vLLM vs TensorRT-LLM 2026: Speed & Deployment
vLLM prioritizes ease of use and broad hardware compatibility with simpler deployment, while TensorRT-LLM focuses on maximum inference speed and optimization specifically for NVIDIA GPUs. vLLM achieves 2-4x faster throughput with PagedAttention, but TensorRT-LLM can deliver 10-40% higher token/sec on NVIDIA hardware through aggressive kernel optimization.
vLLM
High-throughput LLM inference engine with PagedAttention optimization for rapid deployment across multiple GPU platforms.
Teams needing fast deployment across heterogeneous GPU infrastructure, startups prioritizing time-to-market, and organizations with AMD or Intel GPUs
TensorRT-LLM
NVIDIA-optimized inference framework delivering peak performance through model compilation and specialized CUDA kernels for enterprise deployments.
Large-scale enterprises with NVIDIA infrastructure, applications demanding absolute lowest latency, and production systems handling 100K+ daily requests
Quick Answer
AI SummaryvLLM prioritizes ease of use and broad hardware compatibility with simpler deployment, while TensorRT-LLM focuses on maximum inference speed and optimization specifically for NVIDIA GPUs. vLLM achieves 2-4x faster throughput with PagedAttention, but TensorRT-LLM can deliver 10-40% higher token/sec on NVIDIA hardware through aggressive kernel optimization.
Our Verdict
AI-assistedChoose vLLM if you need rapid deployment, cross-platform GPU support, or are running on non-NVIDIA hardware—it's production-ready in minutes with excellent throughput. Choose TensorRT-LLM if you're heavily invested in NVIDIA infrastructure and need absolute peak performance with 10-40% faster token generation and lower memory footprint on H100/A100 GPUs.
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Choose vLLM if
Best pickTeams needing fast deployment across heterogeneous GPU infrastructure, startups prioritizing time-to-market, and organizations with AMD or Intel GPUs
Choose TensorRT-LLM if
Large-scale enterprises with NVIDIA infrastructure, applications demanding absolute lowest latency, and production systems handling 100K+ daily requests
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Key Differences at a Glance
- Inference Speed (Tokens/Second):✓ TensorRT-LLM wins(2200-3400 tok/s (V100) vs 2000-3000 tok/s (V100))
- GPU Hardware Support:✓ vLLM wins(NVIDIA, AMD, Intel, AWS Trainium vs NVIDIA GPUs only)
- Deployment Complexity:✓ vLLM wins(Simple HTTP server in 5 minutes vs Requires model compilation, 20-30 minutes)
Key Facts & Figures
50 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) | |
| GitHub Stars (2026)(stars) | 25,000+ | 3,200+ | |
| 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 | — | — |
| 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) | — | — |
| 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 | — | — |
| Latest Version Release Cycle(weeks) | 2-3 weeks | — | — |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- 2000-3000 tok/s (V100)Inference Speed (Tokens/Second)2200-3400 tok/s (V100)(winner)
- NVIDIA, AMD, Intel, AWS Trainium(winner)GPU Hardware SupportNVIDIA GPUs only
- Simple HTTP server in 5 minutes(winner)Deployment ComplexityRequires model compilation, 20-30 minutes
- 8-12GB VRAM with PagedAttentionMemory Efficiency (13B model)6-10GB VRAM with in-place reductions(winner)
- Up to 256+ concurrent requestsBatch Size SupportUp to 512+ with explicit tuning(winner)
- 85% efficiency across 8 GPUsMulti-GPU Scaling Efficiency92% efficiency across 8 GPUs(winner)
- HuggingFace, safetensors, GGUF native(winner)Model Format SupportRequires TensorRT engine conversion
- Inference Speed (Tokens/Second)
vLLM
2000-3000 tok/s (V100)
TensorRT-LLM
2200-3400 tok/s (V100)(winner)
- GPU Hardware Support
vLLM
NVIDIA, AMD, Intel, AWS Trainium(winner)
TensorRT-LLM
NVIDIA GPUs only
- Deployment Complexity
vLLM
Simple HTTP server in 5 minutes(winner)
TensorRT-LLM
Requires model compilation, 20-30 minutes
- Memory Efficiency (13B model)
vLLM
8-12GB VRAM with PagedAttention
TensorRT-LLM
6-10GB VRAM with in-place reductions(winner)
- Batch Size Support
vLLM
Up to 256+ concurrent requests
TensorRT-LLM
Up to 512+ with explicit tuning(winner)
- Multi-GPU Scaling Efficiency
vLLM
85% efficiency across 8 GPUs
TensorRT-LLM
92% efficiency across 8 GPUs(winner)
- Model Format Support
vLLM
HuggingFace, safetensors, GGUF native(winner)
TensorRT-LLM
Requires TensorRT engine conversion
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 10 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 — 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 — GPU Memory Reduction vs Baseline(%) ~60% — Throughput Improvement (Batching)(x improvement) 10-23x vs standard — | ||
| Memory Usage (13B model, batch=32)(GB) | 10.5 | 8.8(winner) |
| Memory Usage (Llama 2 7B quantized)(GB) | ~6.5 GB | — |
| Setup Time (from install to inference)(minutes) | 5(winner) | 25 |
| 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 1 more attributeSupported Model Formats(formats) 15+ formats (HF, GGUF, AWQ, GPTQ, etc) — | ||
| 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) | — |
| 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(complexity rating) | 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 (2026)(stars) | 25,000+(winner) | 3,200+ |
| CPU Fallback Support(capability) | Limited, requires GPU | — |
| KV Cache Memory Usage Reduction(x factor) | ~4x reduction | — |
| Multi-Model Serving Setup Complexity(complexity level) | High (requires separate instances) | — |
| Setup Time (basic deployment)(minutes) | 5-10 minutes(winner) | 60-120 minutes |
| 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 | — |
| Distributed Parallelism Setup Time(minutes to configure) | 15-30 (built-in helpers) | — |
| 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) |
| 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 | — |
| Production Users (Estimated)(organizations) | ~1,200+ organizations (LLM-focused) | — |
| GitHub Stars (as of 2026)(stars) | ~24,000 | — |
| Cost(USD) | Free (open-source) | Free (requires NVIDIA hardware investment) |
| 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%+) | — |
| Infrastructure Management | User-managed (CUDA, Docker, scaling) | — |
| Production Monitoring(metrics exported) | Basic (throughput, latency) | — |
| Community & Documentation(GitHub stars) | 25,000+ stars, weekly updates | — |
| Official Enterprise Support(boolean) | Community-based | — |
| API Standardization(null) | OpenAI-compatible API | — |
| Enterprise Deployment Features(feature count) | 3 (basic) | — |
| Latest Version Release Cycle(weeks) | 2-3 weeks | — |
Show 10 more attributes
Show 1 more attribute
Pros & Cons
9 pros·4 cons across both
vLLM
Pros
- 2-4x faster throughput than standard implementations via PagedAttention technique
- Supports NVIDIA, AMD, Intel, and specialized accelerators (Trainium, Gaudi)
- Zero-copy deployment: run inference in 5 minutes with pip install + 3 lines of code
- Native HuggingFace model compatibility without format conversion
- Active community (35K+ GitHub stars) with weekly updates and 200+ contributor base
Cons
- 15-20% lower peak token/s on NVIDIA H100 compared to TensorRT-LLM due to less aggressive kernel tuning
- Requires 2-3GB more VRAM than TensorRT-LLM on same hardware for identical batch sizes
TensorRT-LLM
Pros
- 10-40% faster token generation on NVIDIA H100/A100 GPUs through hand-optimized CUDA kernels
- Memory-efficient with 15-25% lower VRAM usage via in-place operations and fused kernels
- Enterprise-grade quantization (INT8, FP8) with negligible accuracy loss (0.5-1%)
- Multi-GPU scaling efficiency of 92% across 8 GPUs, highest in class
Cons
- NVIDIA GPU-only: no support for AMD Instinct, Intel Arc, or consumer-grade hardware
- Steep learning curve: requires 20-30 minutes to compile models, CUDA expertise needed for custom kernels
Frequently Asked Questions
5 questions
TensorRT-LLM delivers 10-40% higher throughput on NVIDIA H100/A100 GPUs through aggressive kernel optimization. However, vLLM achieves 85% of TensorRT-LLM's performance while supporting 7x more hardware platforms. For NVIDIA-only deployments with extreme performance requirements, TensorRT-LLM wins; for heterogeneous infrastructure, vLLM is faster to deploy with comparable throughput.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
- W
vLLM on Wikipedia (opens in new tab)
High-throughput LLM inference engine with PagedAttention optimization for rapid deployment across multiple GPU platforms.
- W
TensorRT-LLM on Wikipedia (opens in new tab)
NVIDIA-optimized inference framework delivering peak performance through model compilation and specialized CUDA kernels for enterprise deployments.
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