Feasibility Analysis on Laptop

Feasibility Analysis on Laptop | Generated by AI

Home 2026.07

Now I have a complete picture. Here’s my full analysis:

🚀 DeepSpec — Feasibility Analysis on This Laptop

📋 Laptop Specs

Component	Status
CPU	Intel i9-13900KF, 32 cores ✅
RAM	62 GiB ✅ (46 GiB available)
GPU	1× RTX 4070 (11.6 GB VRAM) ⚠️
Disk	916 GB NVMe — only 14 GB free ❌
PyTorch	2.10.0+cu128, CUDA 12.8 ✅

🧩 Pipeline Stage Breakdown

1️⃣ Data Download (`download_and_split.py`)

Feasible: ✅ Just downloads open-perfectblend (~few hundred MB JSONL)
Needs internet, small disk space — fine.

2️⃣ Regenerate Answers (`generate_train_data.py`)

Partially feasible: ⚠️
Needs SGLang or compatible inference engine (not installed)
Serving Qwen3-4B on a single RTX 4070 is possible (≈8 GB VRAM for bf16 model) but very slow (script expects 8 GPUs + 32 concurrency)
Disk space is extremely tight for the regenerated JSONL output

3️⃣ Target Cache (`prepare_target_cache.py`)

Not feasible: ❌ NEEDS ~38 TB DISK (per the warning in README.md)
Stores per-token hidden states for the full training set — completely impossible here.

4️⃣ Training (`train.py`)

Not feasible: ❌
- Code spawns nprocs=torch.cuda.device_count() = 1 process (single GPU support)
- sharding_strategy="no_shard" — each process loads the full target model
- Qwen3-4B bf16 ≈ 8 GB + draft model ≈ 2-3 GB + optimizer states ≈ 16 GB + activations with seq_len=4096 → likely OOM on 11.6 GB
- global_batch_size=512 with local_batch_size=1 on 1 GPU = 512 gradient accumulation steps per update → painfully slow
- No disk space for model weights download (~8 GB), checkpoints, or logs

5️⃣ Evaluation (`eval.py`)

Potentially feasible ⚠️ if we free disk space
- Needs Qwen3-4B target model (~8 GB download) + pre-trained draft checkpoint
- Single-GPU mode would work, but VRAM is tight (11.6 GB total)
- Default script wants 4 GPUs — would need CUDA_VISIBLE_DEVICES=0
- Prerequisite: Free up at least ~20-25 GB disk space

🚧 Critical Bottlenecks

#	Issue	Severity
1	Disk full — only 14 GB free, Qwen3-4B alone is ~8 GB	🔴 Showstopper
2	Single GPU (11.6 GB VRAM) — code designed for multi-GPU with `no_shard`, OOM risk	🔴 High
3	38 TB target cache — required for training, physically impossible	🔴 Unavoidable
4	Dependencies — need SGLang for data regeneration (separate install)	🟡 Medium

✅ What Could Work (if we free disk space)

Option A: Quick Evaluation with Pre-trained Checkpoints

Free up ~25+ GB on disk (e.g., clear ~/.cache and large old projects in /mnt/data/)
Download Qwen/Qwen3-4B from HF
Download a pre-trained draft checkpoint (e.g., deepseek-ai/dspark_qwen3_4b_block7)
Run eval.py single-GPU on one benchmark (e.g., GSM8K)
Risk: May OOM at 11.6 GB VRAM depending on sequence length

Option B: Training at Minuscule Scale (not really useful)

Would need to completely rewrite the data pipeline to avoid the 38 TB cache
Build on-the-fly target computation (extremely slow, defeats the purpose)
Not practical for the intended purpose

🧹 Before We Try: Free Up Space?

The biggest issue is disk. There’s 374 GB in /mnt/data/zz-backup/ and 295 GB in /mnt/data/nanoGPT/ and 55 GB in ~/.cache that could be cleaned up. Want me to try freeing up space and then attempt an evaluation run with a pre-trained checkpoint?

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