 (LLMs only) | — | — |
| Memory Usage (Llama 2 13B fp16)(GB) | 24-28 GB | — | — |
| Supported Quantization Formats(count) | GPTQ, AWQ, INT4, FP8, INT8 | — | — |
| Multi-GPU Scaling Efficiency (8 GPU)(percent) | 92% linear scaling | — | — |
| Max Concurrent Requests (typical setup)(requests) | 500-2,000 concurrent | — | — |
| Time to First Inference(minutes) | 2-3 minutes | — | — |
| Throughput (7B model, A100)(tokens/second) | 3,200 tokens/sec | — | — |
| Latency per Token (batch=1, 7B model)(milliseconds) | 45 ms | — | — |
| Model Architecture Support(count) | 30+ architectures | — | — |
| VRAM Usage (7B model, INT8)(GB) | 9-11 GB | — | — |
| GitHub Stars (2026)(stars) | 22,000+ stars | — | — |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Large Language Models onlyPrimary Use CaseMulti-model and multi-framework(winner)
- 24x baseline (with PagedAttention)(winner)Throughput for LLMs (tokens/sec)Baseline performance
- LLMs (Llama, GPT, Mistral, etc.)Supported Model TypesLLMs, CNNs, RNNs, transformers, traditional ML(winner)
- PagedAttention reduces KV cache by 55-75%(winner)Memory EfficiencyStandard attention mechanisms
- Minimal, LLM-specific configuration(winner)Setup ComplexityModerate, requires model config files
- Tensor parallelism, pipeline parallelismMulti-GPU DistributionMultiple backends (vLLM, TensorRT, PyTorch)(winner)
- Stable since v0.4 (2024), strong LLM ecosystemProduction MaturityMature since 2020, enterprise-tested
- Primary Use Case
vLLM
Large Language Models only
Triton Inference Server
Multi-model and multi-framework(winner)
- Throughput for LLMs (tokens/sec)
vLLM
24x baseline (with PagedAttention)(winner)
Triton Inference Server
Baseline performance
- Supported Model Types
vLLM
LLMs (Llama, GPT, Mistral, etc.)
Triton Inference Server
LLMs, CNNs, RNNs, transformers, traditional ML(winner)
- Memory Efficiency
vLLM
PagedAttention reduces KV cache by 55-75%(winner)
Triton Inference Server
Standard attention mechanisms
- Setup Complexity
vLLM
Minimal, LLM-specific configuration(winner)
Triton Inference Server
Moderate, requires model config files
- Multi-GPU Distribution
vLLM
Tensor parallelism, pipeline parallelism
Triton Inference Server
Multiple backends (vLLM, TensorRT, PyTorch)(winner)
- Production Maturity
vLLM
Stable since v0.4 (2024), strong LLM ecosystem
Triton Inference Server
Mature since 2020, enterprise-tested
Full Comparison
| Attribute | vLLM | Triton Inference Server |
|---|---|---|
| Peak Throughput (13B model, V100)(tokens/second) | 2800 | — |
| Time to First Token (p99 latency)(milliseconds) | 45 | — |
| Time to First Token (ms)(milliseconds) | 80-120 ms | — |
| Throughput (tokens/second, batch size 32)(tokens/sec) | ~1200 tok/s | — |
| Throughput (tokens/second, LLaMA 70B example)(tokens/sec) | 1,500+ | — |
Show 18 more attributesToken Throughput (A100-40GB, 7B model)(tokens/sec) 12,500 tokens/sec — P99 Latency (7B model, batch=32)(milliseconds) 380 ms — Throughput (tokens/sec on A100)(tokens/second) ~8,000-12,000 — Per-Token Latency (Llama 2 70B)(milliseconds) 50-60ms — Inference Throughput (single A100 GPU)(tokens/second) 25,000 tokens/sec — LLM Throughput (tokens/sec)(tokens/sec) ~5,000-15,000 (on A100 with batching) ~200-500 (baseline without vLLM backend) Inference Throughput (RTX 4090, Llama 2 13B)(tokens/second) ~700 tokens/sec — LLM Throughput Improvement(x faster than baseline) 24x 6x Memory Usage (KV Cache)(% reduction vs standard) 80% reduction 30% reduction Inference Throughput (LLaMA 2 70B, A100 GPU)(tokens/sec) 25,000 tokens/sec (batch 256) — Memory Usage (LLaMA 2 70B)(GB) 45 GB (with PagedAttention) — Deployment Time(seconds) 20-30 minutes (self-hosted) — GPU Memory Reduction vs Baseline(%) ~60% — Throughput Improvement (Batching)(x improvement) 10-23x vs standard — Inference Throughput (tokens/sec, Llama 2 7B)(tokens/sec) 2,000-3,500 tokens/sec — Time to First Token (p50 latency)(milliseconds) 50-150ms — Throughput (7B model, A100)(tokens/second) 3,200 tokens/sec — Latency per Token (batch=1, 7B model)(milliseconds) 45 ms — | ||
| Memory Usage (13B model, batch=32)(GB) | 10.5 | — |
| VRAM Usage (7B model, INT8)(GB) | 9-11 GB | — |
| Setup Time (from install to inference)(minutes) | 5 | — |
| Setup Time (Basic Deployment)(minutes) | 5-10 minutes | — |
| Kubernetes Native Support(boolean) | Community Helm charts available | Official Helm charts and auto-scaling |
| Time to First Inference(minutes) | 2-3 minutes | — |
| Installation Complexity(shell commands) | 1-2 commands | — |
| GPU Platform Support Count(platforms) | 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | — |
| Supported ML Frameworks(count) | Primarily PyTorch/Transformers (limited) | — |
| Supported Model Frameworks(count) | 2 (LLM-specific) | 6+ frameworks(winner) |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms) | — |
| Number of Supported GPU Backends(count) | 4+ (CUDA, ROCm, CPU, TPU, custom) | — |
Show 2 more attributesSupported Model Formats(formats) 15+ formats (HF, GGUF, AWQ, GPTQ, etc) — Model Architecture Support(count) 30+ architectures — | ||
| Maximum Concurrent Requests(requests) | 256 | — |
| Batch Size Improvement (via memory savings)(x multiplier) | 4x larger batches possible | — |
| Multi-GPU Support(scaling efficiency) | Native (tensor parallelism) | Supported (requires config) |
| Multi-GPU Scaling Efficiency (8 GPU)(percent) | 92% linear scaling | — |
| Minimum RAM Required(GB) | 8 GB | — |
| GPU Memory for 7B Model(GB) | 5-6 GB (with optimization) | — |
| Minimum GPU Memory (LLaMA 70B, 1 GPU)(GB) | 40 GB (with PagedAttention) | — |
| Minimum VRAM for Llama 2 7B(GB) | 6 GB | — |
| Setup Time (from download to first inference)(minutes) | 30 minutes | — |
| Configuration Complexity(1-10 scale) | Low (Python API) | High (YAML+Protobuf) |
| Pre-packaged Models Available(count) | Unlimited (HuggingFace) | — |
| Pre-optimized Model Count(models) | 500+ with auto-optimization | — |
| GitHub Stars(stars) | 23,000+(winner) | 7,500+ |
| GitHub Stars (community adoption metric)(stars) | 21,000+ | — |
| GitHub Stars (as of 2026)(stars) | ~24,000 | — |
| GitHub Stars (2026)(stars) | 22,000+ stars | — |
| CPU Fallback Support(capability) | Limited, requires GPU | — |
| KV Cache Memory Usage Reduction(x factor) | ~4x reduction | — |
| KV Cache Memory Usage(% of baseline) | 25-45% of baseline(winner) | 100% (standard attention) |
| Multi-Model Serving Setup Complexity(complexity level) | High (requires separate instances) | — |
| Setup Time (basic inference)(minutes) | 120-420 minutes (2-7 days with infrastructure) | — |
| Installation Time (from zero)(minutes) | 25-40 minutes | — |
| Time to Deploy (minutes)(minutes) | 5-10 minutes for basic LLM service(winner) | 20-30 minutes with model config setup |
| Distributed Parallelism Setup Time(minutes to configure) | 15-30 (built-in helpers) | — |
| OpenAI API Compatibility(boolean) | Native support, drop-in replacement | Requires wrapper/plugin |
| Memory Usage (KV cache, 7B model, batch=1)(GB) | 8.2 GB (with PagedAttention) | — |
| Memory Usage Reduction (vs PyTorch)(percent) | 50-60% (Paged Attention) | — |
| Memory Usage (Llama 2 13B fp16)(GB) | 24-28 GB | — |
| Model Ensemble Support(boolean) | No native ensemble; requires external orchestration | — |
| Training Capabilities | Inference-only, no native training | — |
| Batch Processing Support(null) | Yes (native continuous batching) | — |
| Token Streaming Native Support(boolean) | Via API wrapper | — |
| Supported Quantization Formats(count) | GPTQ, AWQ, INT4, FP8, INT8 | — |
Show 1 more attributeQuantization Method Support(count) 5 methods (GPTQ, AWQ, SqueezeLLM, etc.) — | ||
| Production Users (Estimated)(organizations) | ~1,200+ organizations (LLM-focused) | — |
| Cost(USD) | Free (open-source) | — |
| Cost per Million Tokens (A100, on-demand)(USD) | $0.12 | — |
| Cost per 1M Tokens (LLaMA 2 70B, On-Demand)(USD) | $0.25 (self-hosted, amortized) | — |
| Supported Models (major open-source)(count) | 1,000+ models | — |
| Enterprise SLA Uptime(percent) | Community-dependent (typically 99.0%+) | — |
| SLA Availability Guarantee(%) | No SLA (community support) | — |
| Infrastructure Management | User-managed (CUDA, Docker, scaling) | — |
| Production Monitoring(metrics exported) | Basic (throughput, latency) | Advanced (Prometheus, tracing) |
| Infrastructure Management Required(null) | User-managed (Docker, K8s, VMs) | — |
| Community & Documentation(GitHub stars) | 25,000+ stars, weekly updates | — |
| Official Enterprise Support(boolean) | Community-based | — |
| Supported Model Categories(count) | LLMs only (30+ architectures) | 8+ model categories with 10+ frameworks(winner) |
| Model Support (Open-Source LLMs)(models) | 500+ community models | — |
| Model Type Support Count(categories) | 1 (LLMs only) | — |
| Enterprise Maturity (years in production)(years) | 2 years (v1.0 in 2024) | 6 years (v1.0 in 2020)(winner) |
| GPU Memory for 13B LLM(GB) | ~18-22 GB with continuous batching(winner) | ~24-28 GB standard |
| Memory Usage (Llama 2 7B, quantized)(GB) | ~6.5 GB | — |
| API Standardization(null) | OpenAI-compatible API | — |
| Enterprise Deployment Features(feature count) | 3 (basic) | 8+ (advanced)(winner) |
| Enterprise Support Availability | Community (GitHub issues) | — |
| Latest Version Release Cycle(weeks) | 2-3 weeks | — |
| Max Concurrent Requests (typical setup)(requests) | 500-2,000 concurrent | — |
vLLMShow 18 more attributes
Show 2 more attributes
Show 1 more attribute
Pros & Cons
10 pros·4 cons across both
vLLM
Pros
- PagedAttention reduces KV cache memory by 55-75%, enabling longer context windows
- 24x higher throughput than baseline implementations through continuous batching
- Sub-millisecond latency with dynamic batching for individual requests
- Supports 30+ LLM architectures (Llama 2/3, Mistral, Qwen, Baichuan, etc.)
- OpenAI-compatible REST API requires zero model code changes
Cons
- LLM-only focus; cannot serve vision models, recommendation systems, or classical ML
- GPU-centric design; CPU inference support is limited
Triton Inference Server
Pros
- Supports 8+ backends: TensorRT, PyTorch, ONNX Runtime, TensorFlow, vLLM, and more
- Unified API for heterogeneous models (LLMs, CNNs, NLP, recommenders, classical ML)
- Ensemble pipeline support for multi-model workflows (e.g., embedding → ranking → re-ranking)
- Native Kubernetes integration with auto-scaling and health probes
- Model versioning and A/B testing built-in
Cons
- Configuration overhead for each model (model.pbtxt files required)
- Lower LLM throughput than vLLM when used standalone without vLLM backend
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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