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Recent advances in inference optimization are focusing on enhancing efficiency and accuracy across various machine learning models. Techniques such as state-space duality algorithms and inference-time steering methods are allowing for significant reductions in computational overhead while maintaining performance. For instance, recent work demonstrates that inference can be executed without the need for custom kernels, enabling seamless deployment across different hardware platforms. Additionally, methods like CORAL are improving model calibration during inference, leading to substantial accuracy gains without the costly process of retraining. The introduction of new data formats, such as HiFloat4, is also optimizing resource usage, reducing power consumption while enhancing model performance. Overall, the field is shifting towards more adaptable and resource-efficient approaches that can address the growing demands of large-scale AI applications, providing solutions that are not only technically sound but also commercially viable in diverse operational environments.
State-space model releases are typically coupled to fused CUDA and Triton kernels, inheriting a hard dependency on NVIDIA hardware. We show that Mamba-2's state space duality algorithm -- diagonal sta...
Large language models (LLMs) exhibit persistent miscalibration, especially after instruction tuning and preference alignment. Modified training objectives can improve calibration, but retraining is ex...
Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, planning, and structur...
This paper introduces HiFloat4 (HiF4), a block floating-point data format tailored for deep learning. Each HiF4 unit packs 64 4-bit elements with 32 bits of shared scaling metadata, averaging 4.5 bits...
Inference in large-scale AI models is typically performed on dense parameter matrices, leading to inference cost and system complexity that scale unsustainably with model size. This limitation does no...
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}Use This Via API or MCP
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