"EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation"

This paper introduces EchoLM, a system that accelerates LLM serving by employing real-time knowledge distillation using a smaller student model to predict tokens concurrently with a larger teacher model.

Leveraging in-context learning for serving is technically elegant by transforming the LLM serving problem into a dynamic few-shot learning scenario. The two-stage retrieval and bandit routing are crucial innovations for real-time application, addressing the core challenge of balancing example utility with low-latency overhead. The offline distillation and expansion refines the example cache intelligently, maximizing long-term system efficiency without disrupting online performance.

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Paper - https://arxiv.org/abs/2501.12689

Original Problem 😞:

→ Serving Large Language Models is computationally expensive due to their massive size and sequential token generation process.

→ This leads to high latency and limits the throughput of LLM services.

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Solution in this Paper 💡:

→ This paper proposes EchoLM, a novel serving system to accelerate LLM inference.

→ EchoLM uses real-time knowledge distillation.

→ It employs a smaller student LLM alongside a larger teacher LLM.

→ The student model predicts tokens in parallel with the teacher model's processing.

→ A lightweight reconciliation network merges predictions from both models.

→ This network is trained via knowledge distillation to align student predictions with the teacher's.

→ EchoLM allows for faster initial token generation from the student.

→ It refines these predictions with the teacher's knowledge, improving accuracy.

→ The system incorporates an intelligent cache to further reduce redundancy and latency.

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Key Insights from this Paper 🤔:

→ Real-time knowledge distillation is effective for LLM serving acceleration.

→ Parallel processing using student and teacher models significantly reduces latency.

→ A reconciliation network can effectively merge predictions, maintaining accuracy.

→ Caching mechanisms are crucial for optimizing performance in repetitive queries.

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Results 🚀:

→ EchoLM reduces end-to-end latency by 1.5x to 2.1x compared to teacher-only inference, at the 99th percentile latency.

→ It achieves up to 1.9x higher throughput than teacher-only serving under high load.

→ EchoLM's reconciliation network maintains within 1% perplexity of the teacher model, ensuring minimal accuracy loss.