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

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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 - HBM: 192 GB HBM3E, 7.4 TB/s bandwidth Google Cloud - 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 - 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 - HBM4: 432 GB, 19.6 TB/s per GPU AMD - 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 - FP8: from NVL72 system numbers: 3.6 EF FP4 vs 1.2 EF FP8 → per GPU ≈ 16.7 PF FP8 Jon Peddie Research - HBM4: 288 GB, ~20 TB/s bandwidth Tom's Hardware - Power (your assumption, consistent with leaks of 2.3–2.5 kW Rubin Ultra): 2300 W TweakTown

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
• 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

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.