vLLM vs TensorRT-LLM
type: Article
description: vLLM prioritizes ease of use and broad model compatibility with simple Python APIs, while TensorRT-LLM focuses on maximum inference performance through aggressive optimization, requiring more complex compilation workflows. vLLM is better for quick prototyping; TensorRT-LLM excels at production deployment where latency is critical.
language: en-US
date published: 2026-07-09T14:20:54.317Z
date created: 2026-07-09T14:20:54.317Z
date modified: 2026-07-09T14:20:54.634Z
content reference time: 2026-07-09T14:20:54.634Z
author: A Versus B
publisher: A Versus B
about: vLLM
about: TensorRT-LLM
abstract: vLLM prioritizes ease of use and broad model compatibility with simple Python APIs, while TensorRT-LLM focuses on maximum inference performance through aggressive optimization, requiring more complex compilation workflows. vLLM is better for quick prototyping; TensorRT-LLM excels at production deployment where latency is critical.
text: Setup Complexity. Throughput Optimization. Model Support Range. Inference Latency (7B model, batch=1). Community Documentation. Quantization Support. Multi-GPU Scaling
conclusion text: Choose vLLM if you need rapid deployment, broad model compatibility, and minimal setup overhead—ideal for research, MVPs, and diverse model experimentation. Choose TensorRT-LLM if you're optimizing for production performance on Nvidia GPUs where sub-30ms latency and maximum throughput are mandatory requirements.
additional property: Peak Throughput (13B model, V100)
  type: PropertyValue
  value: vLLM: 2800 | TensorRT-LLM: 3100
  value reference: TensorRT-LLM
additional property: Memory Usage (13B model, batch=32)
  type: PropertyValue
  value: vLLM: 10.5 | TensorRT-LLM: 8.8
  value reference: TensorRT-LLM
additional property: Time to First Token (p99 latency)
  type: PropertyValue
  value: vLLM: 45 | TensorRT-LLM: 32
  value reference: TensorRT-LLM
additional property: Setup Time (from install to inference)
  type: PropertyValue
  value: vLLM: 5 | TensorRT-LLM: 25
  value reference: vLLM
additional property: GPU Platform Support Count
  type: PropertyValue
  value: vLLM: 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | TensorRT-LLM: 1 (NVIDIA only)
  value reference: vLLM
additional property: Maximum Concurrent Requests
  type: PropertyValue
  value: vLLM: 256 | TensorRT-LLM: 512
  value reference: TensorRT-LLM
additional property: Time to First Token (ms)
  type: PropertyValue
  value: vLLM: 80-120 ms | TensorRT-LLM: N/A
additional property: Throughput (tokens/second, batch size 32)
  type: PropertyValue
  value: vLLM: ~1200 tok/s | TensorRT-LLM: N/A
additional property: Minimum RAM Required
  type: PropertyValue
  value: vLLM: 8 GB | TensorRT-LLM: N/A
additional property: GPU Memory for 7B Model
  type: PropertyValue
  value: vLLM: 5-6 GB (with optimization) | TensorRT-LLM: N/A
additional property: Setup Time (from download to first inference)
  type: PropertyValue
  value: vLLM: 30 minutes | TensorRT-LLM: N/A
additional property: Pre-packaged Models Available
  type: PropertyValue
  value: vLLM: Unlimited (HuggingFace) | TensorRT-LLM: N/A
additional property: GitHub Stars
  type: PropertyValue
  value: vLLM: 23,000+ | TensorRT-LLM: N/A
additional property: CPU Fallback Support
  type: PropertyValue
  value: vLLM: Limited, requires GPU | TensorRT-LLM: N/A
additional property: Throughput (tokens/second, LLaMA 70B example)
  type: PropertyValue
  value: vLLM: 1,500+ | TensorRT-LLM: N/A
additional property: KV Cache Memory Usage Reduction
  type: PropertyValue
  value: vLLM: ~4x reduction | TensorRT-LLM: N/A
additional property: Supported ML Frameworks
  type: PropertyValue
  value: vLLM: Primarily PyTorch/Transformers (limited) | TensorRT-LLM: N/A
additional property: GitHub Stars (community adoption metric)
  type: PropertyValue
  value: vLLM: 21,000+ | TensorRT-LLM: N/A
additional property: Minimum GPU Memory (LLaMA 70B, 1 GPU)
  type: PropertyValue
  value: vLLM: 40 GB (with PagedAttention) | TensorRT-LLM: N/A
additional property: Multi-Model Serving Setup Complexity
  type: PropertyValue
  value: vLLM: High (requires separate instances) | TensorRT-LLM: N/A
additional property: Batch Size Improvement (via memory savings)
  type: PropertyValue
  value: vLLM: 4x larger batches possible | TensorRT-LLM: N/A
additional property: Distributed Parallelism Setup Time
  type: PropertyValue
  value: vLLM: 15-30 (built-in helpers) | TensorRT-LLM: N/A
additional property: Token Throughput (A100-40GB, 7B model)
  type: PropertyValue
  value: vLLM: 12,500 tokens/sec | TensorRT-LLM: N/A
additional property: Memory Usage (KV cache, 7B model, batch=1)
  type: PropertyValue
  value: vLLM: 8.2 GB (with PagedAttention) | TensorRT-LLM: N/A
additional property: Supported Model Frameworks
  type: PropertyValue
  value: vLLM: 2 (LLM-specific) | TensorRT-LLM: N/A
additional property: P99 Latency (7B model, batch=32)
  type: PropertyValue
  value: vLLM: 380 ms | TensorRT-LLM: N/A
additional property: Configuration Complexity
  type: PropertyValue
  value: vLLM: Low (Python API) | TensorRT-LLM: N/A
additional property: Model Ensemble Support
  type: PropertyValue
  value: vLLM: No native ensemble; requires external orchestration | TensorRT-LLM: N/A
additional property: Production Users (Estimated)
  type: PropertyValue
  value: vLLM: ~1,200+ organizations (LLM-focused) | TensorRT-LLM: N/A
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: vLLM: ~24,000 | TensorRT-LLM: N/A
additional property: Throughput (tokens/sec on A100)
  type: PropertyValue
  value: vLLM: ~8,000-12,000 | TensorRT-LLM: ~12,000-18,000
  value reference: TensorRT-LLM
additional property: Per-Token Latency (Llama 2 70B)
  type: PropertyValue
  value: vLLM: 50-60ms | TensorRT-LLM: 30-40ms
  value reference: TensorRT-LLM
additional property: Supported GPU Platforms
  type: PropertyValue
  value: vLLM: NVIDIA, AMD, Intel, CPU (4 platforms) | TensorRT-LLM: NVIDIA only (1 platform)
  value reference: vLLM
additional property: Pre-optimized Model Count
  type: PropertyValue
  value: vLLM: 500+ with auto-optimization | TensorRT-LLM: 50+ curated models
  value reference: vLLM
additional property: Memory Usage Reduction (vs PyTorch)
  type: PropertyValue
  value: vLLM: 50-60% (Paged Attention) | TensorRT-LLM: 40-50% (TensorRT optimizations)
  value reference: vLLM
additional property: Setup Time (Basic Deployment)
  type: PropertyValue
  value: vLLM: 5-10 minutes | TensorRT-LLM: 60-120 minutes
  value reference: vLLM
additional property: Cost
  type: PropertyValue
  value: vLLM: Free (open-source) | TensorRT-LLM: Free (requires NVIDIA hardware investment)
additional property: Inference Throughput (single A100 GPU)
  type: PropertyValue
  value: vLLM: 25,000 tokens/sec | TensorRT-LLM: N/A
additional property: Setup Time (basic inference)
  type: PropertyValue
  value: vLLM: 120-420 minutes (2-7 days with infrastructure) | TensorRT-LLM: N/A
additional property: Cost per Million Tokens (A100, on-demand)
  type: PropertyValue
  value: vLLM: $0.12 | TensorRT-LLM: N/A
additional property: Supported Models (major open-source)
  type: PropertyValue
  value: vLLM: 1,000+ models | TensorRT-LLM: N/A
additional property: Training Capabilities
  type: PropertyValue
  value: vLLM: Inference-only, no native training | TensorRT-LLM: N/A
additional property: Enterprise SLA Uptime
  type: PropertyValue
  value: vLLM: Community-dependent (typically 99.0%+) | TensorRT-LLM: N/A
additional property: Infrastructure Management
  type: PropertyValue
  value: vLLM: User-managed (CUDA, Docker, scaling) | TensorRT-LLM: N/A
additional property: Community & Documentation
  type: PropertyValue
  value: vLLM: 25,000+ stars, weekly updates | TensorRT-LLM: N/A
additional property: LLM Throughput (tokens/sec)
  type: PropertyValue
  value: vLLM: ~5,000-15,000 (on A100 with batching) | TensorRT-LLM: N/A
additional property: KV Cache Memory Usage
  type: PropertyValue
  value: vLLM: 25-45% of baseline | TensorRT-LLM: N/A
additional property: Supported Model Categories
  type: PropertyValue
  value: vLLM: LLMs only (30+ architectures) | TensorRT-LLM: N/A
additional property: OpenAI API Compatibility
  type: PropertyValue
  value: vLLM: Native support, drop-in replacement | TensorRT-LLM: N/A
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/tensorrt-llm
significant link: https://www.aversusb.net/alternatives/tensorrt-llm
significant link: https://www.aversusb.net/api/answer/vllm-vs-tensorrt-llm))
significant link: https://www.aversusb.net/api/v1/schema/vllm-vs-tensorrt-llm))
related link: https://www.aversusb.net/compare/vllm-vs-tensorrt-llm
related link: https://www.aversusb.net/compare/vllm-vs-tensorrt-llm)
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-sagemaker
related link: https://www.aversusb.net/compare/ollama-vs-vllm)
related link: https://www.aversusb.net/compare/vllm-vs-triton)
teaches: How to choose between vLLM and TensorRT-LLM
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: vLLM vs TensorRT-LLM — 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 TensorRT-LLM
image: ImageObject
  caption: vLLM

TensorRT-LLM
type: SoftwareApplication
alternate name: Tensorrt Llm
language: en-US
description: Nvidia's production-grade LLM inference framework with aggressive kernel optimization and quantization.
subject of: vLLM vs TensorRT-LLM

vLLM vs TensorRT-LLM — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for vLLM vs TensorRT-LLM. 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 & Adoption
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: Integration
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
  value reference: Compatibility
variable measured: P99 Latency (7B model, batch=32)
  type: PropertyValue
  value reference: Performance
variable measured: Configuration Complexity
  type: PropertyValue
  value reference: Usability
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
  value reference: Performance
variable measured: Memory Usage (Llama 2 7B, quantized)
  type: PropertyValue
  value reference: Resource Usage
variable measured: Installation Time (from zero)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Minimum VRAM for Llama 2 7B
  type: PropertyValue
  value reference: Hardware Requirements
variable measured: Number of Supported GPU Backends
  type: PropertyValue
  value reference: Compatibility
variable measured: Batch Processing Support
  type: PropertyValue
  value reference: Features
variable measured: API Standardization
  type: PropertyValue
  value reference: Integration
variable measured: LLM Throughput Improvement
  type: PropertyValue
  value reference: Performance
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-tensorrt-llm))

vLLM vs TensorRT-LLM — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for vLLM vs TensorRT-LLM.
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 & Adoption
has defined term: Hardware Compatibility
has defined term: Memory Efficiency
has defined term: Integration
has defined term: Community
has defined term: Ease of Use
has defined term: Developer Experience
has defined term: Efficiency
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: 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 TensorRT-LLM — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

System Requirements
type: DefinedTerm
term code: system-requirements
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Hardware Requirements
type: DefinedTerm
term code: hardware-requirements
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Model Support
type: DefinedTerm
term code: model-support
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Community & Adoption
type: DefinedTerm
term code: community-&-adoption
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Hardware Compatibility
type: DefinedTerm
term code: hardware-compatibility
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Memory Efficiency
type: DefinedTerm
term code: memory-efficiency
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

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

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Efficiency
type: DefinedTerm
term code: efficiency
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Enterprise Adoption
type: DefinedTerm
term code: enterprise-adoption
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Economics
type: DefinedTerm
term code: economics
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Stability
type: DefinedTerm
term code: stability
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Resource Requirements
type: DefinedTerm
term code: resource-requirements
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Resource Usage
type: DefinedTerm
term code: resource-usage
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Enterprise
type: DefinedTerm
term code: enterprise
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

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

Maintenance
type: DefinedTerm
term code: maintenance
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

Capacity
type: DefinedTerm
term code: capacity
in defined term set: vLLM vs TensorRT-LLM — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T14:20:54.634Z
author: A Versus B
publisher: A Versus B
is part of: vllm-vs-tensorrt-llm))
main entity: Which is faster for production inference?
  type: Question
  accepted answer: Answer
    text: TensorRT-LLM is 40-80% faster depending on model size and batch size. For a 7B parameter model at batch=1, TensorRT-LLM delivers ~28ms latency vs vLLM's ~45ms. This gap widens with larger models. However, vLLM's paged attention (PagedAttention v2) closes the gap for very high concurrency workloads.
    language: en-US
main entity: Can I easily switch from vLLM to TensorRT-LLM?
  type: Question
  accepted answer: Answer
    text: Not directly. TensorRT-LLM requires model compilation and doesn't accept raw PyTorch weights. You must convert weights to TensorRT engine format, which requires custom scripts per architecture. vLLM models cannot be migrated without full re-compilation. Plan 1-2 weeks for complex migrations.
    language: en-US
main entity: Which supports more open-source models?
  type: Question
  accepted answer: Answer
    text: vLLM supports 30+ architectures including LLaMA, Mistral, Qwen, Phi, Llava, and community models. TensorRT-LLM officially supports ~15 architectures (LLaMA 2/3, GPT-J, Falcon, Bloom, etc.) and focuses on Nvidia-optimized models. For bleeding-edge or niche models, vLLM is the safer choice.
    language: en-US
main entity: What's the memory overhead difference?
  type: Question
  accepted answer: Answer
    text: TensorRT-LLM uses 40-50% less VRAM through aggressive quantization and kernel fusion. A 7B model uses ~5.5GB in TensorRT-LLM (INT8) vs 10GB in vLLM. TensorRT-LLM's fused kernels and optimized caching give significant memory advantages on A100/H100 GPUs.
    language: en-US
main entity: Which is better for multi-model serving?
  type: Question
  accepted answer: Answer
    text: vLLM wins decisively. Its dynamic batching and simple API allow loading multiple different architectures in one process. TensorRT-LLM requires separate engine files per model and more complex orchestration. For inference platforms serving diverse models, vLLM is 3-4x easier to implement.
    language: en-US

How to Choose Between vLLM and TensorRT-LLM
type: HowTo
description: vLLM prioritizes ease of use and broad model compatibility with simple Python APIs, while TensorRT-LLM focuses on maximum inference performance through aggressive optimization, requiring more complex compilation workflows. vLLM is better for quick prototyping; TensorRT-LLM excels at production deployment where latency is critical.
language: en-US
total time: PT3M
step: Consider: Setup Complexity
step: Consider: Throughput Optimization
step: Consider: Model Support Range
step: Consider: Inference Latency (7B model, batch=1)
step: Consider: Community Documentation
step: Consider: Quantization Support
step: Consider: Multi-GPU Scaling
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose vLLM if you need rapid deployment, broad model compatibility, and minimal setup overhead—ideal for research, MVPs, and diverse model experimentation. Choose TensorRT-LLM if you're optimizing for production performance on Nvidia GPUs where sub-30ms latency and maximum throughput are mandatory requirements.

Consider: Setup Complexity
type: HowToStep
position: 1
text: Setup Complexity: vLLM has pip install + run, while TensorRT-LLM has Clone repo + build + compile + convert models.

Consider: Throughput Optimization
type: HowToStep
position: 2
text: Throughput Optimization: vLLM has Good (paged attention, continuous batching), while TensorRT-LLM has Excellent (kernel fusion, INT8/FP8 quantization, specialized kernels).

Consider: Model Support Range
type: HowToStep
position: 3
text: Model Support Range: vLLM has 30+ architecture types (LLaMA, Mistral, Qwen, etc.), while TensorRT-LLM has 12-15 major architectures (primarily Nvidia-optimized models).

Consider: Inference Latency (7B model, batch=1)
type: HowToStep
position: 4
text: Inference Latency (7B model, batch=1): vLLM has ~45ms per token, while TensorRT-LLM has ~25-30ms per token.

Consider: Community Documentation
type: HowToStep
position: 5
text: Community Documentation: vLLM has Extensive (1000+ GitHub stars, active Discord), while TensorRT-LLM has Official Nvidia docs (professional but narrower scope).

Consider: Quantization Support
type: HowToStep
position: 6
text: Quantization Support: vLLM has GPTQ, AWQ, SqueezeLLM, while TensorRT-LLM has INT8, INT4, FP8, SmoothQuant with kernel optimization.

Consider: Multi-GPU Scaling
type: HowToStep
position: 7
text: Multi-GPU Scaling: vLLM has Tensor parallelism, pipeline parallelism, while TensorRT-LLM has Tensor parallelism, pipeline parallelism, all-reduce optimization.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: vLLM vs TensorRT-LLM
review body: vLLM prioritizes ease of use and broad model compatibility with simple Python APIs, while TensorRT-LLM focuses on maximum inference performance through aggressive optimization, requiring more complex compilation workflows. vLLM is better for quick prototyping; TensorRT-LLM excels at production deployment where latency is critical.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: vLLM vs TensorRT-LLM

Enterprise Maturity (years in production)
type: PropertyValue
value: vLLM: 2 years (v1.0 in 2024) | TensorRT-LLM: N/A

GPU Memory for 13B LLM
type: PropertyValue
value: vLLM: ~18-22 GB with continuous batching | TensorRT-LLM: N/A

Kubernetes Native Support
type: PropertyValue
value: vLLM: Community Helm charts available | TensorRT-LLM: N/A

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

Memory Usage (Llama 2 7B, quantized)
type: PropertyValue
value: vLLM: ~6.5 GB | TensorRT-LLM: N/A

Installation Time (from zero)
type: PropertyValue
value: vLLM: 25-40 minutes | TensorRT-LLM: N/A

Minimum VRAM for Llama 2 7B
type: PropertyValue
value: vLLM: 6 GB | TensorRT-LLM: N/A

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

Batch Processing Support
type: PropertyValue
value: vLLM: Yes (native continuous batching) | TensorRT-LLM: N/A

API Standardization
type: PropertyValue
value: vLLM: OpenAI-compatible API | TensorRT-LLM: N/A

LLM Throughput Improvement
type: PropertyValue
value: vLLM: 24x | TensorRT-LLM: N/A

Memory Usage (KV Cache)
type: PropertyValue
value: vLLM: 80% reduction | TensorRT-LLM: N/A

Enterprise Deployment Features
type: PropertyValue
value: vLLM: 3 (basic) | TensorRT-LLM: N/A

Multi-GPU Support
type: PropertyValue
value: vLLM: Native (tensor parallelism) | TensorRT-LLM: N/A

Production Monitoring
type: PropertyValue
value: vLLM: Basic (throughput, latency) | TensorRT-LLM: N/A

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

Memory Usage (LLaMA 2 70B)
type: PropertyValue
value: vLLM: 45 GB (with PagedAttention) | TensorRT-LLM: N/A

Deployment Time
type: PropertyValue
value: vLLM: 20-30 minutes (self-hosted) | TensorRT-LLM: N/A

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

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

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

SLA Availability Guarantee
type: PropertyValue
value: vLLM: No SLA (community support) | TensorRT-LLM: N/A

Enterprise Support Availability
type: PropertyValue
value: vLLM: Community (GitHub issues) | TensorRT-LLM: N/A

GPU Memory Reduction vs Baseline
type: PropertyValue
value: vLLM: ~60% | TensorRT-LLM: N/A

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

Supported Model Formats
type: PropertyValue
value: vLLM: 15+ formats (HF, GGUF, AWQ, GPTQ, etc) | TensorRT-LLM: N/A

Time to Deploy (minutes)
type: PropertyValue
value: vLLM: 5-10 minutes for basic LLM service | TensorRT-LLM: N/A

Token Streaming Native Support
type: PropertyValue
value: vLLM: Via API wrapper | TensorRT-LLM: N/A

Official Enterprise Support
type: PropertyValue
value: vLLM: Community-based | TensorRT-LLM: N/A

Latest Version Release Cycle
type: PropertyValue
value: vLLM: 2-3 weeks | TensorRT-LLM: N/A

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

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

Model Type Support Count
type: PropertyValue
value: vLLM: 1 (LLMs only) | TensorRT-LLM: N/A

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

Supported Quantization Formats
type: PropertyValue
value: vLLM: GPTQ, AWQ, INT4, FP8, INT8 | TensorRT-LLM: N/A

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

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

Time to First Inference
type: PropertyValue
value: vLLM: 2-3 minutes | TensorRT-LLM: 20-30 minutes
value reference: vLLM

Throughput (7B model, A100)
type: PropertyValue
value: vLLM: 3,200 tokens/sec | TensorRT-LLM: 5,800 tokens/sec
value reference: TensorRT-LLM

Latency per Token (batch=1, 7B model)
type: PropertyValue
value: vLLM: 45 ms | TensorRT-LLM: 28 ms
value reference: TensorRT-LLM

Model Architecture Support
type: PropertyValue
value: vLLM: 30+ architectures | TensorRT-LLM: 15 architectures
value reference: vLLM

Installation Complexity
type: PropertyValue
value: vLLM: 1-2 commands | TensorRT-LLM: 8-12 commands
value reference: vLLM

VRAM Usage (7B model, INT8)
type: PropertyValue
value: vLLM: 9-11 GB | TensorRT-LLM: 5-6 GB
value reference: TensorRT-LLM

GitHub Stars (2026)
type: PropertyValue
value: vLLM: 22,000+ stars | TensorRT-LLM: 7,200+ stars
value reference: vLLM

Quantization Method Support
type: PropertyValue
value: vLLM: 5 methods (GPTQ, AWQ, SqueezeLLM, etc.) | TensorRT-LLM: 6 methods (INT8, FP8, INT4, SmoothQuant, etc.)
value reference: TensorRT-LLM

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vLLM — Wikipedia
type: CreativeWork

TensorRT-LLM — Wikipedia
type: CreativeWork

Compare vLLM vs TensorRT-LLM
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/vllm-vs-tensorrt-llm))
object: vLLM
object: TensorRT-LLM

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