NVIDIA B100 Blackwell AI GPU

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✓ Type: B100 Discrete Accelerator

✓ Memory Clock: 8Gbps HBM3E

✓ Memory Bus Width: 2×4096-bit

✓ Memory Bandwidth: 8TB/sec

✓ VRAM: 192GB (2x96GB)

✓ FP4 Dense Tensor: 7 PFLOPS

✓ INT8/FP8 Dense Tensor: 3.5 P(FL)OPS

✓ FP16 Dense Tensor: 1.8 PFLOPS

✓ TF32 Dense Tensor: 0.9 PFLOPS

✓ FP64 Dense Tensor: 30 TFLOPS

✓ Interconnects: NVLink 5 (1800GB/sec) + PCIe 6.0 (256GB/sec)

✓ GPU: “Blackwell GPU”

✓ GPU Transistor Count: 208B (2x104B)

✓ TDP: 700W

✓ Manufacturing Process: TSMC 4NP

✓ Interface: SXM-DELTA NEXT 6

✓ Architecture: Blackwell

Pricing and availability will be released soon, expected in Q3 2024.

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Description

B100 Discrete Accelerator: Revolutionizing Computational Power

The B100 Discrete Accelerator marks a significant milestone in the evolution of computing hardware, engineered to cater to the demanding needs of high-performance computing and artificial intelligence. At the heart of its design lies the state-of-the-art 8Gbps HBM3E memory, paired with a dual 4096-bit memory bus, facilitating an unprecedented memory bandwidth of 8TB/sec and a generous 192GB of VRAM. Its computational capabilities are equally impressive, with the accelerator capable of achieving up to 7 PFLOPS in FP4 Dense Tensor operations, 3.5 P(FL)OPS in INT8/FP8, and extending its prowess to 1.8 PFLOPS and 0.9 PFLOPS in FP16 and TF32 tensor operations, respectively, culminating in a robust 30 TFLOPS for FP64 Dense Tensor computations. Integration with high-speed NVLink 5 and PCIe 6.0 interfaces ensures seamless communication at speeds up to 1800GB/sec and 256GB/sec, respectively. The heart of this computational giant is the "Blackwell GPU," boasting a transistor count of 208 billion, all within a surprisingly efficient 700W thermal design power (TDP). Manufactured using TSMC's 4NP process and potentially featuring the next-generation SXM interface, the B100 Discrete Accelerator embodies the pinnacle of the Blackwell architecture's engineering excellence, setting a new benchmark for computational efficiency and performance in the realm of discrete accelerators.

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