Technical Direction
AI running on phones, laptops and edge devices: small models, local inference, and progress in on-device chips.
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At a Microsoft event in San Francisco, NVIDIA and Microsoft announced RTX Spark.
Why it matters: NVIDIA and Microsoft lay out the hardware and OS primitives for running agents locally on Windows, with preorder timing and specs.
Google DeepMind launched EmbeddingGemma 2, a 740M-parameter open embedding model under Apache 2.0 that maps text.
Why it matters: The model card numbers let readers judge whether a 740M on-device embedder can replace their current retrieval stack.
Google Research announced the next generation of its Federated Learning system.
Why it matters: Google's TEE-based federated learning design shows how verifiable execution and differential privacy are combined in a production system.
NVIDIA will offer DGX Spark with 64GB of unified memory from Acer, ASUS, Dell, Gigabyte.
Why it matters: The post gives the 64GB configuration's price, memory ceiling and two-unit clustering numbers, so readers can size local agent workloads against it.
IBM Research extended K-Search, the evolutionary kernel search framework from UC Berkeley Sky Lab.
Why it matters: The post details how a structured CUDA-to-MLX translation layer, not the LLM itself, drives the kernel gains.