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 vs TensorRT-LLM — Side-by-side comparison on A Versus B
High-throughput LLM inference engine with paged attention and easy-to-use Python API.
Nvidia's production-grade LLM inference framework with aggressive kernel optimization and quantization.
Setup Complexity. Throughput Optimization. Model Support Range. Inference Latency (7B model, batch=1). Community Documentation. Quantization Support. Multi-GPU Scaling
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.
vLLM: 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | TensorRT-LLM: 1 (NVIDIA only)
Throughput (tokens/second, batch size 32)
vLLM: 5-6 GB (with optimization) | TensorRT-LLM: N/A
Setup Time (from download to first inference)
vLLM: Unlimited (HuggingFace) | TensorRT-LLM: N/A
vLLM: Limited, requires GPU | TensorRT-LLM: N/A
Throughput (tokens/second, LLaMA 70B example)
vLLM: Primarily PyTorch/Transformers (limited) | TensorRT-LLM: N/A
GitHub Stars (community adoption metric)
vLLM: 40 GB (with PagedAttention) | TensorRT-LLM: N/A
vLLM: High (requires separate instances) | TensorRT-LLM: N/A
Batch Size Improvement (via memory savings)
vLLM: 4x larger batches possible | TensorRT-LLM: N/A
vLLM: 15-30 (built-in helpers) | TensorRT-LLM: N/A
vLLM: 12,500 tokens/sec | TensorRT-LLM: N/A
Memory Usage (KV cache, 7B model, batch=1)
vLLM: 8.2 GB (with PagedAttention) | TensorRT-LLM: N/A
vLLM: 2 (LLM-specific) | TensorRT-LLM: N/A
vLLM: Low (Python API) | TensorRT-LLM: N/A
vLLM: No native ensemble; requires external orchestration | TensorRT-LLM: N/A
vLLM: ~1,200+ organizations (LLM-focused) | TensorRT-LLM: N/A
vLLM: NVIDIA, AMD, Intel, CPU (4 platforms) | TensorRT-LLM: NVIDIA only (1 platform)
vLLM: 500+ with auto-optimization | TensorRT-LLM: 50+ curated models
vLLM: 50-60% (Paged Attention) | TensorRT-LLM: 40-50% (TensorRT optimizations)
vLLM: 5-10 minutes | TensorRT-LLM: 60-120 minutes
vLLM: Free (open-source) | TensorRT-LLM: Free (requires NVIDIA hardware investment)
vLLM: 25,000 tokens/sec | TensorRT-LLM: N/A
vLLM: 120-420 minutes (2-7 days with infrastructure) | TensorRT-LLM: N/A
Cost per Million Tokens (A100, on-demand)
vLLM: Inference-only, no native training | TensorRT-LLM: N/A
vLLM: Community-dependent (typically 99.0%+) | TensorRT-LLM: N/A
vLLM: User-managed (CUDA, Docker, scaling) | TensorRT-LLM: N/A
vLLM: 25,000+ stars, weekly updates | TensorRT-LLM: N/A
vLLM: ~5,000-15,000 (on A100 with batching) | TensorRT-LLM: N/A
vLLM: 25-45% of baseline | TensorRT-LLM: N/A
vLLM: LLMs only (30+ architectures) | TensorRT-LLM: N/A
vLLM: Native support, drop-in replacement | TensorRT-LLM: N/A
Enterprise Maturity (years in production)
vLLM: 2 years (v1.0 in 2024) | TensorRT-LLM: N/A
vLLM: ~18-22 GB with continuous batching | TensorRT-LLM: N/A
vLLM: Community Helm charts available | TensorRT-LLM: N/A
Inference Throughput (RTX 4090, Llama 2 13B)
vLLM: ~700 tokens/sec | TensorRT-LLM: N/A
vLLM: 4+ (CUDA, ROCm, CPU, TPU, custom) | TensorRT-LLM: N/A
vLLM: Yes (native continuous batching) | TensorRT-LLM: N/A
vLLM: OpenAI-compatible API | TensorRT-LLM: N/A
vLLM: Native (tensor parallelism) | TensorRT-LLM: N/A
vLLM: Basic (throughput, latency) | TensorRT-LLM: N/A
Inference Throughput (LLaMA 2 70B, A100 GPU)
vLLM: 25,000 tokens/sec (batch 256) | TensorRT-LLM: N/A
vLLM: 45 GB (with PagedAttention) | TensorRT-LLM: N/A
vLLM: 20-30 minutes (self-hosted) | TensorRT-LLM: N/A
Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
vLLM: $0.25 (self-hosted, amortized) | TensorRT-LLM: N/A
vLLM: 500+ community models | TensorRT-LLM: N/A
vLLM: User-managed (Docker, K8s, VMs) | TensorRT-LLM: N/A
vLLM: No SLA (community support) | TensorRT-LLM: N/A
vLLM: Community (GitHub issues) | TensorRT-LLM: N/A
vLLM: 10-23x vs standard | TensorRT-LLM: N/A
vLLM: 15+ formats (HF, GGUF, AWQ, GPTQ, etc) | TensorRT-LLM: N/A
vLLM: 5-10 minutes for basic LLM service | TensorRT-LLM: N/A
vLLM: Via API wrapper | TensorRT-LLM: N/A
vLLM: Community-based | TensorRT-LLM: N/A
Inference Throughput (tokens/sec, Llama 2 7B)
vLLM: 2,000-3,500 tokens/sec | TensorRT-LLM: N/A
vLLM: GPTQ, AWQ, INT4, FP8, INT8 | TensorRT-LLM: N/A
vLLM: 92% linear scaling | TensorRT-LLM: N/A
vLLM: 500-2,000 concurrent | TensorRT-LLM: N/A
vLLM: 2-3 minutes | TensorRT-LLM: 20-30 minutes
vLLM: 3,200 tokens/sec | TensorRT-LLM: 5,800 tokens/sec
vLLM: 30+ architectures | TensorRT-LLM: 15 architectures
vLLM: 1-2 commands | TensorRT-LLM: 8-12 commands
vLLM: 22,000+ stars | TensorRT-LLM: 7,200+ stars
vLLM: 5 methods (GPTQ, AWQ, SqueezeLLM, etc.) | TensorRT-LLM: 6 methods (INT8, FP8, INT4, SmoothQuant, etc.)
How to choose between vLLM and TensorRT-LLM
vLLM vs TensorRT-LLM — Structured Comparison Data
Machine-readable comparison dataset for vLLM vs TensorRT-LLM. Contains 94 attribute comparisons, 5 FAQ pairs.
vLLM vs TensorRT-LLM — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for vLLM vs TensorRT-LLM.
Which is faster for production inference?
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.
Can I easily switch from vLLM to TensorRT-LLM?
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.
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.
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.
Which is better for multi-model serving?
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.
How to Choose Between vLLM and TensorRT-LLM
Setup Complexity: vLLM has pip install + run, while TensorRT-LLM has Clone repo + build + compile + convert models.
Throughput Optimization: vLLM has Good (paged attention, continuous batching), while TensorRT-LLM has Excellent (kernel fusion, INT8/FP8 quantization, specialized kernels).
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)
Inference Latency (7B model, batch=1): vLLM has ~45ms per token, while TensorRT-LLM has ~25-30ms per token.
Community Documentation: vLLM has Extensive (1000+ GitHub stars, active Discord), while TensorRT-LLM has Official Nvidia docs (professional but narrower scope).
Quantization Support: vLLM has GPTQ, AWQ, SqueezeLLM, while TensorRT-LLM has INT8, INT4, FP8, SmoothQuant with kernel optimization.
Multi-GPU Scaling: vLLM has Tensor parallelism, pipeline parallelism, while TensorRT-LLM has Tensor parallelism, pipeline parallelism, all-reduce optimization.