arXiv Machine Learning
Systolic Linear Attention: Co-Designing Tile-Grouped Gated Delta Attention for TPUs
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arXiv AI
Jun 12
MiniMax Sparse Attention
arXiv:2606. 13392v1 Announce Type: new Abstract: Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointly attend over hundreds of thousands to millions of tokens, yet the quadratic cost of softmax attention makes this untenable at deployment scale.
arXiv AI
Jun 19
StreamKL: Fast and Memory-Efficient KL Divergence for Boosting Attention Distillation
arXiv:2606. 20005v1 Announce Type: cross Abstract: Attention distillation, which trains one attention distribution to match another by minimizing their Kullback-Leibler (KL) divergence, is widely used in knowledge distillation, model compression, continual learning, and sparse-attention LLM training.
arXiv Computation and Language
2d ago
FlyWheel: Attention Kernels Built for the TPU
FlyWheel introduces optimized TPU kernels for softmax and linear attention, reducing vector work and improving matrix operand reuse. By overlapping independent computations, FlyWheel achieves up to 4.14× speedups over the RPA kernel and 1.80× over Splash Attention, reaching 81% of peak BF16 throughput on TPU v6e. It also boosts Gated DeltaNet prefill by up to 3.61× and enhances vLLM serving of Qwen3.8-27B prefill throughput by up to 1.56× while preserving accuracy.
arXiv AI
Sep 1
DASC: Decay-Aware State Compression for Hybrid Linear-Attention Serving
arXiv:2608.30386v1 Announce Type: cross Abstract: Hybrid linear-attention architectures have recently scaled to large open-weight models, offering quality competitive with full attention while substa...
arXiv AI
Jul 17
A Modern Multimodal Assistant on a 6 GB 2011 GPU: Stage-Validated, All-GPU CUDA Inference for Fermi
arXiv:2607. 14568v1 Announce Type: cross Abstract: A companion study ran a 35B mixture-of-experts model on a 2011 NVIDIA Tesla C2075 (Fermi, sm_20, 6GB) as a GPU-prefill/CPU-decode hybrid, because the 4-bit model did not fit in device memory (arXiv:2606.
arXiv Machine Learning
Oct 15
GQLA: Group-Query Latent Attention for Hardware-Adaptive Decoding
The paper introduces Group-Query Latent Attention (GQLA), a variant of Multi-head Latent Attention that shares group‑sharded up‑projections to enable a joint latent representation and a shardable expanded cache. GQLA supports both MQA‑absorb and GQA execution using the same weights and existing kernels, and roofline analysis shows how absorption trades traffic for computation while GQA shards KV groups. Experiments on DeepSeek‑V3.1‑Base and other models demonstrate that GQLA achieves comparable training losses to MLA, maintains high accuracy after checkpoint conversion, and improves output throughput by up to 35% on eight H20‑3e GPUs, while also allowing larger batch sizes due to a smaller per‑rank cache.