Chip comparison
Google TPU v5e vs Google TPU v6 (Trillium)
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 v5e | Google TPU v6 (Trillium) |
|---|---|---|
| TDP | 170 W | 200 W |
| Memory | 16 GB HBM2 | 32 GB HBM3 |
| Memory bandwidth | 819 GB/s | 1,600 GB/s |
| BF16 (dense) | 197 TFLOPS | 918 TFLOPS |
| FP8 (dense) | — | 1,836 TFLOPS |
| INT8 (dense) | 393 TOPS | 1,836 TOPS |
| Form factor | OAM (v5e pod, up to 256 chips) | OAM (Trillium pod, up to 256 chips) |
| Announced | 2023-08-29 | 2024-05-14 |
| Released | 2023-11-01 | 2024-12-01 |
Robots running Google TPU v5e
No robots publicly running it yet.
Robots running Google TPU v6 (Trillium)
No robots publicly running it yet.
Data centers with Google TPU v5e
Data centers with Google TPU v6 (Trillium)
Common questions
Google TPU v5e vs Google TPU v6 (Trillium): which is faster for training?
Google TPU v6 (Trillium) has 9.32x the dense FP16/BF16 throughput of the other (Google TPU v5e: 197 TFLOPS; Google TPU v6 (Trillium): 1,836 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
Google TPU v5e vs Google TPU v6 (Trillium): which has more memory?
Google TPU v6 (Trillium) carries more HBM (Google TPU v5e: 16 GB HBM2; Google TPU v6 (Trillium): 32 GB HBM3). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
Google TPU v5e vs Google TPU v6 (Trillium): which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Google TPU v5e 1.16 (197 ÷ 170 W); Google TPU v6 (Trillium) 9.18 (1,836 ÷ 200 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 v5e full page · Google TPU v6 (Trillium) full page.