Sepo Global

The GPU revolution was never about raw FLOPS it was about memory bandwidth and parallelism topology. CUDA cores handle general parallel workloads. Tensor cores fuse multiply-accumulate ops for matrix math at the heart of every transformer layer. HBM stacks solved the real bottleneck: feeding data fast enough to keep compute saturated. NVLink and NVSwitch turned clusters of GPUs into a single logical accelerator because no single chip, however dense, can hold a frontier model's weights and activations alone. The shift from FP32 → FP16 → FP8 → FP4 precision isn't a gimmick. It's how the industry keeps scaling compute-per-watt as Moore's Law slows. We didn't just get faster chips. We got a new computing substrate built for matrix algebra at civilizational scale. #GPU #AI #Semiconductors #NVIDIA

NVIDIA is betting that agentic AI's next bottleneck isn't the GPU – it's the CPU! It just unveiled Vera – a new CPU architecture already being tested by AI companies such as Perplexity, alongside its next-generation Rosa CPU with the upcoming Rigel core. ▪️ Basically, CPUs execute the work around AI models. While GPUs run neural networks, CPUs handle everything between model calls: tool use, code execution, data processing, KV-cache management, retrieval and result verification. For AI agents, this work sits on the critical path. • Every agent follows a loop: reason → execute a tool → analyze/verify → reason again. • Each step depends on the previous one, so the speed of a single CPU core affects how quickly an agent completes its task. More CPU cores increase throughput, but not single-agent speed. ➡️ Agentic AI benefits from maximum single-threaded performance. NVIDIA’s Vera represents this new CPU class: • 88 custom Olympus cores • Up to 1.2 TB/s LPDDR5X memory bandwidth • 3.4 TB/s core-to-core bandwidth • 50% higher instructions per cycle than Grace Results are: • 1.8× higher sustained per-core performance than leading x86 CPUs on agentic workloads • In Perplexity’s tests: 1.5× faster coding workflow and up to 1.9× faster concurrent sandbox startup Now CPUs are evolving alongside GPUs and TPUs, because autonomous agents depend not only on how fast GPUs run models, but also on how fast CPUs execute each agent step.

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