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Vera provides about 1.2 TB/s of memory bandwidth and, according to NVIDIA, delivers up to 3X the bandwidth and about 40% lower memory latency versus AMD EPYC Turin. This higher bandwidth and lower latency prevent the bandwidth and memory wall that chiplet x86 designs hit, giving Vera more effective per-core data throughput. That matters for agentic AI workloads because those workloads combine large instruction footprints, frequent control-flow changes, and heavy data movement, so higher bandwidth and lower latency keep execution resources utilized and improve runtime performance. Answered

NVIDIA reports Vera delivers up to a 1.9X IPC improvement versus AMD EPYC Turin across various AI workflows. NVIDIA says the largest gains appear on workloads with large instruction footprints and frequent control-flow changes, specifically agent runtimes, interpreters, compilers, graph analytics, and data-processing frameworks, thanks to Olympus core features like advanced branch prediction and a 10-wide decode engine. Answered

Vera uses 88 custom Olympus cores versus EPYC Turin's 128 cores, but pairs them with much higher memory bandwidth and lower latency , 1.2 TB/s of LPDDR5X bandwidth and NVIDIA claims up to 3X the bandwidth with 40% lower latency. For AI data-processing tasks this tradeoff favors Vera: its greater per-core bandwidth, lower latency, and higher IPC yield better real-world results (NVIDIA reports 1.5X faster data processing and 1.8X faster Python), so the lower core count is offset by substantially higher memory and execution efficiency. Answered