Chip comparison
Intel Gaudi 2 vs NVIDIA A10 Tensor Core GPU
Side-by-side specs
Straight from each vendor's datasheet. Winner in bold: higher throughput, memory, or transistor count; lower power draw or price. Sparse (2:4) throughput roughly doubles the FP8/INT8 numbers shown.
| Field | Intel Gaudi 2 | NVIDIA A10 Tensor Core GPU |
|---|---|---|
| Process node | TSMC N7 | Samsung 8N |
| Transistors (B) | — | 28.3 |
| Die size | — | 628 mm² |
| TDP | 600 W | 150 W |
| Memory | 96 GB HBM2e | 24 GB GDDR6 |
| Memory bandwidth | 2,450 GB/s | 600 GB/s |
| FP16 (dense) | — | 125 TFLOPS |
| BF16 (dense) | 432 TFLOPS | 125 TFLOPS |
| INT8 (dense) | — | 250 TOPS |
| Form factor | OAM | PCIe |
| Announced | 2022-05-10 | 2021-04-12 |
| Released | 2022-09-01 | 2021-04-12 |
Robots running Intel Gaudi 2
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Robots running NVIDIA A10 Tensor Core GPU
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Data centers with Intel Gaudi 2
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Data centers with NVIDIA A10 Tensor Core GPU
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Common questions
Intel Gaudi 2 vs NVIDIA A10 Tensor Core GPU: which is faster for training?
Intel Gaudi 2 has 3.46x the dense FP16/BF16 throughput of the other (Intel Gaudi 2: 432 TFLOPS; NVIDIA A10 Tensor Core GPU: 125 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
Intel Gaudi 2 vs NVIDIA A10 Tensor Core GPU: which has more memory?
Intel Gaudi 2 carries more HBM (Intel Gaudi 2: 96 GB HBM2e; NVIDIA A10 Tensor Core GPU: 24 GB GDDR6). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
Intel Gaudi 2 vs NVIDIA A10 Tensor Core GPU: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Intel Gaudi 2 0.72 (432 ÷ 600 W); NVIDIA A10 Tensor Core GPU 0.83 (125 ÷ 150 W). NVIDIA A10 Tensor Core GPU wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · Intel Gaudi 2 full page · NVIDIA A10 Tensor Core GPU full page.