Table of Contents
TensorRT-LLM is NVIDIA's high-performance LLM library; vLLM is the open-source ecosystem default. TensorRT-LLM has higher throughput; vLLM has dramatically better ergonomics. The trade-off is essentially complexity vs raw speed.
TensorRT-LLM: +15-30% throughput on Hopper / Blackwell. Cost: 5-30 minute engine build per model + checkpoint, less flexibility, more setup. vLLM: ergonomics + ecosystem + flexibility. For high-throughput single-model production at scale: TensorRT-LLM. For everything else: vLLM.
Comparison
| Aspect | vLLM | TensorRT-LLM |
|---|---|---|
| Throughput on Hopper | High | ~+25% |
| Throughput on Blackwell | High | ~+15-20% |
| Setup time | ~5 minutes | ~30 minutes per model |
| Engine build per checkpoint | No (load directly) | Yes (5-30 min) |
| Ecosystem support | Broad | NVIDIA-specific |
| Multi-LoRA | Native + flexible | Native but stricter |
| Open source | Yes | Yes (since 2023) |
| NVIDIA-only | No (ROCm partial) | Yes (NVIDIA only) |
When each
- vLLM for: experimentation, multi-model platforms, OpenAI-compatible production, frequent model updates, agency / multi-tenant LoRA
- TensorRT-LLM for: single-stable-model production at scale where throughput is the deciding factor, NVIDIA-only deployments, ops team comfortable with engine-build workflow
- SGLang: structured output / agent workloads (separate niche)
Verdict
For 90% of self-hosted AI deployments, vLLM is the right default. TensorRT-LLM is worth the operational complexity only when single-model production at high throughput justifies the ~25% throughput gain. The gap will narrow as vLLM continues to optimise for Blackwell; for new deployments today, vLLM is usually still the right starting point.
Bottom line
vLLM default; TensorRT-LLM for max-throughput single-model. See TensorRT-LLM guide.
