vLLM vs TensorRT-LLM
Compare vLLM and TensorRT-LLM for production LLM serving. Analyze throughput, latency, hardware requirements, and ease of deployment to pick the best inference engine.
Overview
vLLM and TensorRT-LLM are both production-grade inference engines, but they take different paths to achieving high performance. vLLM is an open-source Python library that introduced PagedAttention for efficient KV-cache management and continuous batching for high-throughput serving. It supports a wide range of model architectures out of the box, integrates cleanly with the HuggingFace ecosystem, and can be deployed with minimal configuration. Its accessibility and strong community have made it the default choice for many teams deploying open-weight models in production.
TensorRT-LLM is NVIDIA's first-party solution for squeezing every last drop of performance from NVIDIA GPUs. It works by compiling model graphs into highly optimized TensorRT engines with custom CUDA kernels, fused operations, and hardware-specific optimizations like FP8 quantization on Hopper GPUs. The result is often the lowest possible latency and highest throughput on NVIDIA hardware, but at the cost of a more complex build and deployment process. TensorRT-LLM requires model-specific compilation steps and is tightly coupled to NVIDIA's software stack, making it less portable but exceptionally fast.
Feature Comparison
| Feature | vLLM | TensorRT-LLM |
|---|---|---|
| Ease of setup | pip install, load model, serve | Multi-step build and compile pipeline |
| Peak throughput | Very high | Highest on NVIDIA GPUs |
| Latency optimization | Good with speculative decoding | Best-in-class with fused kernels |
| Continuous batching | ||
| FP8 quantization | Native support with calibration tools | |
| Multi-GPU (tensor parallelism) | ||
| Multi-node inference | Experimental | |
| Model architecture support | Broad (70+ architectures) | Growing (major architectures) |
| Hardware vendor lock-in | Supports NVIDIA, AMD (ROCm) | NVIDIA only |
| HuggingFace integration | Native, load models directly | Requires conversion step |
Strengths
vLLM
- Simple deployment with pip install and a few lines of Python to start serving
- Broad model architecture coverage with rapid support for new open-source models
- Hardware flexibility including AMD GPU support via ROCm
- Active open-source community with frequent releases and contributions
- Native HuggingFace integration eliminates model conversion steps
TensorRT-LLM
- Achieves the absolute lowest latency on NVIDIA GPUs through compiled, fused CUDA kernels
- FP8 quantization on Hopper architecture delivers near-lossless performance at half the memory
- NVIDIA-backed with dedicated engineering for each new GPU generation
- Multi-node inference support for serving the largest models across GPU clusters
- In-flight batching with sophisticated scheduling for consistent latency under load
Which Should You Choose?
vLLM can serve most HuggingFace models immediately without compilation, cutting deployment time from hours to minutes.
TensorRT-LLM's compiled engines and FP8 support extract maximum performance from Hopper GPUs, lowering cost per token.
vLLM supports AMD GPUs via ROCm, while TensorRT-LLM is exclusive to NVIDIA hardware.
TensorRT-LLM's fused kernels and graph-level optimizations deliver the lowest per-token latency achievable on NVIDIA hardware.
vLLM's ability to load HuggingFace models directly avoids the per-model compilation step required by TensorRT-LLM.
Verdict
vLLM and TensorRT-LLM represent a trade-off between ease of use and peak performance. vLLM is the pragmatic choice for most production deployments: it offers excellent throughput, broad model support, hardware flexibility, and minimal operational overhead. Teams that need to iterate quickly, support multiple model architectures, or run on non-NVIDIA hardware will find vLLM far more practical.
TensorRT-LLM is the right choice when you are committed to NVIDIA hardware and need to minimize latency or maximize throughput per GPU at any cost. Large-scale inference providers, latency-sensitive applications, and teams with dedicated ML infrastructure engineers will benefit from the performance gains that TensorRT-LLM's compilation pipeline delivers. Some organizations run both: vLLM for development and staging, TensorRT-LLM for latency-critical production endpoints.
How Ertas Fits In
Ertas AI fine-tunes foundation models and exports them in formats compatible with both vLLM and TensorRT-LLM. For vLLM deployments, Ertas outputs HuggingFace-compatible checkpoints that can be loaded directly. For TensorRT-LLM, Ertas provides the fine-tuned weights that feed into the TensorRT compilation pipeline. Ertas also exports GGUF for local inference scenarios. By handling the fine-tuning complexity, Ertas lets your team focus on optimizing the inference stack rather than the training pipeline.
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