Best GPU for fine-tuning
In short
- Entry point: the RTX 4060 Ti 16GB at 16 GB — holds 374 of 506 catalogue models at Q4_K_M.
- Most headroom: the RTX A6000 at 48 GB — 460 models, 86 more than the entry card.
- Fastest here: the RTX 5090 at 1792 GB/s — bandwidth sets decode speed once a model fits.
- All figures are Q4_K_M at moderate context. Long context shifts them; check yours in the calculator.
Fine-tuning needs far more VRAM than inference. QLoRA on a 13B model wants ~16 GB; bigger runs want 24–48 GB. VRAM headroom dominates these rankings.
48 GB workstation card — single-GPU fine-tuning and 70B inference without going multi-GPU.
The shortlist
- #2View card →RTX 309024 GB · ₹82,000 · 207 tok/s on Llama 8B
24 GB of used-market value — runs 32B coders and 70B with offload for far less than a 4090.
- #3View card →RTX 5090Fastest32 GB · ₹2,45,000 · 397 tok/s on Llama 8B
32 GB and the fastest consumer bandwidth — headroom for 32B at high quant and quick image gen.
- #4View card →RTX 4060 TiBest value16 GB · ₹46,000 · 64 tok/s on Llama 8B
16 GB on a budget: the value pick for 14B models and comfortable image generation.
How we picked
Each card is scored on the VRAM it needs to run the target models, tokens per second computed from our parity-tested engine, value for money at current India street prices, and power draw. We never recommend a card for a model it can't actually run.
Prices are approximate India street prices and move often — confirm live cost with the GPU Cost Calculator and check exact fit for your model on Can I Run It?
More GPU picks
Or use the tools: AI Hardware Advisor · Can I Run It? · GPU Compare · Buy vs Rent
Decision matrix
The same shortlist as above, side by side on the four things that decide it. Model counts are at Q4_K_M; power assumes 8 hours a day at board TDP. Across this shortlist VRAM spans 16–48 GB (3.0×), bandwidth spans 288–1792 GB/s, and catalogue coverage moves from 374 models to 460.
| Card | VRAM | Bandwidth | Models it holds | Largest fit | Power |
|---|---|---|---|---|---|
| RTX 4060 Ti 16GB | 16 GB | 288 GB/s | 374 of 506 | Mistral Small 24B (3.1) Instru | 40 kWh/mo |
| RTX 3090 | 24 GB | 936 GB/s | 417 of 506 | Seed-OSS 36B Instruct | 84 kWh/mo |
| RTX 5090 | 32 GB | 1792 GB/s | 423 of 506 | Nemotron Super 49B Instruct | 138 kWh/mo |
| RTX A6000 | 48 GB | 768 GB/s | 460 of 506 | Qwen 2 VL 72B Instruct | 72 kWh/mo |
Common questions
What GPU should I buy for fine-tuning?
On this shortlist the RTX 4060 Ti 16GB is the entry point at 16 GB, holding 374 of 506 catalogue models at Q4_K_M. The RTX A6000 at 48 GB adds 86 more. Which is right depends on the largest model you intend to run and your context length.
Is 16 GB of VRAM enough for fine-tuning?
It runs 374 of the 506 models in our catalogue at Q4_K_M, so for most mainstream sizes yes. It becomes the limit on larger models and on long context, where KV cache competes for the same budget.
Does a bigger card always run models faster?
No. Capacity and speed are separate. On this shortlist the fastest card is the RTX 5090 at 1792 GB/s, which is not necessarily the one with the most memory. Bandwidth sets decode speed once a model fits; VRAM only decides whether it fits at all.
Check it against your own numbers
These picks assume Q4_K_M at moderate context. Put your real model and context into the GPU Memory Calculator to see the exact fit. On the RTX 4060 Ti 16GB at the bottom of this shortlist, 374 of 506 catalogue models fit at Q4_K_M — whether yours is one of them depends on context length as much as parameter count, which is what KV cache costs if you run long conversations — it is the figure most often left out of a buying decision.