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Person: "clattner_llvm"
not much happened today
dflash nemo-automodel claude openai broadcom qualcomm modular nvidia skypilot modal anthropic hugging-face hardware inference performance-optimization model-training agent-ux security capability-based-security open-source fine-tuning infrastructure model-optimization gdb kimmonismus scaling01 clattner_llvm karpathy gallabytes dabit3 kentonvarda random_walker jubbaonjeans victormustar
OpenAI announced Jalapeño, its first custom AI chip for LLM inference, built with Broadcom, aiming to control more of the AI stack and improve compute economics with a fast 9-month design cycle. Community analysis suggests Jalapeño features 216GB HBM3E, ~7.1–7.4 TB/s bandwidth, and ~10 PFLOPS FP4 performance, signaling hyperscaler-style inference silicon as a new standard. Meanwhile, Qualcomm is acquiring Modular, with Mojo open-sourcing on track, indicating rising competition in vertically integrated inference stacks beyond NVIDIA/CUDA. On infrastructure, NVIDIA's NeMo AutoModel boosts training throughput for MoE models by 3.4–3.7x, and startups like SkyPilot and Modal advance unified and open-source inference solutions. Custom training of DFLASH models yields 30–50% decode gains. In UX, Anthropic's Slack-native Claude agent shifts agent interaction from tools to coworkers, raising new security and cost concerns around identity, permissions, and lock-in, with debates on capability-based security and attribution. Hugging Face responded with its self-hosted Slack coding agent Moon Bot.
Gemma 4
gemma-4 gemma-4-31b gemma-4-26b-a4b google-deepmind multimodality long-context model-architecture moe local-inference model-optimization function-calling quantization jeffdean _philschmid rasbt ggerganov clattner_llvm julien_c clementdelangue
Google DeepMind released Gemma 4, a family of open-weight, multimodal models with long-context support up to 256K tokens under an Apache 2.0 license, marking a major capability and licensing shift. The lineup includes 31B dense, 26B MoE (A4B), and two edge models (E4B, E2B) optimized for local and edge deployment with native multimodal support (text, vision, audio). Early benchmarks show Gemma-4-31B ranking #3 among open models and strong scientific reasoning performance with 85.7% GPQA Diamond. Day-0 ecosystem support includes llama.cpp, Ollama, vLLM, and LM Studio, with notable local inference performance on hardware like M2 Ultra and RTX 4090. The architecture features hybrid attention and MoE layering, diverging from standard transformers. Community and developer engagement is high, with rapid adoption and tooling integration.