vLLM vs TensorRT-LLM
vLLM
Open-source Python library for fast LLM inference with advanced batching and memory optimization.
Teams needing quick deployment across mixed hardware, supporting diverse models, or avoiding vendor lock-in
TensorRT-LLM
NVIDIA's proprietary LLM inference framework for maximum performance on NVIDIA GPUs
Enterprise organizations with NVIDIA-only infrastructure requiring absolute peak performance and latency guarantees
Short Answer
vLLM is a faster, more flexible open-source inference engine that works across multiple hardware platforms with 10-40x throughput improvements, while TensorRT-LLM is NVIDIA's proprietary framework optimized specifically for NVIDIA GPUs with maximum performance on supported models but less flexibility.
Our Verdict
AI-assistedChoose vLLM if you need flexibility across multiple hardware platforms, quick deployment, and support for hundreds of models without vendor lock-in. Choose TensorRT-LLM if you're exclusively on NVIDIA infrastructure and require absolute maximum throughput and latency optimization (20-30% faster on A100/H100 GPUs) for mission-critical production workloads with supported models.
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Choose vLLM if
Teams needing quick deployment across mixed hardware, supporting diverse models, or avoiding vendor lock-in
Choose TensorRT-LLM if
Enterprise organizations with NVIDIA-only infrastructure requiring absolute peak performance and latency guarantees
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Key Differences at a Glance
Key Facts & Figures
| Metric | vLLM | TensorRT-LLM | Diff |
|---|---|---|---|
| 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 | 50,000+ | — | — |
| 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) | 3 (PyTorch, HF Transformers, vLLM native) | — | — |
| P99 Latency (7B model, batch=32)(milliseconds) | 380 ms | — | — |
| Production Users (Estimated)(count) | ~1,200+ organizations (LLM-focused) | — | — |
| GitHub Stars (as of 2026)(stars) | 22,500 stars | — | — |
| Throughput (tokens/sec on A100)(tokens/second) | ~8,000-12,000 | ~12,000-18,000 | -33% |
| Per-Token Latency (Llama 2 70B)(milliseconds) | 50-60ms | 30-40ms | +57% |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms) | NVIDIA only (1 platform) | +300% |
| Pre-optimized Model Count(models) | 500+ with auto-optimization | 50+ curated models | +900% |
| Memory Usage Reduction (vs PyTorch)(percent) | 50-60% (Paged Attention) | 40-50% (TensorRT optimizations) | +22% |
| GitHub Stars (2026)(stars) | 7,500+ | 3,200+ | +134% |
| Setup Time (basic deployment)(minutes) | 5-10 minutes | 60-120 minutes | -92% |
| 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 | — | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
vLLM
Multi-platform (NVIDIA, AMD, Intel, CPU)🏆
TensorRT-LLM
NVIDIA GPUs only
vLLM
10-40x faster
TensorRT-LLM
20-50x faster on NVIDIA GPUs🏆
vLLM
500+ open models (Llama, Mistral, Qwen, etc.)🏆
TensorRT-LLM
50+ optimized models (curated list)
vLLM
Simple pip install, minimal config🏆
TensorRT-LLM
Complex compilation, engine building required
vLLM
~50-60ms per token
TensorRT-LLM
~30-40ms per token🏆
vLLM
7,500+ GitHub stars, 300+ contributors🏆
TensorRT-LLM
3,200+ GitHub stars, 100+ contributors
vLLM
Open-source, free, hardware-agnostic🏆
TensorRT-LLM
Free but requires NVIDIA ecosystem investment
Full Comparison
| Attribute |  vLLM | TensorRT-LLM |
|---|---|---|
| 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+ | — |
| Token Throughput (A100-40GB, 7B model)(tokens/sec) | 12,500 tokens/sec | — |
| P99 Latency (7B model, batch=32)(milliseconds) | 380 ms | — |
Show 3 more attributesThroughput (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 — | ||
| 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) | — |
| Setup Time (from download to first inference)(minutes) | 30 minutes | — |
| Pre-packaged Models Available(count) | Unlimited (HuggingFace) | — |
| Pre-optimized Model Count(models) | 500+ with auto-optimization | 50+ curated models |
| GitHub Stars | 50,000+ | — |
| CPU Fallback Support(capability) | Limited, requires GPU | — |
| KV Cache Memory Usage Reduction(x factor) | ~4x reduction | — |
| Supported ML Frameworks(count) | Primarily PyTorch/Transformers (limited) | — |
| Supported Model Frameworks(count) | 3 (PyTorch, HF Transformers, vLLM native) | — |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms) | NVIDIA only (1 platform) |
| GitHub Stars (community adoption metric)(stars) | 21,000+ | — |
| GitHub Stars (as of 2026)(stars) | 22,500 stars | — |
| GitHub Stars (2026)(stars) | 7,500+ | 3,200+ |
| Multi-Model Serving Setup Complexity(complexity level) | High (requires separate instances) | — |
| Configuration Complexity(config files needed) | 1 (minimal, CLI-driven) | — |
| Setup Time (basic deployment)(minutes) | 5-10 minutes | 60-120 minutes |
| Setup Time (basic inference)(minutes) | 120-420 minutes (2-7 days with infrastructure) | — |
| Batch Size Improvement (via memory savings)(x multiplier) | 4x larger batches possible | — |
| 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) | 40-50% (TensorRT optimizations) |
| Model Ensemble Support(boolean) | No native ensemble; requires external orchestration | — |
| Training Capabilities | Inference-only, no native training | — |
| Production Users (Estimated)(count) | ~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 | — |
| 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) | — |
| Community & Documentation(GitHub stars) | 25,000+ stars, weekly updates | — |
vLLMShow 3 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
vLLM
Pros
- Supports 500+ open-source models out-of-box with automatic compatibility
- Runs on NVIDIA, AMD, Intel GPUs and CPUs without modification
- Paged Attention algorithm reduces memory usage by 50-60%
- OpenAI-compatible API for seamless integration
- Active development with weekly releases and 300+ community contributors
Cons
- Per-token latency 30-40% higher than TensorRT-LLM on NVIDIA GPUs
- Requires more manual tuning for production deployment at scale
TensorRT-LLM
Pros
- 30-40ms per-token latency on A100 (20-30% faster than vLLM)
- Optimized for NVIDIA A100, H100, L40S with specialized kernels
- Supports multi-GPU distributed inference with Megatron-style parallelism
- Production-grade performance monitoring and profiling tools
- Backed by NVIDIA engineering with guaranteed support
Cons
- Only works on NVIDIA GPUs—no AMD, Intel, or CPU support
- Model support limited to 50+ pre-optimized configurations
- Steep learning curve with complex engine building and compilation process
- Requires CUDA expertise and TensorRT knowledge for custom models
Frequently Asked Questions
TensorRT-LLM is 20-30% faster on NVIDIA GPUs, achieving 30-40ms per-token latency vs vLLM's 50-60ms on the same A100 hardware. However, vLLM offers superior throughput efficiency and works across non-NVIDIA platforms, making it faster in multi-hardware environments.
Resources & Learn More
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Where to Buy
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