The Large Model Systems Organization develops large models and systems that are open, accessible, and scalable.
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Agent-Assisted SGLang Development: An Initial Exploration
SGLang development increasingly goes beyond isolated code changes. The same repository now spans LLM serving, distributed runtime, GPU kernels, diffusion pipelines, model-specific execution paths, and...
Improving DeepEP MoE Load Balance in SGLang with Waterfill and LPLB
Mixture-of-Experts (MoE) models rely on Expert Parallelism (EP) to scale inference across multiple GPUs. In SGLang, DeepEP and EPLB provide high-performance serving under EP, but the workload seen by ...
Optimizing Ling-2.6-1T on TPU with SGLang-JAX: Hiding MoE Data Movement Behind Compute with One Pallas Kernel
SGLang-JAX now supports efficient serving of inclusionAI's Ling-2.6-1T on TPU v7x. With a working baseline in place, profiling pointed to the Mixture-of-Experts (MoE) path as the main bottleneck: each...
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