vLLM vs TensorRT-LLM 2026: Performance & Ease of Use
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.
vLLM
High-throughput LLM inference engine with paged attention and easy-to-use Python API.
Researchers, startups, developers prototyping LLM applications, teams needing multi-model support, academic projects
TensorRT-LLM
Nvidia's production-grade LLM inference framework with aggressive kernel optimization and quantization.
Production inference systems, high-throughput serving infrastructure, Nvidia-centric deployments, latency-sensitive applications
Quick Answer
AI SummaryvLLM 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.
Our Verdict
AI-assistedChoose 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.
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TIE — neck and neck
Choose vLLM if
Researchers, startups, developers prototyping LLM applications, teams needing multi-model support, academic projects
Choose TensorRT-LLM if
Production inference systems, high-throughput serving infrastructure, Nvidia-centric deployments, latency-sensitive applications
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Key Differences at a Glance
- Setup Complexity:✓ vLLM wins(pip install + run vs Clone repo + build + compile + convert models)
- Throughput Optimization:✓ TensorRT-LLM wins(Excellent (kernel fusion, INT8/FP8 quantization, specialized kernels) vs Good (paged attention, continuous batching))
- Model Support Range:✓ vLLM wins(30+ architecture types (LLaMA, Mistral, Qwen, etc.) vs 12-15 major architectures (primarily Nvidia-optimized models))
Key Facts & Figures
72 numeric metrics compared
| Metric | vLLM | TensorRT-LLM | Ratio |
|---|---|---|---|
| Peak Throughput (13B model, V100)(tokens/second) | 2800 | 3100 | |
| Memory Usage (13B model, batch=32)(GB) | 10.5 | 8.8 | |
| Time to First Token (p99 latency)(milliseconds) | 45 | 32 | |
| Setup Time (from install to inference)(minutes) | 5 | 25 | |
| GPU Platform Support Count(platforms) | 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | 1 (NVIDIA only) | |
| Maximum Concurrent Requests(requests) | 256 | 512 | |
| 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+ | — | — |
| 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) | — | — |
| 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 | ~12,000-18,000 | |
| Per-Token Latency (Llama 2 70B)(milliseconds) | 50-60ms | 30-40ms | |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms) | NVIDIA only (1 platform) | |
| Pre-optimized Model Count(models) | 500+ with auto-optimization | 50+ curated models | |
| Memory Usage Reduction (vs PyTorch)(percent) | 50-60% (Paged Attention) | 40-50% (TensorRT optimizations) | |
| Setup Time (Basic Deployment)(minutes) | 5-10 minutes | 60-120 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) | — | — |
| KV Cache Memory Usage(% of baseline) | 25-45% of baseline | — | — |
| Supported Model Categories(count) | LLMs only (30+ architectures) | — | — |
| Enterprise Maturity (years in production)(years) | 2 years (v1.0 in 2024) | — | — |
| GPU Memory for 13B LLM(GB) | ~18-22 GB with continuous batching | — | — |
| 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 | — | — |
| Memory Usage (KV Cache)(% reduction vs standard) | 80% reduction | — | — |
| Enterprise Deployment Features(feature count) | 3 (basic) | — | — |
| 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 | — | — |
| 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 | 20-30 minutes | |
| Throughput (7B model, A100)(tokens/second) | 3,200 tokens/sec | 5,800 tokens/sec | |
| Latency per Token (batch=1, 7B model)(milliseconds) | 45 ms | 28 ms | |
| Model Architecture Support(count) | 30+ architectures | 15 architectures | |
| VRAM Usage (7B model, INT8)(GB) | 9-11 GB | 5-6 GB | |
| GitHub Stars (2026)(stars) | 22,000+ stars | 7,200+ stars |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- pip install + run(winner)Setup ComplexityClone repo + build + compile + convert models
- Good (paged attention, continuous batching)Throughput OptimizationExcellent (kernel fusion, INT8/FP8 quantization, specialized kernels)(winner)
- 30+ architecture types (LLaMA, Mistral, Qwen, etc.)(winner)Model Support Range12-15 major architectures (primarily Nvidia-optimized models)
- ~45ms per tokenInference Latency (7B model, batch=1)~25-30ms per token(winner)
- Extensive (1000+ GitHub stars, active Discord)(winner)Community DocumentationOfficial Nvidia docs (professional but narrower scope)
- GPTQ, AWQ, SqueezeLLMQuantization SupportINT8, INT4, FP8, SmoothQuant with kernel optimization(winner)
- Tensor parallelism, pipeline parallelismMulti-GPU ScalingTensor parallelism, pipeline parallelism, all-reduce optimization(winner)
- Setup Complexity
vLLM
pip install + run(winner)
TensorRT-LLM
Clone repo + build + compile + convert models
- Throughput Optimization
vLLM
Good (paged attention, continuous batching)
TensorRT-LLM
Excellent (kernel fusion, INT8/FP8 quantization, specialized kernels)(winner)
- Model Support Range
vLLM
30+ architecture types (LLaMA, Mistral, Qwen, etc.)(winner)
TensorRT-LLM
12-15 major architectures (primarily Nvidia-optimized models)
- Inference Latency (7B model, batch=1)
vLLM
~45ms per token
TensorRT-LLM
~25-30ms per token(winner)
- Community Documentation
vLLM
Extensive (1000+ GitHub stars, active Discord)(winner)
TensorRT-LLM
Official Nvidia docs (professional but narrower scope)
- Quantization Support
vLLM
GPTQ, AWQ, SqueezeLLM
TensorRT-LLM
INT8, INT4, FP8, SmoothQuant with kernel optimization(winner)
- Multi-GPU Scaling
vLLM
Tensor parallelism, pipeline parallelism
TensorRT-LLM
Tensor parallelism, pipeline parallelism, all-reduce optimization(winner)
Full Comparison
| Attribute | vLLM | TensorRT-LLM |
|---|---|---|
| Peak Throughput (13B model, V100)(tokens/second) | 2800 | 3100(winner) |
| Time to First Token (p99 latency)(milliseconds) | 45 | 32(winner) |
| 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 ~12,000-18,000 Per-Token Latency (Llama 2 70B)(milliseconds) 50-60ms 30-40ms 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) — Inference Throughput (RTX 4090, Llama 2 13B)(tokens/second) ~700 tokens/sec — LLM Throughput Improvement(x faster than baseline) 24x — Memory Usage (KV Cache)(% reduction vs standard) 80% 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 5,800 tokens/sec Latency per Token (batch=1, 7B model)(milliseconds) 45 ms 28 ms | ||
| Memory Usage (13B model, batch=32)(GB) | 10.5 | 8.8(winner) |
| VRAM Usage (7B model, INT8)(GB) | 9-11 GB | 5-6 GB(winner) |
| Setup Time (from install to inference)(minutes) | 5(winner) | 25 |
| Setup Time (Basic Deployment)(minutes) | 5-10 minutes(winner) | 60-120 minutes |
| Kubernetes Native Support(boolean) | Community Helm charts available | — |
| Time to First Inference(minutes) | 2-3 minutes(winner) | 20-30 minutes |
| Installation Complexity(shell commands) | 1-2 commands(winner) | 8-12 commands |
| GPU Platform Support Count(platforms) | 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.)(winner) | 1 (NVIDIA only) |
| Supported ML Frameworks(count) | Primarily PyTorch/Transformers (limited) | — |
| Supported Model Frameworks(count) | 2 (LLM-specific) | — |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms)(winner) | NVIDIA only (1 platform) |
| 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 15 architectures | ||
| Maximum Concurrent Requests(requests) | 256 | 512(winner) |
| Batch Size Improvement (via memory savings)(x multiplier) | 4x larger batches possible | — |
| Multi-GPU Support(scaling efficiency) | Native (tensor parallelism) | — |
| 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) | — |
| Pre-packaged Models Available(count) | Unlimited (HuggingFace) | — |
| Pre-optimized Model Count(models) | 500+ with auto-optimization(winner) | 50+ curated models |
| GitHub Stars(stars) | 23,000+ | — |
| GitHub Stars (community adoption metric)(stars) | 21,000+ | — |
| GitHub Stars (as of 2026)(stars) | ~24,000 | — |
| GitHub Stars (2026)(stars) | 22,000+ stars(winner) | 7,200+ 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 | — |
| 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 | — |
| Distributed Parallelism Setup Time(minutes to configure) | 15-30 (built-in helpers) | — |
| OpenAI API Compatibility(boolean) | Native support, drop-in replacement | — |
| Memory Usage (KV cache, 7B model, batch=1)(GB) | 8.2 GB (with PagedAttention) | — |
| Memory Usage Reduction (vs PyTorch)(percent) | 50-60% (Paged Attention)(winner) | 40-50% (TensorRT optimizations) |
| 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.) 6 methods (INT8, FP8, INT4, SmoothQuant, etc.) | ||
| Production Users (Estimated)(organizations) | ~1,200+ organizations (LLM-focused) | — |
| Cost(USD) | Free (open-source) | Free (requires NVIDIA hardware investment) |
| 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) | — |
| 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) | — |
| 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) | — |
| GPU Memory for 13B LLM(GB) | ~18-22 GB with continuous batching | — |
| Memory Usage (Llama 2 7B, quantized)(GB) | ~6.5 GB | — |
| API Standardization(null) | OpenAI-compatible API | — |
| Enterprise Deployment Features(feature count) | 3 (basic) | — |
| 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·5 cons across both
vLLM
Pros
- Supports 30+ model architectures (LLaMA, Mistral, Qwen, Phi, Llava, etc.)
- Paged attention mechanism reduces memory fragmentation by 80%
- Single-line inference: vllm.LLM(model_name) requires no compilation
- OpenAI-compatible API for drop-in replacement scenarios
- Active open-source community with 22,000+ GitHub stars
Cons
- Latency 40-80% higher than TensorRT-LLM on identical hardware
- Less aggressive kernel fusion; relies on PyTorch/CUDA standard operators
TensorRT-LLM
Pros
- 25-30ms latency per token (45% faster than vLLM on comparable benchmarks)
- Supports INT8, FP8, INT4 quantization with optimized kernels
- Multi-GPU tensor parallelism with all-reduce optimization
- Stateful attention caching reduces memory bandwidth by 60%
- Officially optimized for Nvidia H100, L40S, A100 GPUs
Cons
- Requires model compilation workflow; no direct PyTorch model support
- Limited to 12-15 pre-optimized architectures; custom architectures require manual optimization
- Steeper learning curve; requires understanding of TensorRT plugin ecosystem
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
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