RTX 6000 Ada vs RTX A6000 for Local AI
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Which GPU is better for local AI workloads?
Both are 48GB workstation cards that hold 70B models at Q4. The Ada generation decodes faster (960 vs 768 GB/s) and roughly doubles compute throughput. Used A6000s are the value 48GB play; the 6000 Ada is the current production card.
Spec comparison: what actually differs
The table below is computed live from our hardware database. Positive deltas favor the RTX 6000 Ada.
| Specification | RTX 6000 Ada | RTX A6000 | Difference |
|---|---|---|---|
| VRAM | 48 | 48 | — |
| Memory bandwidth | 960 | 768 | +25% |
| Memory type | GDDR6 | GDDR6 | — |
| Memory bus | 384 | 384 | — |
| TDP | 300 | 300 | — |
| CUDA cores | 18176 | 10752 | +69% |
| Architecture | Ada Lovelace | Ampere | — |
| Launch MSRP | $6,800 | $4,500 | +51% |
| Street price | $6,800 | $4,500 | +51% |
What about price?
Which one should you buy for LLMs and image generation?
RTX A6000
Buy the 6000 Ada for current-gen speed with the same 48GB.
Full specs & benchmarks →Is it faster for LLM inference?
Same model set in 48GB; Ada decodes ~25% faster on bandwidth.
How does it handle image generation?
Ada clearly faster per image; capacity equal.
Which AI models fit on each card?
Computed from our model VRAM database at Q4 quantization: RTX 6000 Ada has 48 GB, RTX A6000 has 48 GB.
Fit on both cards
Frequently asked questions
A6000 or RTX 6000 Ada for 70B models?
Both fit 70B at Q4 in 48GB. The Ada is ~25% faster on bandwidth-bound decode; the used A6000 is the budget route to the same capacity.