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NVIDIA GPUs for Reinforcement Learning
I’ve been working on a quick reference to compare the various NVIDIA GPUs and their professional equivalents for my own builds. This list will grow as I try things. Enjoy!
🔍 Consumer vs. Professional GPUs
Consumer GPU | CUDA Cores | VRAM | Memory Type | Pro Equivalent |
---|---|---|---|---|
RTX 3090 | 10,496 | 24 GB GDDR6X | GDDR6X | RTX A6000 (Ampere) |
RTX 4090 | 16,384 | 24 GB GDDR6X | GDDR6X | RTX 6000 Ada |
RTX 5090 | 21,760 | 32 GB GDDR7 | GDDR7 | RTX 6000 Blackwell |
🧠 Professional Card Specs (Detailed)
Pro GPU | Architecture | CUDA Cores | VRAM | Memory Type | Bandwidth (GB/s) | FP64 Support | Power (TDP) | MSRP |
---|---|---|---|---|---|---|---|---|
RTX A6000 | Ampere | 10,752 | 48 GB ECC | GDDR6 | 768 | Yes (1/64) | 300 W | $4,650 |
RTX 6000 Ada | Ada Lovelace | 18,176 | 48 GB ECC | GDDR6 | 960 | Yes (1/64) | 300 W | $6,800 |
RTX 6000 Blackwell | Ada Next? | ~24,576* | 64 GB ECC* | GDDR7* | ~1,536* | Yes (1/64?) | ~350 W* | ~$7,500* |