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Chip comparison

Google TPU v5p vs NVIDIA L40S

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.

FieldGoogle TPU v5pNVIDIA L40S
Process nodeTSMC 4N
Transistors (B)76.3
TDP300 W350 W
Memory95 GB HBM2e48 GB GDDR6
Memory bandwidth2,765 GB/s864 GB/s
FP16 (dense)362 TFLOPS
BF16 (dense)459 TFLOPS362 TFLOPS
FP8 (dense)733 TFLOPS
INT8 (dense)918 TOPS733 TOPS
Form factorOAM (per-chip in v5p pod)PCIe
Announced2023-12-062023-08-08
Released2024-01-01

Robots running Google TPU v5p

No robots publicly running it yet.

Robots running NVIDIA L40S

No robots publicly running it yet.

Data centers with Google TPU v5p

Data centers with NVIDIA L40S

No data centers publicly running it yet.

Common questions

Google TPU v5p vs NVIDIA L40S: which is faster for training?

NVIDIA L40S has 1.60x the dense FP16/BF16 throughput of the other (Google TPU v5p: 459 TFLOPS; NVIDIA L40S: 733 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.

Google TPU v5p vs NVIDIA L40S: which has more memory?

Google TPU v5p carries more HBM (Google TPU v5p: 95 GB HBM2e; NVIDIA L40S: 48 GB GDDR6). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

Google TPU v5p vs NVIDIA L40S: which is more power-efficient?

Dense FP16/BF16 TFLOPS per watt: Google TPU v5p 1.53 (459 ÷ 300 W); NVIDIA L40S 2.09 (733 ÷ 350 W). NVIDIA L40S wins at the die level; system-level efficiency also depends on cooling and interconnect.

Google TPU v5p vs NVIDIA L40S: which is more widely used?

Google TPU v5p: 0 robots and 1 data center publicly running it. NVIDIA L40S: 0 robots and 0 data centers. Empty here means no operator has said publicly, not zero adoption in the market.

See also: every chip comparison · Google TPU v5p full page · NVIDIA L40S full page.