Seed Diffusion fundamentally solves the speed bottleneck problem by adopting discrete diffusion technology and parallel decoding scheme. While traditional autoregressive models (e.g., GPT) need to be generated serially word by word, Seed Diffusion adopts a parallel generation mechanism: it first generates an overall fuzzy draft, and then outputs the complete result at once through multiple rounds of refinement. This architecture makes its inference speed up to2146 tokens/s, which is 5.4 times higher than the traditional model. Practice Recommendation:
- Prefer diffusion models that support parallel decoding in code generation scenarios
- Using its two-stage training property (mask diffusion + edit diffusion) to handle complex logic
- Automatically Maintaining the Declaration-Use Order of Code through Constraint Order Diffusion Techniques
This answer comes from the articleSeed Diffusion: Validating High-Speed Language Models for Next-Generation ArchitecturesThe
May not be reproduced without permission:AI productivity tools " How to solve the speed bottleneck of traditional language models during code generation?
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