Disaggregated Inference for Scalable LLMs

Introduction

LLM inference bottlenecks arise from coupled prefill (prompt processing) and decode (token generation) phases. Disaggregation separates them for parallel scaling.

Key Concepts

• Prefill Phase: Computes KV cache from input prompt (compute-intensive).
• Decode Phase: Autoregressive token generation (memory-bound).
• Disaggregation: Run prefill on GPU clusters, decode on separate ones.

Implementation Steps

1. Install Dependencies:

   pip install torch vllm
   

2. Configure vLLM Engine:

   from vllm import LLM, SamplingParams
   llm = LLM(model="meta-llama/Llama-2-7b-hf", tensor_parallel_size=2)
   

3. Separate Prefill/Decode:
   • Prefill: Use high-compute GPUs for batch prompts.
   • Decode: Distribute KV caches to low-latency nodes.
4. Integrate with PyTorch:

   import torch
   

Custom disagg logic: offload decode to remote

outputs = llm.generate(prompts, sampling_params)

## Example

In a chat app: Prefill user queries on cluster A (fast embedding), decode responses on cluster B (streaming output).

## Evaluation
- Metrics: Tokens/sec, latency percentiles.
- Trade-offs: Network overhead vs. resource efficiency.

## Conclusion

Disaggregation unlocks LLM scalability; integrate with orchestration tools like Ray for production.