Olive from MiniMax walks through MiniMax M3, an open-weight model built for agentic coding, 1M token context, and native multimodality from the first training step. The talk centers on MSA, a GQA-aware block-level sparse attention design co-developed with GPU infrastructure in mind, and on why training text and vision together from scratch beats adding vision later.
A workshop by Harshul Jain and Tanmay Sah breaks down LLM inference optimization from first principles, addressing memory, TTFT, and throughput issues. It covers foundational concepts like KV cache mathematics and the prefill/decode phases, then dives into model-side optimizations (quantization, attention variants like GQA, FlashAttention) and serving-side techniques (Paged Attention, continuous batching, prefix caching, KV quantization). The session concludes with benchmarks of popular inference engines like vLLM and SGLang, highlighting their strengths for different workloads, and outlines future research directions.
Cassidy Hardin from Google DeepMind introduces Gemma 4, a new family of open-weight models with significant architectural and performance improvements. This summary covers the four new models (31B Dense, 26B MoE, and two "Effective" on-device models), deep dives into architectural changes like mixed global/local attention and Per-Layer Embeddings (PLE), and details the new native multimodal capabilities for vision and audio.