GenAI Inference Engines: TensorRT-LLM vs vLLM vs Hugging Face TGI vs LMDeploy

TensorRT-LLM: NVIDIA’s Powerhouse for Optimized Inference

TensorRT-LLM is NVIDIA’s answer for making large language models run fast and smooth. Built on their TensorRT framework, it’s designed to squeeze every drop of performance out of NVIDIA GPUs. It does this with tricks like layer fusion, precision tweaking (FP16, INT8, FP8...), and kernel optimization that cuts down compute time without breaking the model’s accuracy.

It’s not just about speed. TensorRT-LLM handles big models efficiently by managing memory smartly, so you don’t crash mid-run. It also supports dynamic batching, letting you process multiple requests at once without running out of memory. If you’re already using NVIDIA hardware, it’s a no-brainer as it’s tightly glued to their ecosystem, like CUDA and cuDNN, and it even integrates with NVIDIA’s Triton Inference Server and NVIDIA Dynamo.

That said, it’s not perfect for everyone. Setup can be a pain if you’re not comfortable with NVIDIA’s tools, and it’s less flexible if you’re on non-NVIDIA hardware. Still, for raw power and optimization on NVIDIA GPUs, it’s tough to beat.

vLLM: High-Throughput Inference with PagedAttention

vLLM is very good at handling high volumes of inference jobs fast. It’s an open-source project that shines when you need high throughput without slowing down. The secret sauce is PagedAttention, a trick that manages memory way better than most. Instead of loading everything at once and eating up RAM, it splits the key-value caches into chunks, only grabbing what’s needed. Less waste, more speed.

It’s very flexible too. Works with popular models like LLaMA or Mistral right out of the box, and it supports many hardware, including NVIDIA or AMD GPUs. You also get dynamic batching to group requests efficiently, keeping the pipeline running smoothly. Setup is pretty straightforward if you’re used to Python and PyTorch.

The primary limitation of vLLM is its relative immaturity in the market, which means it may not yet offer the comprehensive feature set available in more established solutions. However, for organizations seeking an efficient solution that delivers high-performance inference, vLLM represents an excellent choice.

Hugging Face TGI: A Production-Ready Solution for Text Generation

Hugging Face TGI (Text Generation Inference) is made for people who want to get models up and running without a headache. It’s a tool built by the Hugging Face team, so it plays nice with their massive library of pre-trained models—think BERT, GPT, and more. It’s designed for real-world use, like powering chatbots or apps where text generation has to work fast and not crash.

TGI handles the heavy lifting with features like continuous batching, which keeps the system busy by swapping in new requests as old ones finish. It supports GPU acceleration and can scale up if you’ve got the hardware. Plus, it’s got built-in safety—like filtering out bad outputs—which is handy for production. You can deploy it with Docker in a few steps, so it relatively easy to set up.

The catch? It’s tied to Hugging Face’s ecosystem, so if you’re not already in that world, it might feel restrictive. Still, for a plug-and-play option that’s ready to use, TGI is a great choice.

LMDeploy: Efficient Deployment with Superior Decoding Speed

LMDeploy is a toolkit from the MMRazor and MMDeploy teams, built to compress, deploy, and run large language models without fuss. What makes it stand out? It has got excellent decoding speed—up to 1.8x more requests per second than vLLM on an A100 GPU. That is thanks to tricks like persistent batching, blocked KV caching, and slick CUDA kernels that keep the GPU busy.

It has two engines: TurboMind for max performance and a PyTorch one for easier tinkering. TurboMind is the star here—it pushes 4-bit inference 2.4x faster than FP16, and it handles big models like Llama-2 70B with ease. You can also quantize weights and KV caches to save memory without harming accuracy. Deployment is a breeze too—one command sets up a server across multiple machines if you need it. Plus, it remembers chat history in multi-round talks, so it doesn’t waste time redoing old work.

The downside? TurboMind is picky—it doesn’t play nice with sliding window attention models like Mistral yet. And if you’re not on NVIDIA GPUs, you’re stuck with the slower PyTorch engine. Still, for speed and simplicity on the right hardware, LMDeploy is a great choice.

Performance Comparison: Latency, Throughput, and Scalability

Let’s break down how these engines stack up on latency (how fast one request finishes), throughput (how many requests they can ingest), and scalability (how well they handle bigger loads or more hardware).

TensorRT-LLM shines on latency if you have NVIDIA GPUs. It’s highly optimized for NVIDIA hardware, so single requests finish quickly: under 50ms for most models on an A100. Throughput is excellent too, especially with dynamic batching. Benchmarks by BentoML show that this engine reaches 700 tokens per second at 100 concurrent users for Llama 3 70B Q4 on an A100 80GB GPU. TensorRT-LLM performs stronglyin scenarios with long inputs and high request rates, offering good throughput. Scalability on multiple GPUs is supported out of the box with excellent performance.

vLLM has good throughput too, particularly in decode-heavy workloads, with high throughput and low latency after recent updates. Benchmarks by BentoML show that this engine reaches 600-650 tokens per second at 100 concurrent users for Llama 3 70B Q4 on an A100 80GB GPU. Latency is good but not as good as TensorRT-LLM: around 60-80ms for solo runs. It scales well across GPUs, even mixing brands, but it’s less polished for huge setups.

Hugging Face TGI performs similarly to vLLM, providing a balance of performance and ease of use. Latency is decent: 50-70ms on a good GPU. Benchmarks by BentoML show that this engine reaches 600-650 tokens per second at 100 concurrent users for Llama 3 70B Q4 on an A100 80GB GPU. It’s built to scale for production, so it handles more users or machines smoothly, especially with Docker.

LMDeploy wins on decoding speed. It excels in token generation rate, especially for smaller models, and has low Time to First Token (TTFT) for quantized large models. Latency is low: 40-60ms. And throughput is excellent. Benchmarks by BentoML show that this engine reaches 700 tokens per second at 100 concurrent users for Llama 3 70B Q4 on an A100 80GB GPU. Scaling is easy with its server setup, but it leans hard on NVIDIA GPUs for the best results; PyTorch mode lags behind.

Bottom line: TensorRT-LLM and LMDeploy lead on raw speed. Your pick depends on your hardware and how many requests you are handling.

Quantization Capabilities

Quantization reduces model precision to lower memory usage and speed up inference, which is important for resource-constrained environments. Here’s how each engine performs:

TensorRT-LLM supports FP8, FP4, INT4 with Activation-aware Weight Quantization (AWQ), and INT8 with SmoothQuant, offering robust options for optimizing NVIDIA GPU performance.

vLLM provides flexibility with GPTQ, AWQ, INT4, INT8, and FP8, adapting to various hardware and precision needs.

Hugging Face TGI integrates bitsandbytes for 8-bit and 4-bit quantization and GPT-Q for weight-only quantization, suitable for production deployments.

LMDeploy offers 4-bit AWQ, 8-bit quantization, and online INT8/INT4 KV cache quantization, enhancing efficiency for large models on limited hardware.

Hardware Compatibility

Hardware support determines where you can deploy these engines, impacting scalability and performance:

TensorRT-LLM is exclusive to NVIDIA CUDA, leveraging GPU accelerators for high performance.

vLLM supports NVIDIA CUDA, AMD ROCm, AWS Neuron, and CPU, offering broad compatibility for diverse setups.

Hugging Face TGI works with NVIDIA CUDA, AMD ROCm, Intel Gaudi, and AWS Inferentia, providing flexibility for various hardware environments.

LMDeploy is optimized for NVIDIA CUDA, ensuring top performance on NVIDIA GPUs but with limited support for other platforms.

Ease of Use

The setup and integration process can affect development timelines. Here’s how each engine fares:

TensorRT-LLM requires converting checkpoints, building the TensorRT engine, and configuring parameters, making it challenging and time-consuming for engineers.

vLLM is user-friendly with comprehensive documentation, easy installation, and seamless Python library integration.

Hugging Face TGI benefits from Hugging Face’s ecosystem, offering pre-built Docker images and thorough documentation for quick deployment.

LMDeploy features a simple setup with a single command to launch the server and Python APIs for customization, balancing ease with flexibility.

Conclusion

Picking the right engine depends on your use case, hardware, and skills.

TensorRT-LLM is your go-to inference engine if you are running big models on NVIDIA GPUs and need every ounce of speed (think low-latency applications like real-time chatbots or AI assistants where replies have to be generated in milliseconds). It’s perfect for companies already deep in NVIDIA’s world, but it might be overkill if you are looking for simplicity.

vLLM is a great trade-off between speed and simplicity. It works really well for startups or researchers who want something flexible and quick to set up.

Hugging Face TGI is equivalent to vLLM in terms of speed and simplicity. It’s easy to deploy, scales smoothly, and ties into Hugging Face’s model hub, so it’s ideal for teams wanting a no-fuss solution.

LMDeploy shines on performance, like TensorRT-LLM. It suits users with NVIDIA GPUs who want simple setup and top performance, but it’s less handy if your models don’t play nice with TurboMind.

If you cannot or do not want to deploy your own GenAI model yourself, you can use NLP Cloud and leverage fast generative AI models at scale in production. Try fast inference on NLP Cloud now!

If you have questions about inference engines in general, please don't hesitate to ask us, it's always a pleasure to advise!

Julien
CTO at NLP Cloud