Chip comparison
Google TPU v6 (Trillium) vs NVIDIA GB300 (Blackwell Ultra)
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 | Google TPU v6 (Trillium) | NVIDIA GB300 (Blackwell Ultra) |
|---|---|---|
| Process node | — | TSMC 4NP |
| TDP | 200 W | 1400 W |
| Memory | 32 GB HBM3 | 288 GB HBM3e |
| Memory bandwidth | 1,600 GB/s | 8,000 GB/s |
| BF16 (dense) | 918 TFLOPS | — |
| FP8 (dense) | 1,836 TFLOPS | 5,000 TFLOPS |
| INT8 (dense) | 1,836 TOPS | — |
| Form factor | OAM (Trillium pod, up to 256 chips) | Superchip (Blackwell Ultra + Grace) |
| Announced | 2024-05-14 | 2025-03-18 |
| Released | 2024-12-01 | 2025-11-01 |
Robots running Google TPU v6 (Trillium)
No robots publicly running it yet.
Robots running NVIDIA GB300 (Blackwell Ultra)
No robots publicly running it yet.
Data centers with Google TPU v6 (Trillium)
Data centers with NVIDIA GB300 (Blackwell Ultra)
Common questions
Google TPU v6 (Trillium) vs NVIDIA GB300 (Blackwell Ultra): which is faster for training?
NVIDIA GB300 (Blackwell Ultra) has 2.72x the dense FP8 throughput of the other (Google TPU v6 (Trillium): 1,836 TFLOPS; NVIDIA GB300 (Blackwell Ultra): 5,000 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
Google TPU v6 (Trillium) vs NVIDIA GB300 (Blackwell Ultra): which has more memory?
NVIDIA GB300 (Blackwell Ultra) carries more HBM (Google TPU v6 (Trillium): 32 GB HBM3; NVIDIA GB300 (Blackwell Ultra): 288 GB HBM3e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
Google TPU v6 (Trillium) vs NVIDIA GB300 (Blackwell Ultra): which is more power-efficient?
Dense FP8 TFLOPS per watt: Google TPU v6 (Trillium) 9.18 (1,836 ÷ 200 W); NVIDIA GB300 (Blackwell Ultra) 3.57 (5,000 ÷ 1400 W). Google TPU v6 (Trillium) wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · Google TPU v6 (Trillium) full page · NVIDIA GB300 (Blackwell Ultra) full page.