Cross-posted from Best GPU for LLM — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.
The RTX 4090 is the best consumer GPU for LLM fine-tuning in 2026. Its 24GB VRAM handles QLoRA on models up to 34B and full LoRA on 7B-13B. For anything larger, you need multi-GPU setups or cloud.
See the recommended pick on the original guide
Who this is for
You want to fine-tune an open-source LLM on your own data — customer support responses, domain-specific documents, coding style, or creative writing. You need to know which GPU handles your training workload without running out of memory.
VRAM requirements by method
| Method | 7B Model | 13B Model | 34B Model | 70B Model |
|---|---|---|---|---|
| Full fine-tuning | ~30GB | ~55GB | ~140GB | ~280GB |
| LoRA (r=16) | ~18GB | ~32GB | ~72GB | ~150GB |
| QLoRA (4-bit) | ~8GB | ~14GB | ~24GB | ~48GB |
QLoRA is the game-changer for consumer GPUs. By quantizing the base model to 4-bit and training only the adapter layers, you reduce VRAM by 60-75% with minimal quality loss.
VRAM chart available at the original article
Best GPUs for fine-tuning
| GPU | VRAM | Best Method | Max Model Size | Price |
|---|---|---|---|---|
| RTX 5090 | 32GB | QLoRA 70B / LoRA 13B | 70B QLoRA | ~$2,000 |
| RTX 4090 | 24GB | QLoRA 34B / LoRA 7B | 34B QLoRA | ~$1,600 |
| RTX 3090 (used) | 24GB | QLoRA 34B / LoRA 7B | 34B QLoRA | ~$800 |
| RTX 4060 Ti 16GB | 16GB | QLoRA 13B | 13B QLoRA | ~$400 |
| RTX 3060 12GB | 12GB | QLoRA 7B | 7B QLoRA | ~$250 |
See the recommended pick on the original guide
The used RTX 3090 at $800 is exceptional value for fine-tuning — same 24GB as the 4090 at half the price. Training is less bandwidth-sensitive than inference, so the older architecture barely matters. See our VRAM planning guide for more detail.
Which GPU should you buy?
- Fine-tuning 7B models (QLoRA)? → RTX 4060 Ti 16GB ($400). Handles it with room to spare.
- Fine-tuning 13B-34B (QLoRA)? → RTX 4090 ($1,600) or used RTX 3090 ($800). 24GB is the sweet spot.
- Fine-tuning 70B? → RTX 5090 ($2,000) for QLoRA. Full LoRA on 70B requires multi-GPU.
- Just experimenting? → Whatever GPU you already have. QLoRA on 7B works on 8GB cards.
Common mistakes to avoid
- Attempting full fine-tuning on consumer GPUs. Full fine-tuning a 7B model needs ~30GB. Use QLoRA or LoRA instead — quality is nearly identical for most use cases.
- Buying by TFLOPS instead of VRAM. Training needs VRAM first, compute second. A 24GB RTX 3090 beats a 16GB RTX 5080 for fine-tuning.
- Forgetting gradient checkpointing. Enabling gradient checkpointing in your training config reduces VRAM by 30-50% at the cost of ~20% slower training.
- Training without validation data. This isn't a GPU mistake, but overfitting on your dataset is the #1 reason fine-tunes fail. Always split your data.
Final verdict
| Need | Best pick | Price |
|---|---|---|
| Best overall | RTX 4090 | ~$1,600 |
| Best value | RTX 3090 (used) | ~$800 |
| Best budget | RTX 4060 Ti 16GB | ~$400 |
See the recommended pick on the original guide
See the recommended pick on the original guide
QLoRA changed the game for consumer GPU fine-tuning. A $400 card can fine-tune 13B models that would have required $10,000 hardware two years ago.
Related guides on Best GPU for LLM
- Best Budget GPU for Local LLM 2026: RTX 3060 to $350
- Best GPU for 7B Parameter Models in 2026 (Ranked)
- Best GPU for Continue.dev (Local AI Coding) in 2026
The full version lives on Best GPU for LLM — VRAM calculator, GPU comparison table, and live Amazon pricing.
