GPU comparison
RTX 6000 Ada vs RTX PRO 6000 Blackwell
Compared on the specs that decide a purchase: memory, bandwidth, power, and which workloads each card actually fits — gaming, creative, or local models when that is the job.
Specifications verified October 2, 2026. Launch MSRP is not today's price — in September 2026 the memory shortage pushed street prices 1.2×–3× above it; the notes row below carries a dated lowest UK/US price where we have one.

NVIDIA
RTX 6000 Ada
48GB VRAM · 960 GB/s

NVIDIA
RTX PRO 6000 Blackwell
96GB VRAM · 1792 GB/s
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| Specification | RTX 6000 Ada | RTX PRO 6000 Blackwell |
|---|---|---|
| Vendor | ||
| Vendor | NVIDIA | NVIDIA |
| Memory | ||
| Memory | 48GB | Winner: 96GB |
| Usable for a model | ||
| Usable for a model | 48GB | Winner: 96GB |
| Bandwidth | ||
| Bandwidth | 960 GB/s | Winner: 1792 GB/s |
| Segment | ||
| Segment | workstation | workstation |
| Released | ||
| Released | Not verifiedUnverified | Not verifiedUnverified |
| Launch MSRP | ||
| Launch MSRP | No launch MSRP published | No launch MSRP published |
| Notes | ||
| Notes | NVIDIA specifies 48GB GDDR6 ECC, 960GB/s bandwidth and a dual-slot full-height board. Announced September 20, 2022, with distribution planned from December; the exact shipment day and a manufacturer launch MSRP are unverified here. The site's fit labels are memory estimates, not runtime or speed measurements. Compare current partner prices and the exact software stack. | Workstation Edition: NVIDIA specifies 96GB GDDR7 ECC, 1792GB/s memory bandwidth and up to 600W. The March 18, 2025 announcement planned distribution from April and OEM systems from May; March 18 is not a verified shipment day. A manufacturer launch MSRP is unverified, so the previous retailer-associated $8,565 value is withheld. Max-Q and Server editions are separate configurations. Memory capacity does not guarantee that every 70B model/runtime/context fits. Verify current purchase or rental terms separately. |
| UK street price | ||
| UK street price | Search Amazon (opens in new tab) | Search Amazon (opens in new tab) |
Local models
Estimated local-model fit
Planning estimates from published VRAM and a weights-plus-overhead heuristic. Context length, KV cache, quantization and runtime can change actual memory needs.
| Model | RTX 6000 Ada (est.) | RTX PRO 6000 Blackwell (est.) |
|---|---|---|
| Qwen 3 4B4B parameters | ||
| Qwen 3 4B4B parameters | Est. fit | Est. fit |
| Qwen 3.8 27B27B parameters | ||
| Qwen 3.8 27B27B parameters | Est. fit | Est. fit |
| Gemma 4 31B30.7B parameters | ||
| Gemma 4 31B30.7B parameters | Est. fit | Est. fit |
| Llama 3.1 8B Instruct8B parameters | ||
| Llama 3.1 8B Instruct8B parameters | Est. fit | Est. fit |
| Qwen 3 14B14.8B parameters | ||
| Qwen 3 14B14.8B parameters | Est. fit | Est. fit |
| Gemma 3 27B27B parameters | ||
| Gemma 3 27B27B parameters | Est. fit | Est. fit |
| Qwen 3 30B-A3B30.5B parameters | ||
| Qwen 3 30B-A3B30.5B parameters | Est. fit | Est. fit |
| Qwen 3 32B32.8B parameters | ||
| Qwen 3 32B32.8B parameters | Est. fit | Est. fit |
| Llama 3.3 70B70B parameters | ||
| Llama 3.3 70B70B parameters | Est. tight | Est. fit |
| Muse Glimmer 30B29.6B parameters | ||
| Muse Glimmer 30B29.6B parameters | Est. fit | Est. fit |
| Mixtral 8x22B141B parameters | ||
| Mixtral 8x22B141B parameters | Est. no fit | Est. tight |
| Qwen 3 235B-A22B235B parameters | ||
| Qwen 3 235B-A22B235B parameters | Est. no fit | Est. no fit |
| Qwen 3 Coder 480B-A35B480B parameters | ||
| Qwen 3 Coder 480B-A35B480B parameters | Est. no fit | Est. no fit |
| DeepSeek V4-Flash284B parameters | ||
| DeepSeek V4-Flash284B parameters | Est. no fit | Est. no fit |
Common questions
RTX 6000 Ada vs RTX PRO 6000 Blackwell
Answered from the verified figures on this page rather than general guidance.
Which is better for high-refresh gaming, RTX 6000 Ada or RTX PRO 6000 Blackwell?
Which has more VRAM for gaming and creative work, RTX 6000 Ada or RTX PRO 6000 Blackwell?
For local inference only — which holds larger models, RTX 6000 Ada or RTX PRO 6000 Blackwell?
For local inference only — is RTX 6000 Ada or RTX PRO 6000 Blackwell faster at generating tokens?
For local inference only — can either run a 70B model?
If one card holds a model the other cannot, that capacity difference usually outweighs a bandwidth advantage. A model that spills to system memory can be substantially slower, but the actual impact depends on the backend, transfer path, context, and workload.