Jake Tech Lab

Intel Gaudi 3 vs Google TPU v7 Ironwood

Google TPU v7 Ironwood comes out ahead of Intel Gaudi 3 on 6 of the 6 things that decide this, going by what each maker publishes. The one that matters for you may be one the leader loses.

Intel Gaudi 3
Intel Gaudi 3
6.2
Fair
0 of 6 rounds
vs
Google TPU v7 IronwoodWinner
Google TPU v7 Ironwood
7.4
Good
6 of 6 rounds

The verdict

Google TPU v7 Ironwood takes it, 6–0 on rounds.

On the published figures, Google TPU v7 Ironwood takes 6 of 6 rounds. Treat that as a starting point and check the current figures on both pages, because prices and plans move.

Choose Intel Gaudi 3 if…

  • • You have a reason to pick Intel Gaudi 3 that is not on this list

Choose Google TPU v7 Ironwood if…

  • • You want Google TPU v7 Ironwood's edge on how much memory it has and how fast its memory is

Round by round

  1. Round 1How much memory it hasGoogle TPU v7 Ironwood wins
    Intel Gaudi 35/10

    128 GB HBM2E

    hbm capacity

    • 128 GB HBM2E, against 192 GB HBM

    Source: cdrdv2-public.intel.com ↗

    Google TPU v7 Ironwood8/10

    192 GB HBM

    hbm capacity per chip

    Source: docs.cloud.google.com ↗

    Google TPU v7 Ironwood wins this round on what the company itself publishes.

  2. Round 2How fast its memory isGoogle TPU v7 Ironwood wins
    Intel Gaudi 35/10

    3.7 TB/s

    hbm bandwidth

    • 3.7 TB/s, against 7.37 TB/s

    Source: cdrdv2-public.intel.com ↗

    Google TPU v7 Ironwood8/10

    7.37 TB/s

    hbm bandwidth per chip

    Source: blog.google ↗

    Google TPU v7 Ironwood wins this round on what the company itself publishes.

  3. Round 3How fast it runs FP8 AI mathGoogle TPU v7 Ironwood wins
    Intel Gaudi 35/10

    1,678 TFLOPS

    fp8 mme (matrix) tflops

    • 1,678 TFLOPS, against 4,614 TFLOPs

    Source: cdrdv2-public.intel.com ↗

    Google TPU v7 Ironwood8/10

    4,614 TFLOPs

    peak fp8 compute per chip

    Source: blog.google ↗

    Google TPU v7 Ironwood wins this round on what the company itself publishes.

  4. Round 4How fast it runs FP16 AI mathGoogle TPU v7 Ironwood wins
    Intel Gaudi 35/10

    459 TFLOPS

    FP16 (signed) matrix (MME) peak, Gaudi 3 technical paper Table 5; 1,678 is the BF16/FP8 figure

    • 1,678 TFLOPS, against 2,307 TFLOPs

    Source: cdrdv2-public.intel.com ↗

    Google TPU v7 Ironwood8/10

    2,307 TFLOPs

    BF16 peak per chip (Google publishes no FP16 figure)

    • peak bf16 compute per chip

    Source: docs.cloud.google.com ↗

    Google TPU v7 Ironwood wins this round on what the company itself publishes.

  5. Round 5Which memory generation it usesGoogle TPU v7 Ironwood wins
    Intel Gaudi 35/10

    HBM2E

    memory type

    • HBM2E, against HBM3E

    Source: cdrdv2-public.intel.com ↗

    Google TPU v7 Ironwood8/10

    HBM3E

    hbm type

    Source: cloud.google.com ↗

    Google TPU v7 Ironwood wins this round on what the company itself publishes.

  6. Round 6How new it isGoogle TPU v7 Ironwood wins
    Intel Gaudi 35/10

    September 24, 2024

    launch press release date

    • September 24, 2024, against Apr 09, 2025

    Source: intel.com ↗

    Google TPU v7 Ironwood8/10

    Apr 09, 2025

    announcement date

    Source: blog.google ↗

    Google TPU v7 Ironwood wins this round on what the company itself publishes.

Intel Gaudi 3Intel Gaudi 3
Google TPU v7 IronwoodGoogle TPU v7 Ironwood

Side by side

Overall score
6.2
7.4
Best
Price
See price
See price
How fast its memory is
6.2
7.3
How much memory it has
6.2
7.3
How much power it draws
5.8
—
How fast it runs FP16 AI math
7.4
7.3
How new it is
7.3
7.8
Head-to-head findings
How fast its memory is
3.7 TB/s
7.37 TB/s
How much memory it has
128 GB HBM2E
192 GB HBM
How much power it draws
900W
—
How fast it runs FP16 AI math
459 TFLOPS
2,307 TFLOPs
How new it is
September 24, 2024
Apr 09, 2025
Which memory generation it uses
HBM2E
HBM3E
How fast it runs FP8 AI math
1,678 TFLOPS
4,614 TFLOPs
How it handles scientific FP64 math
—
—
Record
Head-to-head record
2–3
3–1
Head-to-heads won
40 %
75 %
Rounds won
38 %
75 %
Pros
  • bf16 mme (matrix) tflops
  • peak bf16 compute per chip
Cons
  • 128 GB HBM2E, against 192 GB HBM
  • 3.7 TB/s, against 7.37 TB/s
  • 1,678 TFLOPS, against 4,614 TFLOPs
  • 1,678 TFLOPS, against 2,307 TFLOPs
  • 192 GB HBM, against 288 GB HBM3E
  • 7.37 TB/s, against 8 TB/s
  • 4,614 TFLOPs, against 5 PFLOPs
  • 2,307 TFLOPs, against 2.5 PFLOPs
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