Accelerating Sonar through speculation

Speculative decoding accelerates large language model inference by having a smaller draft model propose several tokens that a larger target model verifies, allowing multiple accepted tokens plus an additional target-generated token to be emitted in one step. Perplexity applies this approach to reduce inter-token latency for Sonar models through several methods, including conventional target-draft pairing, EAGLE-style feature-aware prediction, and multi-token prediction (MTP) heads that use target hidden states as well as tokens. Target-draft decoding can reuse a fine-tuned small LLM but adds KV-cache memory use and prefill overhead, while EAGLE can reduce memory needs with compact custom draft layers but requires specialized training and tree attention masks that may significantly slow verification. Perplexity therefore emphasizes linear and single-token MTP variants, developing infrastructure to train prediction heads efficiently and finding that restoring RMS normalization layers improved convergence and accuracy for large models. Its inference implementation tightly couples draft and target models, shares scheduling and KV-cache allocation, and uses overlapping CPU and GPU work to limit synchronization costs, with different scheduling strategies tailored to full draft models and faster MTP layers.

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