Editing Openai/6925f847-0600-800d-8a2e-57670d2a3feb


{{DISPLAYTITLE:TPU v7 vs MI450X vs Rubin}}

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== gpt-5-1-thinking ==
=== User: Compare tpu 7 ironwood to mi450x and Rubin in fp8, fp4 power consumption, memory bandwidth, memory capacity ===
Compare tpu 7 ironwood to mi450x and Rubin in fp8, fp4 power consumption, memory bandwidth, memory capacity 

Assume tpu has 1000W power and mi450x has 1400w and Rubin has 2300w 

Make a 10GW comparison table

=== Assistant: Here’s a clean way to look at it: first per-accelerator, then a 10 GW build-out. ===
Here’s a clean way to look at it: first per-accelerator, then a 10 GW build-out.

==== 1. Per-accelerator assumptions (TPU v7 Ironwood vs MI450X vs Rubin) ====

Specs I’m using (from public info + one explicit assumption):
* TPU v7 Ironwood - FP8: 4.6 PFLOP/s per chip The Register<ref>{{cite web|title=The Register|url=https://www.theregister.com/2025/11/06/googles_ironwood_tpus_ai/|publisher=The Register|date=2025-11-06|access-date=2025-11-29}}</ref> - HBM: 192 GB HBM3E, 7.4 TB/s bandwidth Google Cloud<ref>{{cite web|title=Google Cloud|url=https://cloud.google.com/blog/products/compute/inside-the-ironwood-tpu-codesigned-ai-stack|publisher=Google Cloud|access-date=2025-11-29}}</ref> - Power: you told me to assume 1000 W (this lines up with 9,216 chips ≈ 10 MW in Google material → ~1 kW/chip). XPU.pub<ref>{{cite web|title=XPU.pub|url=https://xpu.pub/2025/04/16/google-ironwood/|publisher=xpu.pub|date=2025-04-16|access-date=2025-11-29}}</ref> - FP4: not published. I’ll assume 2× FP8 throughput ≈ 9.2 PFLOP/s (same pattern as other tensor cores). This is a model assumption, not a spec.
* AMD Instinct MI450X - From AMD Helios rack numbers: 72 MI450 → 1.4 EF FP8, 2.9 EF FP4 per rack, so per GPU: - FP8 ≈ 19.4 PFLOP/s, FP4 ≈ 40.3 PFLOP/s AMD<ref>{{cite web|title=AMD|url=https://www.amd.com/en/blogs/2025/amd-helios-ai-rack-built-on-metas-2025-ocp-design.html|publisher=amd.com|access-date=2025-11-29}}</ref> - HBM4: 432 GB, 19.6 TB/s per GPU AMD<ref>{{cite web|title=AMD|url=https://www.amd.com/en/blogs/2025/amd-helios-ai-rack-built-on-metas-2025-ocp-design.html|publisher=amd.com|access-date=2025-11-29}}</ref> - Power (your assumption): 1400 W
* NVIDIA Vera Rubin (VR200 / Rubin GPU) - FP4: 50 PFLOP/s per GPU (2 reticle-limited dies, 50 PF FP4) Glenn K. Lockwood<ref>{{cite web|title=Glenn K. Lockwood|url=https://www.glennklockwood.com/garden/processors/R200|publisher=Glenn K. Lockwood|access-date=2025-11-29}}</ref> - FP8: from NVL72 system numbers: 3.6 EF FP4 vs 1.2 EF FP8 → per GPU ≈ 16.7 PF FP8 Jon Peddie Research<ref>{{cite web|title=Jon Peddie Research|url=https://www.jonpeddie.com/news/nvidia-prepares-vera-rubin-for-next-ai-wave/|publisher=Jon Peddie Research|access-date=2025-11-29}}</ref> - HBM4: 288 GB, ~20 TB/s bandwidth Tom's Hardware<ref>{{cite web|title=Tom's Hardware|url=https://www.tomshardware.com/pc-components/gpus/nvidias-vera-rubin-platform-in-depth-inside-nvidias-most-complex-ai-and-hpc-platform-to-date|publisher=Tom's Hardware|access-date=2025-11-29}}</ref> - Power (your assumption, consistent with leaks of 2.3–2.5 kW Rubin Ultra): 2300 W TweakTown<ref>{{cite web|title=TweakTown|url=https://www.tweaktown.com/news/108068/nvidia-could-change-cooling-solution-for-rubin-ultra-ai-gpus-for-huge-2300w-thermal-concerns/index.html|publisher=tweaktown.com|access-date=2025-11-29}}</ref>

===== Per-chip comparison =====

(All FLOPs are dense, no sparsity; TF/W is “teraFLOPs per watt.”)

| Accelerator     | Power (W) | FP8 (PF) | FP8 (TF/W) | FP4 (PF) | FP4 (TF/W) | HBM (GB) | HBM BW (TB/s) |
| --------------- | --------- | -------- | ---------- | -------- | ---------- | -------- | ------------- |
| TPU v7 Ironwood | 1000      | 4.6      | 4.6        | 9.2*     | 9.2        | 192      | 7.4           |
| AMD MI450X      | 1400      | 19.4     | 13.9       | 40.3     | 28.8       | 432      | 19.6          |
| NVIDIA Rubin    | 2300      | 16.7     | 7.25       | 50       | 21.7       | 288      | ~20           |

*Ironwood FP4 is an assumption (2× FP8) because Google hasn’t published FP4 peak.

Quick takeaways per device:
* Perf/W: MI450X is the clear winner in FP8 and FP4 TF/W under your power assumptions.
* Raw FP4: Rubin wins on absolute FP4 per chip (50 PF vs ~40 PF MI450X).
* Memory capacity: MI450X has 1.5× Rubin’s HBM and 2.25× Ironwood’s (432 GB vs 288 GB vs 192 GB). GamingBench<ref>{{cite web|title=GamingBench|url=https://gamingbench.net/amd-ceo-confirms-instinct-mi450-is-built-on-2nm-process-technology/|publisher=gamingbench.net|access-date=2025-11-29}}</ref>
* Memory bandwidth: MI450X is also top on per-chip HBM BW at 19.6 TB/s, slightly above Rubin and far above Ironwood.

==== 2. 10 GW build-out (accelerator power only) ====

Now apply your power assumptions to a 10 GW accelerator budget (ignoring CPUs, networking, cooling overhead, etc.).
* Total power budget: 10,000 MW = 10,000,000,000 W

Number of accelerators you can power:
* Ironwood: 10,000,000,000 W / 1,000 W ≈ 10.0 M chips
* MI450X: 10,000,000,000 W / 1,400 W ≈ 7.14 M GPUs
* Rubin: 10,000,000,000 W / 2,300 W ≈ 4.35 M GPUs

(Values rounded to three sig figs below.)

===== 10 GW cluster comparison =====

Units:
* Total HBM = EB (exabytes, 1 EB = 10⁹ GB)
* Total BW = PB/s
* Compute = EFLOP/s (1 EF = 10³ PF)

| Metric @ 10 GW accel power | TPU v7 Ironwood | AMD MI450X | NVIDIA Rubin |
| -------------------------- | --------------- | ---------- | ------------ |
| Accelerators (count)       | 1.00×10⁷        | 7.14×10⁶   | 4.35×10⁶     |
| Total HBM (EB)             | 1.92 EB         | 3.09 EB    | 1.25 EB      |
| Total HBM BW (PB/s)        | 7.4×10⁴         | 1.4×10⁵    | 8.7×10⁴      |
| Total FP8 (EF)             | 4.6×10⁴         | 1.39×10⁵   | 7.25×10⁴     |
| Total FP4 (EF)             | 9.2×10⁴*        | 2.88×10⁵   | 2.17×10⁵     |

*Again, Ironwood FP4 is an inferred 2× FP8, so its FP4 figures are less solid than MI450X/Rubin, which are tied to specific system or chip disclosures. The Next Platform<ref>{{cite web|title=The Next Platform|url=https://www.jonpeddie.com/news/nvidia-prepares-vera-rubin-for-next-ai-wave/|publisher=The Next Platform|access-date=2025-11-29}}</ref>

==== 3. How they stack up in a 10 GW “AI factory” ====

If you hold total watts fixed:
* Throughput side: - MI450X gives ~3× the FP8 and ~3× the FP4 of Ironwood at the same 10 GW, and about 2× the FP8 and ~1.3× the FP4 of Rubin.
* Memory side: - MI450X cluster has >2.5× the HBM of a Rubin cluster at the same power, and ~1.6× Ironwood’s memory. - HBM bandwidth at 10 GW is also highest for MI450X (~140,000 PB/s).

Intuition:
* Ironwood wins on tight Google/Pathways integration and inference focus, but on raw flops & HBM per watt it’s behind the next-gen HBM4 GPUs.
* Rubin is the “max FP4” monster with extreme power draw; at equal facility power it gives a bit less FP8 and much less HBM than MI450X, but more FP4 than MI450X if you normalized per device rather than per watt.
* MI450X is the “dense memory + efficiency” option: at a fixed 10 GW budget you get the most HBM, the most bandwidth, and the most FP8/FP4 among the three, assuming these roadmap numbers hold.

If you want, I can redo the 10 GW table under different power assumptions (e.g., Rubin at 2.5 kW, MI450X at 1.5 kW, Ironwood air-cooled at 800 W) or switch the metric to tokens/s per watt for some target model size.

== References ==
<references />


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