vLLM vs Triton Inference Server
type: Article
description: 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.
language: en-US
date published: 2026-07-09T12:32:14.660Z
date created: 2026-07-09T12:32:14.660Z
date modified: 2026-07-09T12:32:15.283Z
content reference time: 2026-07-09T12:32:15.283Z
author: A Versus B
publisher: A Versus B
about: vLLM
about: Triton Inference Server
abstract: 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.
text: Primary Use Case. Throughput for LLMs (tokens/sec). Supported Model Types. Memory Efficiency. Setup Complexity. Multi-GPU Distribution. Production Maturity
conclusion text: 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.
additional property: Peak Throughput (13B model, V100)
  type: PropertyValue
  value: vLLM: 2800 | Triton Inference Server: N/A
additional property: Memory Usage (13B model, batch=32)
  type: PropertyValue
  value: vLLM: 10.5 | Triton Inference Server: N/A
additional property: Time to First Token (p99 latency)
  type: PropertyValue
  value: vLLM: 45 | Triton Inference Server: N/A
additional property: Setup Time (from install to inference)
  type: PropertyValue
  value: vLLM: 5 | Triton Inference Server: N/A
additional property: GPU Platform Support Count
  type: PropertyValue
  value: vLLM: 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | Triton Inference Server: N/A
additional property: Maximum Concurrent Requests
  type: PropertyValue
  value: vLLM: 256 | Triton Inference Server: N/A
additional property: Time to First Token (ms)
  type: PropertyValue
  value: vLLM: 80-120 ms | Triton Inference Server: N/A
additional property: Throughput (tokens/second, batch size 32)
  type: PropertyValue
  value: vLLM: ~1200 tok/s | Triton Inference Server: N/A
additional property: Minimum RAM Required
  type: PropertyValue
  value: vLLM: 8 GB | Triton Inference Server: N/A
additional property: GPU Memory for 7B Model
  type: PropertyValue
  value: vLLM: 5-6 GB (with optimization) | Triton Inference Server: N/A
additional property: Setup Time (from download to first inference)
  type: PropertyValue
  value: vLLM: 30 minutes | Triton Inference Server: N/A
additional property: Pre-packaged Models Available
  type: PropertyValue
  value: vLLM: Unlimited (HuggingFace) | Triton Inference Server: N/A
additional property: GitHub Stars
  type: PropertyValue
  value: vLLM: 23,000+ | Triton Inference Server: 7,500+
  value reference: vLLM
additional property: CPU Fallback Support
  type: PropertyValue
  value: vLLM: Limited, requires GPU | Triton Inference Server: N/A
additional property: Throughput (tokens/second, LLaMA 70B example)
  type: PropertyValue
  value: vLLM: 1,500+ | Triton Inference Server: N/A
additional property: KV Cache Memory Usage Reduction
  type: PropertyValue
  value: vLLM: ~4x reduction | Triton Inference Server: N/A
additional property: Supported ML Frameworks
  type: PropertyValue
  value: vLLM: Primarily PyTorch/Transformers (limited) | Triton Inference Server: N/A
additional property: GitHub Stars (community adoption metric)
  type: PropertyValue
  value: vLLM: 21,000+ | Triton Inference Server: N/A
additional property: Minimum GPU Memory (LLaMA 70B, 1 GPU)
  type: PropertyValue
  value: vLLM: 40 GB (with PagedAttention) | Triton Inference Server: N/A
additional property: Multi-Model Serving Setup Complexity
  type: PropertyValue
  value: vLLM: High (requires separate instances) | Triton Inference Server: N/A
additional property: Batch Size Improvement (via memory savings)
  type: PropertyValue
  value: vLLM: 4x larger batches possible | Triton Inference Server: N/A
additional property: Distributed Parallelism Setup Time
  type: PropertyValue
  value: vLLM: 15-30 (built-in helpers) | Triton Inference Server: N/A
additional property: Token Throughput (A100-40GB, 7B model)
  type: PropertyValue
  value: vLLM: 12,500 tokens/sec | Triton Inference Server: N/A
additional property: Memory Usage (KV cache, 7B model, batch=1)
  type: PropertyValue
  value: vLLM: 8.2 GB (with PagedAttention) | Triton Inference Server: N/A
additional property: Supported Model Frameworks
  type: PropertyValue
  value: vLLM: 2 (LLM-specific) | Triton Inference Server: 6+ frameworks
  value reference: Triton Inference Server
additional property: P99 Latency (7B model, batch=32)
  type: PropertyValue
  value: vLLM: 380 ms | Triton Inference Server: N/A
additional property: Configuration Complexity
  type: PropertyValue
  value: vLLM: Low (Python API) | Triton Inference Server: High (YAML+Protobuf)
additional property: Model Ensemble Support
  type: PropertyValue
  value: vLLM: No native ensemble; requires external orchestration | Triton Inference Server: N/A
additional property: Production Users (Estimated)
  type: PropertyValue
  value: vLLM: ~1,200+ organizations (LLM-focused) | Triton Inference Server: N/A
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: vLLM: ~24,000 | Triton Inference Server: N/A
additional property: Throughput (tokens/sec on A100)
  type: PropertyValue
  value: vLLM: ~8,000-12,000 | Triton Inference Server: N/A
additional property: Per-Token Latency (Llama 2 70B)
  type: PropertyValue
  value: vLLM: 50-60ms | Triton Inference Server: N/A
additional property: Supported GPU Platforms
  type: PropertyValue
  value: vLLM: NVIDIA, AMD, Intel, CPU (4 platforms) | Triton Inference Server: N/A
additional property: Pre-optimized Model Count
  type: PropertyValue
  value: vLLM: 500+ with auto-optimization | Triton Inference Server: N/A
additional property: Memory Usage Reduction (vs PyTorch)
  type: PropertyValue
  value: vLLM: 50-60% (Paged Attention) | Triton Inference Server: N/A
additional property: Setup Time (Basic Deployment)
  type: PropertyValue
  value: vLLM: 5-10 minutes | Triton Inference Server: N/A
additional property: Cost
  type: PropertyValue
  value: vLLM: Free (open-source) | Triton Inference Server: N/A
additional property: Inference Throughput (single A100 GPU)
  type: PropertyValue
  value: vLLM: 25,000 tokens/sec | Triton Inference Server: N/A
additional property: Setup Time (basic inference)
  type: PropertyValue
  value: vLLM: 120-420 minutes (2-7 days with infrastructure) | Triton Inference Server: N/A
additional property: Cost per Million Tokens (A100, on-demand)
  type: PropertyValue
  value: vLLM: $0.12 | Triton Inference Server: N/A
additional property: Supported Models (major open-source)
  type: PropertyValue
  value: vLLM: 1,000+ models | Triton Inference Server: N/A
additional property: Training Capabilities
  type: PropertyValue
  value: vLLM: Inference-only, no native training | Triton Inference Server: N/A
additional property: Enterprise SLA Uptime
  type: PropertyValue
  value: vLLM: Community-dependent (typically 99.0%+) | Triton Inference Server: N/A
additional property: Infrastructure Management
  type: PropertyValue
  value: vLLM: User-managed (CUDA, Docker, scaling) | Triton Inference Server: N/A
additional property: Community & Documentation
  type: PropertyValue
  value: vLLM: 25,000+ stars, weekly updates | Triton Inference Server: N/A
additional property: LLM Throughput (tokens/sec)
  type: PropertyValue
  value: vLLM: ~5,000-15,000 (on A100 with batching) | Triton Inference Server: ~200-500 (baseline without vLLM backend)
  value reference: vLLM
additional property: KV Cache Memory Usage
  type: PropertyValue
  value: vLLM: 25-45% of baseline | Triton Inference Server: 100% (standard attention)
  value reference: vLLM
additional property: Supported Model Categories
  type: PropertyValue
  value: vLLM: LLMs only (30+ architectures) | Triton Inference Server: 8+ model categories with 10+ frameworks
  value reference: Triton Inference Server
additional property: OpenAI API Compatibility
  type: PropertyValue
  value: vLLM: Native support, drop-in replacement | Triton Inference Server: Requires wrapper/plugin
interactivity type: active
significant link: https://www.aversusb.net/entity/vllm
significant link: https://www.aversusb.net/alternatives/vllm
significant link: https://www.aversusb.net/entity/triton-inference-server
significant link: https://www.aversusb.net/alternatives/triton-inference-server
significant link: https://www.aversusb.net/api/answer/vllm-vs-triton))
significant link: https://www.aversusb.net/api/v1/schema/vllm-vs-triton))
related link: https://www.aversusb.net/compare/vllm-vs-triton)
related link: https://www.aversusb.net/compare/ollama-vs-vllm
related link: https://www.aversusb.net/compare/vllm-vs-ray-serve
related link: https://www.aversusb.net/compare/vllm-vs-triton
related link: https://www.aversusb.net/compare/vllm-vs-tensorrt-llm
related link: https://www.aversusb.net/compare/vllm-vs-sagemaker
related link: https://www.aversusb.net/compare/ollama-vs-vllm)
related link: https://www.aversusb.net/compare/vllm-vs-tgi)
teaches: How to choose between vLLM and Triton Inference Server
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: vLLM vs Triton Inference Server — Side-by-side comparison on A Versus B

A Versus B
type: Organization

vLLM
type: SoftwareApplication
alternate name: Vllm
language: en-US
description: High-throughput LLM inference engine with paged attention and easy-to-use Python API.
image: ImageObject
  caption: vLLM
subject of: vLLM vs Triton Inference Server
image: ImageObject
  caption: vLLM

Triton Inference Server
type: SoftwareApplication
alternate name: TIS
language: en-US
description: General-purpose inference server supporting multiple frameworks and model types
subject of: vLLM vs Triton Inference Server

vLLM vs Triton Inference Server — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for vLLM vs Triton Inference Server. Contains 94 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: Peak Throughput (13B model, V100)
  type: PropertyValue
  value reference: Performance
variable measured: Memory Usage (13B model, batch=32)
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Time to First Token (p99 latency)
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time (from install to inference)
  type: PropertyValue
  value reference: Deployment
variable measured: GPU Platform Support Count
  type: PropertyValue
  value reference: Compatibility
variable measured: Maximum Concurrent Requests
  type: PropertyValue
  value reference: Scalability
variable measured: Time to First Token (ms)
  type: PropertyValue
  value reference: Performance
variable measured: Throughput (tokens/second, batch size 32)
  type: PropertyValue
  value reference: Performance
variable measured: Minimum RAM Required
  type: PropertyValue
  value reference: System Requirements
variable measured: GPU Memory for 7B Model
  type: PropertyValue
  value reference: Hardware Requirements
variable measured: Setup Time (from download to first inference)
  type: PropertyValue
  value reference: Usability
variable measured: Pre-packaged Models Available
  type: PropertyValue
  value reference: Model Support
variable measured: GitHub Stars
  type: PropertyValue
  value reference: Community
variable measured: CPU Fallback Support
  type: PropertyValue
  value reference: Hardware Compatibility
variable measured: Throughput (tokens/second, LLaMA 70B example)
  type: PropertyValue
  value reference: Performance
variable measured: KV Cache Memory Usage Reduction
  type: PropertyValue
  value reference: Memory Efficiency
variable measured: Supported ML Frameworks
  type: PropertyValue
  value reference: Integrations
variable measured: GitHub Stars (community adoption metric)
  type: PropertyValue
  value reference: Community
variable measured: Minimum GPU Memory (LLaMA 70B, 1 GPU)
  type: PropertyValue
  value reference: Hardware Requirements
variable measured: Multi-Model Serving Setup Complexity
  type: PropertyValue
  value reference: Ease of Use
variable measured: Batch Size Improvement (via memory savings)
  type: PropertyValue
  value reference: Scalability
variable measured: Distributed Parallelism Setup Time
  type: PropertyValue
  value reference: Developer Experience
variable measured: Token Throughput (A100-40GB, 7B model)
  type: PropertyValue
  value reference: Performance
variable measured: Memory Usage (KV cache, 7B model, batch=1)
  type: PropertyValue
  value reference: Efficiency
variable measured: Supported Model Frameworks
  type: PropertyValue
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variable measured: P99 Latency (7B model, batch=32)
  type: PropertyValue
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variable measured: Configuration Complexity
  type: PropertyValue
  value reference: Operational
variable measured: Model Ensemble Support
  type: PropertyValue
  value reference: Features
variable measured: Production Users (Estimated)
  type: PropertyValue
  value reference: Enterprise Adoption
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community
variable measured: Throughput (tokens/sec on A100)
  type: PropertyValue
  value reference: Performance
variable measured: Per-Token Latency (Llama 2 70B)
  type: PropertyValue
  value reference: Performance
variable measured: Supported GPU Platforms
  type: PropertyValue
  value reference: Compatibility
variable measured: Pre-optimized Model Count
  type: PropertyValue
  value reference: Model Support
variable measured: Memory Usage Reduction (vs PyTorch)
  type: PropertyValue
  value reference: Efficiency
variable measured: Setup Time (Basic Deployment)
  type: PropertyValue
  value reference: Deployment
variable measured: Cost
  type: PropertyValue
  value reference: Economics
variable measured: Inference Throughput (single A100 GPU)
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time (basic inference)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Cost per Million Tokens (A100, on-demand)
  type: PropertyValue
  value reference: Cost
variable measured: Supported Models (major open-source)
  type: PropertyValue
  value reference: Ecosystem
variable measured: Training Capabilities
  type: PropertyValue
  value reference: Features
variable measured: Enterprise SLA Uptime
  type: PropertyValue
  value reference: Reliability
variable measured: Infrastructure Management
  type: PropertyValue
  value reference: Operations
variable measured: Community & Documentation
  type: PropertyValue
  value reference: Support
variable measured: LLM Throughput (tokens/sec)
  type: PropertyValue
  value reference: Performance
variable measured: KV Cache Memory Usage
  type: PropertyValue
  value reference: Memory Efficiency
variable measured: Supported Model Categories
  type: PropertyValue
  value reference: Flexibility
variable measured: OpenAI API Compatibility
  type: PropertyValue
  value reference: Developer Experience
variable measured: Enterprise Maturity (years in production)
  type: PropertyValue
  value reference: Stability
variable measured: GPU Memory for 13B LLM
  type: PropertyValue
  value reference: Resource Requirements
variable measured: Kubernetes Native Support
  type: PropertyValue
  value reference: Deployment
variable measured: Inference Throughput (RTX 4090, Llama 2 13B)
  type: PropertyValue
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variable measured: Memory Usage (Llama 2 7B, quantized)
  type: PropertyValue
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variable measured: Installation Time (from zero)
  type: PropertyValue
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variable measured: Minimum VRAM for Llama 2 7B
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variable measured: Number of Supported GPU Backends
  type: PropertyValue
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variable measured: Batch Processing Support
  type: PropertyValue
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variable measured: API Standardization
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variable measured: LLM Throughput Improvement
  type: PropertyValue
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variable measured: Memory Usage (KV Cache)
variable measured: Enterprise Deployment Features
variable measured: Multi-GPU Support
variable measured: Production Monitoring
variable measured: Inference Throughput (LLaMA 2 70B, A100 GPU)
variable measured: Memory Usage (LLaMA 2 70B)
variable measured: Deployment Time
variable measured: Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
variable measured: Model Support (Open-Source LLMs)
is based on: https://www.aversusb.net/compare/vllm-vs-triton))

vLLM vs Triton Inference Server — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for vLLM vs Triton Inference Server.
has defined term: Performance
has defined term: Resource Efficiency
has defined term: Deployment
has defined term: Compatibility
has defined term: Scalability
has defined term: System Requirements
has defined term: Hardware Requirements
has defined term: Usability
has defined term: Model Support
has defined term: Community
has defined term: Hardware Compatibility
has defined term: Memory Efficiency
has defined term: Integrations
has defined term: Ease of Use
has defined term: Developer Experience
has defined term: Efficiency
has defined term: Operational
has defined term: Features
has defined term: Enterprise Adoption
has defined term: Economics
has defined term: Cost
has defined term: Ecosystem
has defined term: Reliability
has defined term: Operations
has defined term: Support
has defined term: Flexibility
has defined term: Stability
has defined term: Resource Requirements
has defined term: Resource Usage
has defined term: Integration
has defined term: Enterprise
has defined term: Support & Licensing
has defined term: Maintenance
has defined term: Capacity

Performance
type: DefinedTerm
term code: performance
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

System Requirements
type: DefinedTerm
term code: system-requirements
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Hardware Requirements
type: DefinedTerm
term code: hardware-requirements
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Model Support
type: DefinedTerm
term code: model-support
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Hardware Compatibility
type: DefinedTerm
term code: hardware-compatibility
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Memory Efficiency
type: DefinedTerm
term code: memory-efficiency
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Integrations
type: DefinedTerm
term code: integrations
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Efficiency
type: DefinedTerm
term code: efficiency
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Operational
type: DefinedTerm
term code: operational
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Enterprise Adoption
type: DefinedTerm
term code: enterprise-adoption
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Economics
type: DefinedTerm
term code: economics
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Stability
type: DefinedTerm
term code: stability
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Resource Requirements
type: DefinedTerm
term code: resource-requirements
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Resource Usage
type: DefinedTerm
term code: resource-usage
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Enterprise
type: DefinedTerm
term code: enterprise
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Support & Licensing
type: DefinedTerm
term code: support-&-licensing
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Maintenance
type: DefinedTerm
term code: maintenance
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

Capacity
type: DefinedTerm
term code: capacity
in defined term set: vLLM vs Triton Inference Server — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T12:32:15.283Z
author: A Versus B
publisher: A Versus B
is part of: vllm-vs-triton))
main entity: Can I use vLLM as a backend in Triton?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Does vLLM support vision-language models?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: What's the latency difference between vLLM and Triton for a single request?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which should I use for an LLM-only service?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can I do multi-model inference with vLLM?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US

How to Choose Between vLLM and Triton Inference Server
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: Primary Use Case
step: Consider: Throughput for LLMs (tokens/sec)
step: Consider: Supported Model Types
step: Consider: Memory Efficiency
step: Consider: Setup Complexity
step: Consider: Multi-GPU Distribution
step: Consider: Production Maturity
tool: A Versus B Comparison Guide
tip: HowToTip
  text: 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.

Consider: Primary Use Case
type: HowToStep
position: 1
text: 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)
type: HowToStep
position: 2
text: Throughput for LLMs (tokens/sec): vLLM has 24x baseline (with PagedAttention), while Triton Inference Server has Baseline performance.

Consider: Supported Model Types
type: HowToStep
position: 3
text: Supported Model Types: vLLM has LLMs (Llama, GPT, Mistral, etc.), while Triton Inference Server has LLMs, CNNs, RNNs, transformers, traditional ML.

Consider: Memory Efficiency
type: HowToStep
position: 4
text: Memory Efficiency: vLLM has PagedAttention reduces KV cache by 55-75%, while Triton Inference Server has Standard attention mechanisms.

Consider: Setup Complexity
type: HowToStep
position: 5
text: Setup Complexity: vLLM has Minimal, LLM-specific configuration, while Triton Inference Server has Moderate, requires model config files.

Consider: Multi-GPU Distribution
type: HowToStep
position: 6
text: Multi-GPU Distribution: vLLM has Tensor parallelism, pipeline parallelism, while Triton Inference Server has Multiple backends (vLLM, TensorRT, PyTorch).

Consider: Production Maturity
type: HowToStep
position: 7
text: Production Maturity: vLLM has Stable since v0.4 (2024), strong LLM ecosystem, while Triton Inference Server has Mature since 2020, enterprise-tested.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: vLLM vs Triton Inference Server
review body: 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.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: vLLM vs Triton Inference Server

Enterprise Maturity (years in production)
type: PropertyValue
value: vLLM: 2 years (v1.0 in 2024) | Triton Inference Server: 6 years (v1.0 in 2020)
value reference: Triton Inference Server

GPU Memory for 13B LLM
type: PropertyValue
value: vLLM: ~18-22 GB with continuous batching | Triton Inference Server: ~24-28 GB standard
value reference: vLLM

Kubernetes Native Support
type: PropertyValue
value: vLLM: Community Helm charts available | Triton Inference Server: Official Helm charts and auto-scaling

Inference Throughput (RTX 4090, Llama 2 13B)
type: PropertyValue
value: vLLM: ~700 tokens/sec | Triton Inference Server: N/A

Memory Usage (Llama 2 7B, quantized)
type: PropertyValue
value: vLLM: ~6.5 GB | Triton Inference Server: N/A

Installation Time (from zero)
type: PropertyValue
value: vLLM: 25-40 minutes | Triton Inference Server: N/A

Minimum VRAM for Llama 2 7B
type: PropertyValue
value: vLLM: 6 GB | Triton Inference Server: N/A

Number of Supported GPU Backends
type: PropertyValue
value: vLLM: 4+ (CUDA, ROCm, CPU, TPU, custom) | Triton Inference Server: N/A

Batch Processing Support
type: PropertyValue
value: vLLM: Yes (native continuous batching) | Triton Inference Server: N/A

API Standardization
type: PropertyValue
value: vLLM: OpenAI-compatible API | Triton Inference Server: N/A

LLM Throughput Improvement
type: PropertyValue
value: vLLM: 24x | Triton Inference Server: 6x
value reference: vLLM

Memory Usage (KV Cache)
type: PropertyValue
value: vLLM: 80% reduction | Triton Inference Server: 30% reduction
value reference: vLLM

Enterprise Deployment Features
type: PropertyValue
value: vLLM: 3 (basic) | Triton Inference Server: 8+ (advanced)
value reference: Triton Inference Server

Multi-GPU Support
type: PropertyValue
value: vLLM: Native (tensor parallelism) | Triton Inference Server: Supported (requires config)

Production Monitoring
type: PropertyValue
value: vLLM: Basic (throughput, latency) | Triton Inference Server: Advanced (Prometheus, tracing)

Inference Throughput (LLaMA 2 70B, A100 GPU)
type: PropertyValue
value: vLLM: 25,000 tokens/sec (batch 256) | Triton Inference Server: N/A

Memory Usage (LLaMA 2 70B)
type: PropertyValue
value: vLLM: 45 GB (with PagedAttention) | Triton Inference Server: N/A

Deployment Time
type: PropertyValue
value: vLLM: 20-30 minutes (self-hosted) | Triton Inference Server: N/A

Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
type: PropertyValue
value: vLLM: $0.25 (self-hosted, amortized) | Triton Inference Server: N/A

Model Support (Open-Source LLMs)
type: PropertyValue
value: vLLM: 500+ community models | Triton Inference Server: N/A

Infrastructure Management Required
type: PropertyValue
value: vLLM: User-managed (Docker, K8s, VMs) | Triton Inference Server: N/A

SLA Availability Guarantee
type: PropertyValue
value: vLLM: No SLA (community support) | Triton Inference Server: N/A

Enterprise Support Availability
type: PropertyValue
value: vLLM: Community (GitHub issues) | Triton Inference Server: N/A

GPU Memory Reduction vs Baseline
type: PropertyValue
value: vLLM: ~60% | Triton Inference Server: N/A

Throughput Improvement (Batching)
type: PropertyValue
value: vLLM: 10-23x vs standard | Triton Inference Server: N/A

Supported Model Formats
type: PropertyValue
value: vLLM: 15+ formats (HF, GGUF, AWQ, GPTQ, etc) | Triton Inference Server: N/A

Time to Deploy (minutes)
type: PropertyValue
value: vLLM: 5-10 minutes for basic LLM service | Triton Inference Server: 20-30 minutes with model config setup
value reference: vLLM

Token Streaming Native Support
type: PropertyValue
value: vLLM: Via API wrapper | Triton Inference Server: N/A

Official Enterprise Support
type: PropertyValue
value: vLLM: Community-based | Triton Inference Server: N/A

Latest Version Release Cycle
type: PropertyValue
value: vLLM: 2-3 weeks | Triton Inference Server: N/A

Inference Throughput (tokens/sec, Llama 2 7B)
type: PropertyValue
value: vLLM: 2,000-3,500 tokens/sec | Triton Inference Server: N/A

Time to First Token (p50 latency)
type: PropertyValue
value: vLLM: 50-150ms | Triton Inference Server: N/A

Model Type Support Count
type: PropertyValue
value: vLLM: 1 (LLMs only) | Triton Inference Server: N/A

Memory Usage (Llama 2 13B fp16)
type: PropertyValue
value: vLLM: 24-28 GB | Triton Inference Server: N/A

Supported Quantization Formats
type: PropertyValue
value: vLLM: GPTQ, AWQ, INT4, FP8, INT8 | Triton Inference Server: N/A

Multi-GPU Scaling Efficiency (8 GPU)
type: PropertyValue
value: vLLM: 92% linear scaling | Triton Inference Server: N/A

Max Concurrent Requests (typical setup)
type: PropertyValue
value: vLLM: 500-2,000 concurrent | Triton Inference Server: N/A

Time to First Inference
type: PropertyValue
value: vLLM: 2-3 minutes | Triton Inference Server: N/A

Throughput (7B model, A100)
type: PropertyValue
value: vLLM: 3,200 tokens/sec | Triton Inference Server: N/A

Latency per Token (batch=1, 7B model)
type: PropertyValue
value: vLLM: 45 ms | Triton Inference Server: N/A

Model Architecture Support
type: PropertyValue
value: vLLM: 30+ architectures | Triton Inference Server: N/A

Installation Complexity
type: PropertyValue
value: vLLM: 1-2 commands | Triton Inference Server: N/A

VRAM Usage (7B model, INT8)
type: PropertyValue
value: vLLM: 9-11 GB | Triton Inference Server: N/A

GitHub Stars (2026)
type: PropertyValue
value: vLLM: 22,000+ stars | Triton Inference Server: N/A

Quantization Method Support
type: PropertyValue
value: vLLM: 5 methods (GPTQ, AWQ, SqueezeLLM, etc.) | Triton Inference Server: N/A

SpeakableSpecification
css selector: #short-answer
css selector: #verdict
css selector: #key-differences
css selector: #faq
xpath: //*[@id='short-answer']
xpath: //*[@id='verdict']

vLLM — Wikipedia
type: CreativeWork

Triton Inference Server — Wikipedia
type: CreativeWork

Compare vLLM vs Triton Inference Server
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/vllm-vs-triton))
object: vLLM
object: Triton Inference Server

Memory Usage (KV Cache)
type: PropertyValue
value reference: Performance

Enterprise Deployment Features
type: PropertyValue
value reference: Enterprise

Multi-GPU Support
type: PropertyValue
value reference: Scalability

Production Monitoring
type: PropertyValue
value reference: Operations

Inference Throughput (LLaMA 2 70B, A100 GPU)
type: PropertyValue
value reference: Performance

Memory Usage (LLaMA 2 70B)
type: PropertyValue
value reference: Performance

Deployment Time
type: PropertyValue
value reference: Performance

Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
type: PropertyValue
value reference: Cost

Model Support (Open-Source LLMs)
type: PropertyValue
value reference: Flexibility

Infrastructure Management Required
type: PropertyValue
value reference: Operations

SLA Availability Guarantee
type: PropertyValue
value reference: Reliability

Enterprise Support Availability
type: PropertyValue
value reference: Support & Licensing

GPU Memory Reduction vs Baseline
type: PropertyValue
value reference: Performance

Throughput Improvement (Batching)
type: PropertyValue
value reference: Performance

Supported Model Formats
type: PropertyValue
value reference: Compatibility

Time to Deploy (minutes)
type: PropertyValue
value reference: Ease of Use

Token Streaming Native Support
type: PropertyValue
value reference: Features

Official Enterprise Support
type: PropertyValue
value reference: Support

Latest Version Release Cycle
type: PropertyValue
value reference: Maintenance

Inference Throughput (tokens/sec, Llama 2 7B)
type: PropertyValue
value reference: Performance

Time to First Token (p50 latency)
type: PropertyValue
value reference: Performance

Model Type Support Count
type: PropertyValue
value reference: Flexibility

Memory Usage (Llama 2 13B fp16)
type: PropertyValue
value reference: Efficiency

Supported Quantization Formats
type: PropertyValue
value reference: Features

Multi-GPU Scaling Efficiency (8 GPU)
type: PropertyValue
value reference: Scalability

Max Concurrent Requests (typical setup)
type: PropertyValue
value reference: Capacity

Time to First Inference
type: PropertyValue
value reference: Deployment

Throughput (7B model, A100)
type: PropertyValue
value reference: Performance

Latency per Token (batch=1, 7B model)
type: PropertyValue
value reference: Performance

Model Architecture Support
type: PropertyValue
value reference: Compatibility

Installation Complexity
type: PropertyValue
value reference: Deployment

VRAM Usage (7B model, INT8)
type: PropertyValue
value reference: Resource Efficiency

GitHub Stars (2026)
type: PropertyValue
value reference: Community

Quantization Method Support
type: PropertyValue
value reference: Features