Chip comparison
Groq LPU (Language Processing Unit) vs NVIDIA H100 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 H100 Tensor Core GPU |
|---|---|---|
| Process node | GlobalFoundries 14nm | TSMC 4N |
| Transistors (B) | — | 80 |
| Die size | — | 814 mm² |
| TDP | 375 W | 700 W |
| Memory | 0.23 GB SRAM (230 MB on-die) | 80 GB HBM3 |
| Memory bandwidth | 80,000 GB/s | 3,350 GB/s |
| FP16 (dense) | 188 TFLOPS | 989 TFLOPS |
| BF16 (dense) | — | 989 TFLOPS |
| FP8 (dense) | — | 1,979 TFLOPS |
| INT8 (dense) | 750 TOPS | 1,979 TOPS |
| Launch price (list) | — | $30,000 |
| Form factor | PCIe (GroqCard) / rack-scale GroqRack | SXM5 |
| Announced | 2020-01-01 | 2022-03-22 |
| Released | 2021-06-01 | 2022-10-13 |
Robots running Groq LPU (Language Processing Unit)
No robots publicly running it yet.
Robots running NVIDIA H100 Tensor Core GPU
No robots publicly running it yet.
Data centers with Groq LPU (Language Processing Unit)
No data centers publicly running it yet.
Common questions
Groq LPU (Language Processing Unit) vs NVIDIA H100 Tensor Core GPU: which is faster for training?
NVIDIA H100 Tensor Core GPU has 10.53x the dense FP16/BF16 throughput of the other (Groq LPU (Language Processing Unit): 188 TFLOPS; NVIDIA H100 Tensor Core GPU: 1,979 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 H100 Tensor Core GPU: which has more memory?
NVIDIA H100 Tensor Core GPU carries more HBM (Groq LPU (Language Processing Unit): 0.23 GB SRAM (230 MB on-die); NVIDIA H100 Tensor Core GPU: 80 GB HBM3). 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 H100 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 H100 Tensor Core GPU 2.83 (1,979 ÷ 700 W). NVIDIA H100 Tensor Core GPU wins at the die level; system-level efficiency also depends on cooling and interconnect.
Groq LPU (Language Processing Unit) vs NVIDIA H100 Tensor Core GPU: which is more widely used?
Groq LPU (Language Processing Unit): 0 robots and 0 data centers publicly running it. NVIDIA H100 Tensor Core GPU: 0 robots and 7 data centers. Empty here means no operator has said publicly, not zero adoption in the market.
See also: every chip comparison · Groq LPU (Language Processing Unit) full page · NVIDIA H100 Tensor Core GPU full page.