Hardware and Software Feasibility Check | Generated by AI
Here’s the full assessment after checking the machine:
Hardware check: H200 ✅ — compute capability 9.0, supports FA3 + FP8
Software: torch 2.4.1 (needs upgrade to 2.9.1 for nanochat), no flash attention, no venv
Data: No ClimbMix dataset downloaded (nanochat needs it), no tokenizer trained
Disk: 16G free — too tight. Need to free space first
Feasibility: Yes, we can do the 1.5B training. Here's what it takes:
| Step | Action | Space impact |
|------|--------------------------------------------------------------|-----------------------------|
| 1 | Delete FineWeb-Edu raw parquets (96G) + tokenized .npy (67G) | Frees 163G, 179G total free |
| 2 | Set up nanochat venv (uv + torch 2.9.1+cu128 + flash-attn) | ~10G (cache) |
| 3 | Download ~170 ClimbMix shards for ~29B tokens | ~60G |
| 4 | Train BPE tokenizer on ClimbMix data | ~1G |
| 5 | 1.5B pre-training (~180 hours) | Checkpoints ~6-12G |
Remaining disk: 179G - 10G - 60G - 12G = ~97G free — comfortable
Before we proceed, let me ask about data strategy. The FineWeb-Edu data already on disk was tokenized for a different pipeline (GPT-2 BPE + .npy format). nanochat expects raw parquet text + its own BPE tokenizer. So we have two options: