Chip comparison
Groq LPU (Language Processing Unit) 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 | Groq LPU (Language Processing Unit) | NVIDIA A10 Tensor Core GPU |
|---|---|---|
| Process node | GlobalFoundries 14nm | Samsung 8N |
| Transistors (B) | — | 28.3 |
| Die size | — | 628 mm² |
| TDP | 375 W | 150 W |
| Memory | 0.23 GB SRAM (230 MB on-die) | 24 GB GDDR6 |
| Memory bandwidth | 80,000 GB/s | 600 GB/s |
| FP16 (dense) | 188 TFLOPS | 125 TFLOPS |
| BF16 (dense) | — | 125 TFLOPS |
| INT8 (dense) | 750 TOPS | 250 TOPS |
| Form factor | PCIe (GroqCard) / rack-scale GroqRack | PCIe |
| Announced | 2020-01-01 | 2021-04-12 |
| Released | 2021-06-01 | 2021-04-12 |
Robots running Groq LPU (Language Processing Unit)
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Robots running NVIDIA A10 Tensor Core GPU
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Data centers with Groq LPU (Language Processing Unit)
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Data centers with NVIDIA A10 Tensor Core GPU
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Common questions
Groq LPU (Language Processing Unit) vs NVIDIA A10 Tensor Core GPU: which is faster for training?
Groq LPU (Language Processing Unit) has 1.50x the dense FP16/BF16 throughput of the other (Groq LPU (Language Processing Unit): 188 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.
Groq LPU (Language Processing Unit) vs NVIDIA A10 Tensor Core GPU: which has more memory?
NVIDIA A10 Tensor Core GPU carries more HBM (Groq LPU (Language Processing Unit): 0.23 GB SRAM (230 MB on-die); 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.
Groq LPU (Language Processing Unit) vs NVIDIA A10 Tensor Core GPU: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Groq LPU (Language Processing Unit) 0.50 (188 ÷ 375 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 · Groq LPU (Language Processing Unit) full page · NVIDIA A10 Tensor Core GPU full page.