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gpu-inference-cost

A collection of 3 posts

BEVPoolV3 Is the GPU Optimization Physical AI Actually Needs

BEVPoolV3 Is the GPU Optimization Physical AI Actually Needs

Physical AI keeps getting sold from the top of the stack down: humanoids, autonomous vehicles, warehouse robots, spatial computers, “world models,” and whatever phrase survived the keynote draft. NVIDIA’s latest developer post is useful because it starts where production systems usually hurt: a scatter-reduce kernel that has to finish

AWS Trainium Wants to Be a Merchant Chip. NVIDIA’s Moat Is the Software Gravity Around the Chip

AWS Trainium Wants to Be a Merchant Chip. NVIDIA’s Moat Is the Software Gravity Around the Chip

AWS selling Trainium racks outside its own cloud would be more than a product-line expansion. It would be Amazon volunteering for a harder exam: proving its AI silicon can survive outside the managed AWS cocoon, where customers expect not just cheaper tokens but debuggable compilers, boring operations, mature frameworks, predictable

Agentic RAG Does Not Need Another Framework. It Needs Fewer PCIe Road Trips

Agentic RAG Does Not Need Another Framework. It Needs Fewer PCIe Road Trips

Agentic RAG has spent the last year collecting abstractions. Planners, routers, memory layers, tool registries, tracing dashboards, evaluation harnesses — useful pieces, mostly. But Anubhab Banerjee’s CUDA Top-K retrieval experiment points at a less glamorous problem that many teams still under-measure: the retrieval step keeps crossing the PCIe border like