Overkill GPU for GPT-2 Training

Overkill GPU for GPT-2 Training | Generated by AI

Home 2026.06

Here’s the status of 129.212.187.36:

Machine Status Summary
======================

Hostname:  snapshots-gpu-mi300x1-192gb-devcloud-atl1
OS:        Ubuntu 24.04.4 LTS
CPU:       Intel Xeon Platinum 8568Y+ (20 cores, 1 socket)
RAM:       235 GB (230 GB available)
Disk:      697 GB (668 GB free)
Python:    3.12.3
PyTorch:   NOT installed

GPU:       AMD Instinct MI300X VF
  GFX:     gfx942
  VRAM:    192 GB (300 MB used, essentially idle)
  Power:   139W / 750W cap
  Temp:    38°C
  Status:  Low-power state (idle)

Can we train GPT-2 there? Absolutely yes — it’s overkill for GPT-2.

192 GB HBM3 on the MI300X is massive. GPT-2 (124M params) fits in ~1 GB of VRAM. You could train GPT-2 XL (1.5B) without breaking a sweat.
The main blocker: PyTorch is not installed. We’d need to install PyTorch with ROCm backend (pip install torch --index-url https://download.pytorch.org/whl/rocm6.2 or similar).
The GPU is in low-power state (idle) — it’ll wake up on first compute.

Want me to set it up? I’d:

Install PyTorch with ROCm support
Clone nanoGPT (Karpathy’s clean GPT-2 training repo)
Download the dataset (Shakespeare or OpenWebText)
Kick off a training run

Or if you want GPT-2 XL / a bigger model, the 192 GB MI300X can handle it easily. What scale are you thinking?

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