Chips · accelerators
NVIDIA’s B200 does 5.3 times the Tesla V100 SXM2’s compute per watt
12 of 18 accelerators shipped by the date first promised. Each figure is quoted from the vendor’s own datasheet, product page or release.
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16-bit compute per watt, by the year each chip shipped
dense BF16 or FP16 at the vendor’s maximum power; 12 of 26 chips have no published powerEvery chip, every figure
dense compute for one chip; memory is high-bandwidth memory; each chip’s page gives the words each figure is quoted from| Chip | Shipped | 16-bit TFLOPS | 8-bit TFLOPS | FP4 TFLOPS | Memory | Bandwidth | Power | GFLOPS/W | Runs at |
|---|---|---|---|---|---|---|---|---|---|
| AMD | |||||||||
| Instinct MI250X | 2021-11 | 383 | 383 INT8 TOPS | — | 128 GB | 3.2 TB/s | 560 W | 684 | 7 clusters |
| Instinct MI300A | 2023-12 | 981 | 1,960 | — | 128 GB | 5.3 TB/s | 760 W | 1,290 | 4 clusters |
| Instinct MI300X | 2023-12 | 1,300 | 2,610 | — | 192 GB | 5.3 TB/s | 750 W | 1,733 | 3 clusters |
| Instinct MI325X | none on record | 1,300 | 2,610 | — | 256 GB | 6 TB/s | 1,000 W | 1,300 | none tracked |
| Instinct MI355X | 2025-09 | 2,500 | 5,000 | 10,100 | 288 GB | 8 TB/s | 1,400 W | 1,786 | none tracked |
| AWS | |||||||||
| Trainium2 | 2024-12 | 667 | 1,299 | — | 96 GiB | 2.9 TB/s | — | — | 5 data centers |
| Trainium3 | 2025-12 | 671 | 2,517 | 2,517 | 144 GiB | 4.9 TB/s | — | — | 2 data centers |
| TPU v4 | 2022-10 | 275 | 275 INT8 TOPS | — | 32 GiB | 1.2 TB/s | — | — | 2 data centers, 4 clusters |
| TPU v5e | 2023-11 | 197 | 393 INT8 TOPS | — | 16 GB | 0.86 TB/s | — | — | 10 data centers |
| TPU v5p | 2024-04 | 459 | 459 | — | 95 GiB | 2.77 TB/s | — | — | 10 data centers, 1 cluster |
| TPU v6e | 2024-12 | 918 | 918 | — | 32 GB | 1.64 TB/s | — | — | 14 data centers |
| TPU7x (Ironwood) | 2026-03 | 2,307 | 4,614 | — | 192 GiB | 7.38 TB/s | — | — | 10 data centers |
| Huawei | |||||||||
| Ascend 910C | 2025-03 | — | — | — | — | — | — | — | 3 data centers |
| Ascend 950 | 2026-09 | — | 1,000 | 2,000 | — | — | — | — | 1 data center |
| Intel | |||||||||
| Gaudi 2 | 2022-06 | 432 | 865 | — | 96 GB | 2.4 TB/s | 600 W | 720 | 1 cluster |
| Gaudi 3 | 2024-09 | 1,678 | 1,678 | — | 128 GB | 3.7 TB/s | 900 W | 1,864 | none tracked |
| Microsoft | |||||||||
| Maia 100 | none on record | — | — | — | 64 GB | 1.8 TB/s | 700 W | — | 1 cluster |
| NVIDIA | |||||||||
| Tesla V100 SXM2 | 2017-08 | 125 FP16 | — | — | 32 GB | 0.9 TB/s | 300 W | 417 | 46 clusters |
| A100 SXM 80GB | none on record | 312 | 624 INT8 TOPS | — | 80 GB | 2.04 TB/s | 400 W | 780 | 3 data centers, 89 clusters |
| H100 SXM | 2022-09 | 990 | 1,979 | — | 80 GB | 3.35 TB/s | 700 W | 1,414 | 14 data centers, 71 clusters |
| GH200 Grace Hopper Superchip | 2023-05 | 990 | 1,979 | — | 96 GB | 4 TB/s | 1,000 W | 990 | 10 clusters |
| H200 SXM | 2024-08 | 990 | 1,979 | — | 141 GB | 4.8 TB/s | 700 W | 1,414 | 3 data centers, 6 clusters |
| B200 | 2025-02 | 2,200 | 4,500 | 9,000 | 180 GB | 7.7 TB/s | 1,000 W | 2,200 | 19 data centers |
| GB200 Grace Blackwell Superchip | 2025-05 | 5,000 | 10,000 | 20,000 | 372 GB | 16 TB/s | — | — | 1 cluster |
| B300 (Blackwell Ultra) | 2025-08 | 2,200 | 4,500 | 14,000 | 270 GB | 7.7 TB/s | 1,100 W | 2,000 | 30 data centers |
| Rubin GPU | 2026-08 | 4,000 | 17,500 | 35,000 | 288 GB | 19.3 TB/s | — | — | 11 data centers |
12 of 18 accelerators shipped by the date first promised
the longest slip was the Google TPU v4, up to 10 months; a shipment is often the day the vendor said it was already shipping, so a slip is an upper boundNVIDIA’s B300 (Blackwell Ultra) runs at 30 of the 95 largest AI data centers
a site with two chip types counts for both; its H100-equivalents are the whole site’sGoogle’s TPU v6e gives the most compute per dollar at list price
dense 16-bit compute for an hour, over the vendor’s own on-demand price per chip-hour| Chip | Price a chip-hour | TFLOP-hours a dollar | Price |
|---|---|---|---|
| Google TPU v6e | $2.70 | 340 | on-demand, us-east1 (first US region listed), per chip-hour |
| Google TPU7x (Ironwood) | $12.00 | 192 | on-demand, us-central1, per chip-hour |
| Google TPU v5e | $1.20 | 164 | on-demand, us-central1, per chip-hour |
| Google TPU v5p | $4.20 | 109 | on-demand, us-east5 (first US region listed), per chip-hour |
| Google TPU v4 | $3.22 | 85 | on-demand, us-central2 (Oklahoma), TPU v4 pod, per chip-hour |
Epoch AI names these chips, which have no entry here
every other chip name Epoch AI gives maps to one chip above- H20 1 data center. NVIDIA publishes no datasheet for the H20, its chip for China, on any page this site could read.
- AMD Instinct MI210 2 clusters. A PCIe card of AMD’s MI200 family. The record holds the family’s MI250X module.
- Google TPU v3 2 clusters. A TPU from before 2021. The record starts with TPU v4.
- NVIDIA Tesla K20X 2 clusters. An NVIDIA chip from before 2017. The record starts with the V100, NVIDIA’s first data-center chip with tensor cores.
- NVIDIA Tesla K40c 2 clusters. An NVIDIA chip from before 2017. The record starts with the V100, NVIDIA’s first data-center chip with tensor cores.
- Cerebras CS-1 1 cluster. A whole system built on one wafer-scale chip, not a chip in the sense of this record.
- Cerebras CS-2 1 cluster. A whole system built on one wafer-scale chip, not a chip in the sense of this record.
- Google TPU v1 1 cluster. A TPU from before 2021. The record starts with TPU v4.
- Google TPU v2 1 cluster. A TPU from before 2021. The record starts with TPU v4.
- GroqChip LPU v1 1 cluster. Not in the record yet: Groq sells cloud access and systems, and the record so far holds chips sold or rented by the largest vendors.
- Huawei Ascend 910B 1 cluster. Not in the record yet. The record holds Huawei’s Ascend 910C and 950.
- Intel Data Center GPU Max 1550 1 cluster. Intel’s GPU for supercomputers. The record follows Intel’s Gaudi AI accelerators.
- NVIDIA A40 PCIe 1 cluster. A graphics and inference card, not one of the NVIDIA training chips this record follows.
- NVIDIA L40 1 cluster. A graphics and inference card, not one of the NVIDIA training chips this record follows.
- NVIDIA P100 1 cluster. An NVIDIA chip from before 2017. The record starts with the V100, NVIDIA’s first data-center chip with tensor cores.
- NVIDIA Quadro RTX 5000 1 cluster. A workstation graphics card, not a data-center AI chip.
- NVIDIA Tesla K40m 1 cluster. An NVIDIA chip from before 2017. The record starts with the V100, NVIDIA’s first data-center chip with tensor cores.
- NVIDIA Tesla K80 1 cluster. An NVIDIA chip from before 2017. The record starts with the V100, NVIDIA’s first data-center chip with tensor cores.
- NVIDIA Tesla P100 PCIe 16GB 1 cluster. An NVIDIA chip from before 2017. The record starts with the V100, NVIDIA’s first data-center chip with tensor cores.
- NVIDIA Tesla P100 SXM2 1 cluster. An NVIDIA chip from before 2017. The record starts with the V100, NVIDIA’s first data-center chip with tensor cores.
- PEZY-SC2 1 cluster. A many-core processor in Japanese supercomputers, outside the AI accelerators this record follows.
Chips left out, and why
a figure goes in only with a quote this site could read on the vendor’s own page- NVIDIA H20 NVIDIA has no product page or datasheet for it on its own site (nvidia.com/en-us/data-center/h20/ returns 404); only vGPU docs name it.
- Each vendor’s own datasheets, product pages, documentation and releases. Every figure links to its page from the chip’s own page. Each page was read and each quote checked on 23 Oct 2026.
- NVIDIA's Tensor Core figures marked as sparse are with sparsity, which doubles the dense figure:
2. Specification in sparse. Dense is one-half sparse spec shown.
NVIDIA GB200 NVL72 - Where each chip runs: Epoch AI, AI data centers (95 sites, retrieved 22 Oct 2026), and Epoch AI, GPU clusters (453 clusters after those Epoch marks as superseded; Epoch stopped the list in July 2025). Both CC BY 4.0.