GeForce RTX 3090 vs RTX A6000 for Local AI
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Which GPU is better for local AI workloads?
The A6000's 48GB fits 70B models at Q4 comfortably — the cheapest single-card path to them. Its 768 GB/s bandwidth trails the 3090's 936, so shared models decode slightly slower. The 3090 remains the consumer value pick; the A6000 is the capacity workhorse.
Spec comparison: what actually differs
The table below is computed live from our hardware database. Positive deltas favor the GeForce RTX 3090.
| Specification | GeForce RTX 3090 | RTX A6000 | Difference |
|---|---|---|---|
| VRAM | 24 | 48 | -50% |
| Memory bandwidth | 936 | 768 | +22% |
| Memory type | GDDR6X | GDDR6 | — |
| Memory bus | 384 | 384 | — |
| TDP | 350 | 300 | +17% |
| CUDA cores | 10496 | 10752 | -2% |
| Architecture | Ampere | Ampere | — |
| Launch MSRP | $1,499 | $4,500 | -67% |
| Street price | $1,499 | $4,500 | -67% |
What about price?
Which one should you buy for LLMs and image generation?
RTX A6000
Buy the used A6000 when you need 48GB and multi-GPU stability (blower cooler).
Full specs & benchmarks →Is it faster for LLM inference?
70B Q4 (~40GB) only on the A6000. 32B-class decode: 3090 slightly faster on bandwidth.
How does it handle image generation?
Both handle heavy pipelines; A6000's 48GB enables huge batches.
Which AI models fit on each card?
Computed from our model VRAM database at Q4 quantization: GeForce RTX 3090 has 24 GB, RTX A6000 has 48 GB.
Fit only on the RTX A6000 (48 GB)
Fit on both cards
Frequently asked questions
3090 or A6000 for 70B models?
A6000: 70B at Q4 needs about 40GB — the 3090's 24GB requires offloading. The A6000 is the cheapest single-card 70B route.
Is the A6000 slower than a 3090?
Slightly, on paper: 768 versus 936 GB/s bandwidth. In practice its stability and 48GB capacity matter more for large-model work.