Best GPU for Ollama
In short
- Entry point: the RTX 3060 12GB at 12 GB — holds 359 of 506 catalogue models at Q4_K_M.
- Most headroom: the RTX 3090 at 24 GB — 417 models, 58 more than the entry card.
- Fastest here: the RTX 3090 at 936 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.
Ollama makes running local models one command. The right GPU decides how big a model you can pull and how fast it replies. Here are the best picks at each price.
The cheapest way into local AI — 12 GB handles 7-13B models and SD1.5 / SDXL.
The shortlist
- #2View card →RTX 4060 Ti16 GB · ₹46,000 · 36 tok/s on Qwen2.5 14B
16 GB on a budget: the value pick for 14B models and comfortable image generation.
- #3View card →RTX 3090Most VRAM24 GB · ₹82,000 · 118 tok/s on Qwen2.5 14B
24 GB of used-market value — runs 32B coders and 70B with offload for far less than a 4090.
- #4View card →RTX 4070 Ti SUPER16 GB · ₹78,000 · 85 tok/s on Qwen2.5 14B
Fast 16 GB card — high bandwidth means noticeably quicker tokens than the 4060 Ti.
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 12–24 GB (2.0×), bandwidth spans 288–936 GB/s, and catalogue coverage moves from 359 models to 417.
| Card | VRAM | Bandwidth | Models it holds | Largest fit | Power |
|---|---|---|---|---|---|
| RTX 3060 12GB | 12 GB | 360 GB/s | 359 of 506 | DeepSeek Coder V2 Lite 16B (2. | 41 kWh/mo |
| RTX 4060 Ti 16GB | 16 GB | 288 GB/s | 374 of 506 | Mistral Small 24B (3.1) Instru | 40 kWh/mo |
| RTX 4070 Ti SUPER | 16 GB | 672 GB/s | 374 of 506 | Mistral Small 24B (3.1) Instru | 68 kWh/mo |
| RTX 3090 | 24 GB | 936 GB/s | 417 of 506 | Seed-OSS 36B Instruct | 84 kWh/mo |
Common questions
What GPU should I buy for Ollama?
On this shortlist the RTX 3060 12GB is the entry point at 12 GB, holding 359 of 506 catalogue models at Q4_K_M. The RTX 3090 at 24 GB adds 58 more. Which is right depends on the largest model you intend to run and your context length.
Is 12 GB of VRAM enough for Ollama?
It runs 359 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 3090 at 936 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 3060 12GB at the bottom of this shortlist, 359 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.