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Nvidia’s TiDAR experiment could speed up AI token generation using hybrid diffusion decoder — new research boasts big throughput gains, but limitations remain
- Nvidia's TiDAR shows multi-token decoding boosts, delivering up to about 5.9x throughput on small LLM backbones.
- The study reports 4.71x and 5.91x throughput gains for 1.5B and 8B parameter models, respectively.
- TiDAR uses a three-region attention mask to allow diffusion drafting while keeping the autoregressive cache valid.
- Inference tested on 1.5B and 8B models shows speedups without measurable accuracy loss on key benchmarks.
- The authors caution results are preliminary and bound to small-scale models with standard PyTorch setups.
- Memory bandwidth and model size are cited as limiting factors for scaling TiDAR to larger models.
- The paper used a single H100 and standard PyTorch with FlexAttention for the reported results.
- TiDAR blends autoregressive and diffusion objectives by training on a fully masked copy of the sequence.
- Results suggest potential for higher per-GPU throughput in cloud settings and consumer inference with further engineering.
- The study highlights potential efficiency gains from reducing memory movement during next-token generation.
- The article notes the research remains exploratory and compares TiDAR to other diffusion and speculative decoding methods.
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