24GB GPU: Your Sweet Spot | Generated by AI
Question: Friend has a 96GB NVIDIA Blackwell RTX Pro 6000. Should you invest in a 24GB GPU for personal use?
Answer:
Your friend has the RTX Pro 6000 Blackwell — that’s a $8,000+ workstation card, 96GB GDDR7, designed for professional/enterprise workloads. Not your target.
For you, 24GB is the sweet spot right now. Here’s why:
What 24GB unlocks:
| Model | VRAM needed | 24GB fits? |
|---|---|---|
| Llama 3.1 70B (Q4) | ~40GB | ❌ need offload |
| Llama 3.1 8B (full BF16) | ~16GB | ✅ |
| Mistral 7B fine-tune (LoRA) | ~18GB | ✅ |
| GPT-2 760M training | ~4GB | ✅ trivially |
| DeepSeek 7B full fine-tune | ~22GB | ✅ tight |
| Stable Diffusion XL | ~10GB | ✅ |
You already have RTX 4070 (12GB) on your workstation. The jump to 24GB doubles your headroom — you can do real LoRA fine-tuning of 7B/8B models without gradient checkpointing tricks.
What to buy:
- RTX 4090 (24GB GDDR6X) — ~$1,800 USD / ~13,000 CNY used. Best perf/$ for ML in 2025. CUDA ecosystem, everything just works.
- RTX 3090 (24GB GDDR6) — ~$600-800 USD used. Slower but same VRAM, great budget pick.
- RTX 4080 Super (16GB) — skip, 16GB is too close to your current 12GB.
My take for your situation:
You have AMD MI300X access via dev cloud ($2/hr) for large training runs. The 24GB local GPU is for fast iteration — edit → test → debug loops that don’t justify spinning up cloud. RTX 3090 used is the pragmatic move: cheap, 24GB, proven CUDA support, community tutorials everywhere. Save the CNY delta for AMD cloud time.
The 96GB Blackwell your friend has is overkill unless you’re running 70B+ models locally or doing massive batch training. You have MI300X for that.
If budget allows: RTX 4090 > RTX 3090 for longevity. If tight: RTX 3090 used is fine for another 2-3 years of 7B-scale work.