Efficient Inference with Command R+ - Optimizing Speed and Cost for Enterprise AI

This course examines techniques for improving Command A inference speed and cost, including sliding-window attention, speculative decoding, and guided decoding. It also discusses evaluation, speculative-training data, and performance tradeoffs.

Syllabus

0:00 – Introduction to Command R+ Inference Optimization
0:55 – Sparse Attention Architecture & Sliding Window
2:21 – Speculative Decoding Overview
4:32 – Using Medusa for Parallel Token Prediction
6:29 – Evaluation and Training with W&B
7:54 – Synthetic vs. Original Data in Speculative Training
9:00 – Final Gains and Performance Tradeoffs
11:44 – Guided Decoding with Speculative Inference
14:29 – Dynamic Guided Decoding and FSM Integration
19:03 – Combining Guided Decoding with Speculative Tokens