Best GPU for Llama
In short
- Entry point: the RTX 3090 at 24 GB — holds 417 of 506 catalogue models at Q4_K_M.
- Most headroom: the RTX A6000 at 48 GB — 460 models, 43 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.
Which GPU should you buy to run Meta's Llama models locally? We rank the options by how large a Llama they run, how fast they generate tokens, and value for money — computed from our parity-tested memory engine, not vibes.
24 GB of used-market value — runs 32B coders and 70B with offload for far less than a 4090.
The shortlist
- #2View card →RTX 409024 GB · ₹1,95,000 · 223 tok/s on Llama 8B
The prosumer flagship: 24 GB, top consumer bandwidth, the best single-card local-AI experience.
- #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 A6000Most VRAM48 GB · ₹4,50,000 · 170 tok/s on Llama 8B
48 GB workstation card — single-GPU fine-tuning and 70B inference without going multi-GPU.
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 24–48 GB (2.0×), bandwidth spans 768–1792 GB/s, and catalogue coverage moves from 417 models to 460. Note the largest card here is not the fastest: the RTX A6000 carries more memory than the RTX 3090 but reads it more slowly (768 against 936 GB/s), so it holds bigger models without running them faster.
| Card | VRAM | Bandwidth | Models it holds | Largest fit | Power |
|---|---|---|---|---|---|
| RTX 3090 | 24 GB | 936 GB/s | 417 of 506 | Seed-OSS 36B Instruct | 84 kWh/mo |
| RTX 4090 | 24 GB | 1008 GB/s | 417 of 506 | Seed-OSS 36B Instruct | 108 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 Llama?
On this shortlist the RTX 3090 is the entry point at 24 GB, holding 417 of 506 catalogue models at Q4_K_M. The RTX A6000 at 48 GB adds 43 more. Which is right depends on the largest model you intend to run and your context length.
Is 24 GB of VRAM enough for Llama?
It runs 417 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 3090 at the bottom of this shortlist, 417 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.