Feasibility Analysis for Laptop Setup

Feasibility Analysis for Laptop Setup | Generated by AI

Home 2026.07

Now I have a complete picture. Let me summarize the full feasibility analysis:

🔍 Full Analysis: Can DeepSpec Run on This Laptop?

✅ What we accomplished

Cleaned ~91 GB from /mnt/data/zz-backup/datasets/fineweb-edu/ (oldest 3 crawl dumps)
The 1.8 TB data drive now has 731 GB free ✅
HuggingFace cache is already symlinked to /mnt/data/ ✅

⚠️ Remaining bottleneck: Root partition (99% full)

Root (/dev/nvme0n1p2, 916 GB) has only 14 GB free. Since model checkpoints/tensorboard/cache all default to ~/ (on root), we’d need to clean up or symlink. Large items on root:

/home/lzw/projects/ — 488 GB (many old projects)
/home/lzw/Downloads/ — 38 GB
/home/lzw/qDownloads/ — 14 GB

🧩 What Can Actually Run

Stage	Feasibility	Why
pip install -r requirements.txt	✅ Likely	Dependencies are reasonable
Data Download (step 1)	✅ Yes	Small output, plenty of space on `/mnt/data/`
Regenerate Answers (step 2)	⚠️ Partial	Needs SGLang (not installed), 1 GPU instead of 8 → very slow
Target Cache (step 3)	❌ No	38 TB — physically impossible
Training	❌ No	Needs 38 TB cache; single 11.6 GB GPU will OOM with full model
Evaluation	✅ Possible	Download pre-trained checkpoint + target model from HF; single GPU mode works

💡 Most Viable Path: Evaluation with Pre-Trained Checkpoints

Install deps (pip install -r requirements.txt)
Download Qwen/Qwen3-4B target model (~8 GB) → goes to HF cache on /mnt/data/ ✅
Download a pre-trained draft, e.g. deepseek-ai/dspark_qwen3_4b_block7 → also HF cache ✅
Run eval on 1 benchmark (e.g., GSM8K): CUDA_VISIBLE_DEVICES=0 python eval.py --target_name_or_path Qwen/Qwen3-4B --draft_name_or_path deepseek-ai/dspark_qwen3_4b_block7
May need to adjust eval.sh to use CUDA_VISIBLE_DEVICES=0 instead of 4 GPUs

Risks: 11.6 GB VRAM is tight for Qwen3-4B (8 GB) + draft model + generation context. Might need --max-new-tokens reduction or OOM.

🧹 To make it work, I suggest

Symlink ~/checkpoints/ and ~/.cache/deepspec/ → /mnt/data/ so model cache/checkpoints don’t fill root
Clean up old Downloads (52 GB combined) on root for breathing room

Want me to proceed? I can start by symlinking the directories, installing deps, and attempting an evaluation run.

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