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HiSparse: Turbocharging Sparse Attention with Hierarchical Memory
Self-attention has become a major bottleneck in scaling LLMs to long contexts because of its quadratic compute and memory/IO cost. This has driven growing interest in efficient attention mechanisms. A...
Highlights of SGLang at NVIDIA GTC 2026
SGLang came to NVIDIA GTC 2026 with panels, a happy hour, a 200-person meetup, and a hands-on training lab. Three days, five events, one packed week at the center of the LLM ecosystem and left with a ...
Elastic EP in SGLang: Achieving Partial Failure Tolerance for DeepSeek MoE Deployments
To serve massive Mixture-of-Experts (MoE) models efficiently, deploying a "wide" Expert Parallelism (EP) strategy—often spanning 32 GPUs or more per inference instance—is not just an option; it is a n...
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