Chip comparison
Google TPU v5e vs Google TPU v5p
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 v5p |
|---|---|---|
| TDP | 170 W | 300 W |
| Memory | 16 GB HBM2 | 95 GB HBM2e |
| Memory bandwidth | 819 GB/s | 2,765 GB/s |
| BF16 (dense) | 197 TFLOPS | 459 TFLOPS |
| INT8 (dense) | 393 TOPS | 918 TOPS |
| Form factor | OAM (v5e pod, up to 256 chips) | OAM (per-chip in v5p pod) |
| Announced | 2023-08-29 | 2023-12-06 |
| Released | 2023-11-01 | 2024-01-01 |
Robots running Google TPU v5e
No robots publicly running it yet.
Robots running Google TPU v5p
No robots publicly running it yet.
Data centers with Google TPU v5e
Data centers with Google TPU v5p
Common questions
Google TPU v5e vs Google TPU v5p: which is faster for training?
Google TPU v5p has 2.33x the dense FP16/BF16 throughput of the other (Google TPU v5e: 197 TFLOPS; Google TPU v5p: 459 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 v5p: which has more memory?
Google TPU v5p carries more HBM (Google TPU v5e: 16 GB HBM2; Google TPU v5p: 95 GB HBM2e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
Google TPU v5e vs Google TPU v5p: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Google TPU v5e 1.16 (197 ÷ 170 W); Google TPU v5p 1.53 (459 ÷ 300 W). Google TPU v5p 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 v5p full page.