When deploying large language models on Runpod, choosing the right inference framework can dramatically impact both performance and cost efficiency. While vLLM has dominated the high-throughput inference space, SGLang emerges as the clear winner for a specific but increasingly important use case: multi-turn conversations with shared context.

Understanding Multi-Turn Conversations and Caching

Most production AI applications handle complex, multi-turn interactions where context builds over time, such as customer support chatbots, coding assistants, or educational tutoring systems. Both vLLM and SGLang recognize that reprocessing identical context repeatedly is wasteful, but they solve this problem differently.

vLLM's Automatic Prefix Caching vs SGLang's RadixAttention

vLLM's Automatic Prefix Caching (APC):

• Caches exact prefix matches using block-level storage
• Requires identical token sequences to trigger cache hits
• Optimized for batch inference where multiple requests share exact prefixes
• Works best with templated prompts and structured batch processing
• Manual configuration often needed for optimal cache utilization

SGLang's RadixAttention:

• Uses a radix tree structure for more flexible prefix matching
• Automatically detects and caches partial overlaps in conversation context
• Designed for dynamic multi-turn conversations with evolving context
• Zero configuration - automatically optimizes cache usage patterns
• Better handles branching conversations and varied interaction patterns

The Key Difference: Static vs Dynamic Optimization

The fundamental difference lies in their design philosophy:

vLLM excels when you can predict and structure your caching patterns. If you're running batch inference on templated prompts or have consistent request patterns, vLLM's APC provides excellent performance with precise control.

SGLang shines in unpredictable, dynamic scenarios where conversation flows vary. Its radix tree approach automatically discovers caching opportunities that would require manual optimization in vLLM.

Prompting example

Here's an example of what setting the stage for a multi-turn prompt might look like:

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And here's the multi-turn prompting we will be doing for our tests:

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You can see that during this test, it successively builds on each prior example. Notice how each user question is completely different:

• "what would you recommend for training a large language model on a budget?"
• "how would you optimize inference costs for a production deployment?"
• "What are the key considerations for distributed training mentioned in the context?"

RadixAttention's tree structure elegantly handles this pattern. It caches the shared system context once, then efficiently processes each unique user query. The cache hit covers the expensive part (processing thousands of tokens of technical context), while only the small user queries need fresh computation.

Testing parameters

To get some benchmarking numbers, I used 2X H100 SXM pods in Secure Cloud, using deepseek-ai/DeepSeek-R1-Distill-Llama-70B. Running the prompt, I get the following results.

Large Context RadixAttention Analysis

So, hitting the cache on a 7k size prompt results in about a ~20% increase, and roughly matching the speed on a small prompt with no context at all.

vLLM Benchmark Results

The results paint a picture - on fresh context, the two engines are relatively equally matched. However, RadixAttention gives a clear benefit in the larger multi-turn conversations, especially when the cache is involved, giving about a 10% boost over vLLM at the same context loads. These benchmark results translate to significant cost savings in production scenarios. Consider a customer support chatbot handling 1,000 conversations per hour, where each conversation averages 5 turns with substantial context. The 10-20% performance improvement from SGLang's RadixAttention means significant compute save, especially in a serverless environment where compute is paid for by the second.

When to Choose Each Framework

Choose SGLang when:

• Building conversational AI applications with unpredictable dialog flows
• Handling customer support, tutoring, or coding assistance use cases
• Working with large context windows that vary between conversations
• Prioritizing zero-configuration optimization
• Deploying applications where conversation context frequently overlaps but isn't identical

Choose vLLM when:

• Running batch inference with predictable, templated prompts
• Handling high-throughput scenarios with exact prefix matches
• Need fine-grained control over caching behavior
• Working with structured workflows where request patterns are consistent
• Prioritizing maximum throughput over conversation-specific optimizations

See for yourself

I've written a Jupyter notebook and some handy scripts that let you run the same prompt against both engines and it will tell you which is better for your GPU spec and your prompt, using the same real world hardware that you'll be using in production. Download it from GitHub here.

The script provides detailed metrics for each engine. Here's an example for a simple one-shot prompt (Do an in-depth historical analysis of the Declaration of Independence)‍

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So in this one-shot case, vLLM actually came up on top - so clearly, it's not as simple as just picking one package or another every single time.

Conclusion

We offer both SGLang and vLLM in our Quick Deploy endpoints - but to date, we haven't been super clear about why you would want to use one over the other. We want to empower you to choose the right one for the job. Both engines are very powerful and flexible, but the truth is there's going to be one tool that's better for any particular job, and we want to empower you to choose the right one for the job. Now, we have a notebook that will help you do that.

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