I'm building a system for Robotic Process Automation (RPA) that involves multi-step reasoning. I’ve seen that vLLM is the standard, but people are starting to talk about SGLang and "RadixAttention." For a project requiring deep analysis and tool use, which engine provides better control over prefix caching? If I have a 10,000-token system prompt, is vLLM’s PagedAttention enough to handle 50 parallel agents efficiently?
3 answers
For Robotic Process Automation, where you often use the same massive system prompt across multiple agents, prefix caching is your best friend. vLLM recently added Automatic Prefix Caching (APC), which is quite good for simple cases. However, SGLang was built from the ground up for these complex "graph-like" interactions. In my experience with Blockchain automation, if your agents are sharing a huge amount of context for every task, SGLang’s RadixAttention might give you a slight edge in memory reuse. That said, vLLM is much easier to maintain and has a larger ecosystem of community support, which is vital for long-term project stability.
If I choose vLLM, how much VRAM can I actually save with Automatic Prefix Caching for a shared list?
vLLM is definitely the safer bet for production. SGLang is exciting but still feels a bit "experimental" for mission-critical RPA
I agree, Elena. Reliability is king in automation. vLLM has the track record and the corporate backing (like NVIDIA and AMD support) that you need.
Keith, the savings can be dramatic—sometimes up to 80% for the prefix itself. Since the KV cache for the and system prompt is only stored once and then "pointed to" by all 50 agents, you're only paying the memory cost for the new tokens each agent generates. For Project Management tools that need to coordinate many moving parts, this allows you to run far more sophisticated agents on a single A100 than you ever could with standard transformers. Just make sure your prefixes are identical, or the caching won't trigger.