Unlocking the Future of AI with Disaggregated LLM Inference
As AI models grow more powerful, optimizing their infrastructure is crucial. Disaggregation in large language models (LLMs) offers a solution, transforming how businesses leverage AI for efficiency.
Key Insights:
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LLM Inference Phases:
- Prefill Phase: Achieves 90-95% GPU utilization, processing input context efficiently.
- Decode Phase: Operates at 20-40% GPU utilization, generating outputs with higher latency.
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Disaggregated Architectures:
- Separate prefill and decode tasks onto optimized hardware clusters.
- Enhance performance with frameworks like vLLM, SGLang, and TensorRT-LLM—demonstrating up to 6.4x throughput improvements.
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Cost Efficiency:
- Organizations can cut infrastructure costs by 15-40% while improving GPU utilization and energy efficiency.
Why it Matters:
Transitioning to disaggregated serving architectures is essential for businesses aiming to enhance their AI deployments.
Join the Conversation: Share your thoughts on implementing disaggregated LLMs in your organization and how they can drive efficiency and innovation. Let’s explore this game-changing advancement together!
