Chip comparison
NVIDIA A100 Tensor Core GPU vs SambaNova SN40L
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 | NVIDIA A100 Tensor Core GPU | SambaNova SN40L |
|---|---|---|
| Process node | TSMC N7 | TSMC N5 |
| Transistors (B) | 54 | — |
| Die size | 826 mm² | — |
| TDP | 400 W | 700 W |
| Memory | 80 GB HBM2e | 1500 GB DDR5 (three-tier: 64 MB SRAM + 64 GB HBM3 + 1.5 TB DDR5) |
| Memory bandwidth | 2,039 GB/s | — |
| FP16 (dense) | 312 TFLOPS | 638 TFLOPS |
| BF16 (dense) | 312 TFLOPS | — |
| FP8 (dense) | — | 1,250 TFLOPS |
| INT8 (dense) | 624 TOPS | — |
| Launch price (list) | $10,000 | — |
| Form factor | SXM4 | PCIe (SN40L DataScale system) |
| Announced | 2020-05-14 | 2023-09-19 |
| Released | 2020-05-14 | 2024-01-01 |
Robots running NVIDIA A100 Tensor Core GPU
No robots publicly running it yet.
Robots running SambaNova SN40L
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Data centers with NVIDIA A100 Tensor Core GPU
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Data centers with SambaNova SN40L
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Common questions
NVIDIA A100 Tensor Core GPU vs SambaNova SN40L: which is faster for training?
SambaNova SN40L has 4.01x the dense FP16/BF16 throughput of the other (NVIDIA A100 Tensor Core GPU: 312 TFLOPS; SambaNova SN40L: 1,250 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
NVIDIA A100 Tensor Core GPU vs SambaNova SN40L: which has more memory?
SambaNova SN40L carries more HBM (NVIDIA A100 Tensor Core GPU: 80 GB HBM2e; SambaNova SN40L: 1500 GB DDR5 (three-tier: 64 MB SRAM + 64 GB HBM3 + 1.5 TB DDR5)). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
NVIDIA A100 Tensor Core GPU vs SambaNova SN40L: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: NVIDIA A100 Tensor Core GPU 0.78 (312 ÷ 400 W); SambaNova SN40L 1.79 (1,250 ÷ 700 W). SambaNova SN40L wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · NVIDIA A100 Tensor Core GPU full page · SambaNova SN40L full page.