NVIDIA 的扩散语言模型打破了传统逐 token 生成瓶颈，做推理加速或大模型部署的团队可以直接拿来提升 GPU 利用率，值得关注。
NVIDIA 发布了 Nemotron-Labs-Diffusion 系列扩散语言模型，与传统逐 token 生成不同，该模型能在单次推理中并行生成多个 token，并支持在生成过程中进行修订。这种设计充分利用了现代 GPU 的并行计算能力，显著提升推理速度。模型系列包含 3B 到 14B 参数规模，并提供了视觉-语言变体。目前该模型已开源可用。
Most language models only generate one token at a time. We just released Nemotron-Labs-Diffusion, a...
Most language models only generate one token at a time. We just released Nemotron-Labs-Diffusion, a family of diffusion language models that take a different approach, generating multiple tokens in parallel within a single model. Rather than committing to each token permanently, these models can revise as they go, resulting in faster inference that better utilizes modern GPUs. The full model family ranges from 3B to 14B, including vision-language variants. Available now: nvda.ws/4tEnTxP 💬 8 🔄 18 ❤️ 128 👀 6220 📊 30 ⚡