Which LLM inference engine should you choose?

When you want to run large language models (like ChatGPT) in your own applications, you need something called an “inference engine” - think of it as the software that makes your AI model actually work. But with so many options out there, how do you know which one to pick?

Let me break down the most popular ones and help you choose the right one for your needs.

The Real-World Problem

Imagine you’re building a customer service chatbot for a big online store. You need it to handle thousands of customer questions at the same time, and it needs to respond quickly (under 200 milliseconds). Which tool should you use?

1. vLLM - The Popular Choice

vLLM is like the Swiss Army knife of inference engines. It’s fast, reliable, and works well for most situations.

What it’s good at:

• Handles about 180-220 requests per second
• Responds in 80-120 milliseconds
• Uses less memory than other options
• Easy to set up and use

Best for: Most projects, especially when you want something that just works without too much hassle.

2. SGLang - The Smart One

SGLang is great when you need your AI to do more than just chat - like filling out forms, following specific instructions, or working in cloud environments.

What it’s good at:

• Handles about 120-160 requests per second
• Responds in 150-200 milliseconds
• Much faster for complex tasks (3-5x faster than others)
• Works great in cloud environments

Best for: Projects where you need structured responses or are running in the cloud.

3. KTransformers - The Lightweight Option

KTransformers is like the energy-efficient car of inference engines. It doesn’t need a powerful GPU and can run on regular computers.

What it’s good at:

• Handles about 25-35 requests per second
• Responds in 400-600 milliseconds
• Only needs 2-4GB of RAM
• Works on regular computers (no fancy GPU needed)

Best for: Small projects, edge devices, or when you’re on a budget.

4. TensorRT-LLM - The Speed Demon

TensorRT-LLM is the fastest option, but it only works with NVIDIA graphics cards. It’s like having a sports car - very fast, but you need the right hardware.

What it’s good at:

• Handles about 250-300 requests per second (the fastest!)
• Responds in 40-80 milliseconds (also the fastest!)
• Can compress models to save space
• Optimized for NVIDIA hardware

Best for: When you need maximum speed and have NVIDIA hardware.

5. Triton - The Enterprise Choice

Triton is like the professional tool that big companies use. It can handle multiple models at once and has lots of enterprise features.

What it’s good at:

• Handles about 200-250 requests per second
• Responds in 70-110 milliseconds
• Can run multiple models at the same time
• Built-in monitoring and security features

Best for: Big companies or when you need to run multiple AI models.

6. ONNX Runtime - The Flexible One

ONNX Runtime works on almost any hardware and operating system. It’s like the universal adapter of inference engines.

What it’s good at:

• Handles about 140-180 requests per second
• Responds in 120-160 milliseconds
• Works on Windows, Mac, and Linux
• Works with both GPU and CPU

Best for: When you need flexibility or are working across different platforms.

7. DeepSpeed - The Memory Saver

DeepSpeed is great when you’re working with really big models and need to save memory. It’s like having a smart storage system.

What it’s good at:

• Handles about 180-220 requests per second
• Responds in 100-140 milliseconds
• Uses 60-80% less memory than other options
• Great for very large models

Best for: When you’re working with huge models or have limited memory.

Two More Worth Mentioning

8. Text Generation Inference (TGI)

Made by Hugging Face, this is like the “enterprise-ready” version with built-in monitoring and security.

What it’s good at:

• Handles about 160-200 requests per second
• Responds in 90-130 milliseconds
• Built-in monitoring and security features
• Easy to deploy in production

Best for: Production environments where you need monitoring and security.

9. LMDeploy

Made by Alibaba, this is another fast option with some advanced features.

What it’s good at:

• Handles about 220-260 requests per second
• Responds in 70-100 milliseconds
• Advanced optimization techniques
• Good for high-performance needs

Best for: When you need high performance and have the technical expertise.

Real-World Performance Test

Here’s what happened when researchers tested these engines on the same hardware (A100 GPU, 40GB memory):

What this tells us:

• TensorRT-LLM is the fastest but only works with NVIDIA hardware
• vLLM gives you the best balance of speed and ease of use
• Triton is great when you need to run multiple models
• SGLang is best for complex tasks
• DeepSpeed is best for big models

Simple Comparison Chart

What Should You Choose?

If you have a powerful NVIDIA GPU (A100/H100):

• Want maximum speed? Go with TensorRT-LLM
• Want something that just works? Go with vLLM
• Running a big company? Go with Triton

If you have an older NVIDIA GPU (T4/V100):

• Best overall choice: vLLM
• Want to save memory? Go with DeepSpeed
• Need flexibility? Go with ONNX Runtime

If you only have a regular computer (CPU only):

• Best performance: KTransformers
• Need compatibility? Go with ONNX Runtime
• Want to save resources? Go with KTransformers

My Recommendation

For most people, I’d recommend starting with vLLM. Here’s why:

• It’s fast enough for most needs
• It’s easy to set up and use
• It has a great community and lots of support
• It works well in most situations

If you need maximum speed and have NVIDIA hardware, try TensorRT-LLM. If you’re building something for a big company, consider Triton. And if you’re just getting started or on a budget, KTransformers is a good choice.

The key is to think about your specific needs: How fast do you need it to be? What hardware do you have? How much time do you want to spend setting it up? Answer these questions, and you’ll find the right tool for your project.