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14 papers from NVIDIA Research on Architecture Design (Transformers, SSMs, MoE)
Forget assuming NaNs and single-bit flips are the main culprits in GPU silent data corruption; this study reveals they're surprisingly rare, demanding a rethink of fault modeling.
Looping language models isn't just for single agents anymore: Recursive Multi-Agent Systems (RecursiveMAS) show that agent collaboration itself can be scaled through recursion, yielding faster and more efficient problem-solving.
Forget GPU-centric designs: AMMA slashes attention latency by 15x and energy consumption by 7x with a memory-centric architecture for long-context LLMs.
Multimodal models can now achieve state-of-the-art performance in real-world tasks like document understanding and audio-video comprehension with significantly reduced inference latency thanks to novel token-reduction techniques.
Forget clunky animation pipelines – MotionBricks lets you assemble real-time, high-quality character motions like LEGOs, even controlling robots.
Open-vocabulary 3D instance segmentation just got 100x faster, thanks to a new transformer architecture that ditches region proposals and fragmented masks.
Squeeze up to 3.2x more performance from your long-context LLMs by intelligently splitting attention computation between CPU and GPU.
Speech-to-speech translation can now convey laughter and tears with human-like fidelity, thanks to a surprisingly data-efficient approach leveraging LoRA experts.
Nemotron 3 Super proves you can achieve comparable accuracy to existing 120B models, but with significantly higher inference throughput, by combining Mamba, Attention, and Mixture-of-Experts.
Gaussian Splatting gets a high-frequency boost: Neural Harmonic Textures unlock significantly more detail in primitive-based 3D reconstructions without sacrificing speed.
Stop wasting precious GPU memory: this new cache-semantic hash table library achieves up to 3.9 billion key-value lookups per second, outperforming standard approaches by up to 9.4x.
Training trillion-parameter Mixture-of-Experts models just got a whole lot faster: Megatron Core now achieves >1 PFLOP/GPU on NVIDIA's latest hardware.
Forget monolithic LoRAs: LoRWeB dynamically mixes a basis set of LoRAs to unlock SOTA generalization in visual analogy tasks.
Achieve state-of-the-art depth completion by adapting 3D foundation models at test time with minimal parameter updates, outperforming task-specific encoders that often overfit.
