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AI Choice Engine

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.

RTX 6000 Ada

NVIDIA

RTX 6000 Ada

48GB VRAM · 960 GB/s

vs
RTX PRO 6000 Blackwell

NVIDIA

RTX PRO 6000 Blackwell

96GB VRAM · 1792 GB/s

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GPU specification, memory, bandwidth, and price comparison
SpecificationRTX 6000 AdaRTX PRO 6000 Blackwell
VendorNVIDIANVIDIA
Memory48GBWinner: 96GB
Usable for a model48GBWinner: 96GB
Bandwidth960 GB/sWinner: 1792 GB/s
Segmentworkstationworkstation
ReleasedNot verifiedUnverifiedNot verifiedUnverified
Launch MSRPNo launch MSRP publishedNo launch MSRP published
NotesNVIDIA 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 priceSearch 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.

Local model fit comparison for both GPUs
ModelRTX 6000 Ada (est.)RTX PRO 6000 Blackwell (est.)
Qwen 3 4B4B parametersEst. fitEst. fit
Qwen 3.8 27B27B parametersEst. fitEst. fit
Gemma 4 31B30.7B parametersEst. fitEst. fit
Llama 3.1 8B Instruct8B parametersEst. fitEst. fit
Qwen 3 14B14.8B parametersEst. fitEst. fit
Gemma 3 27B27B parametersEst. fitEst. fit
Qwen 3 30B-A3B30.5B parametersEst. fitEst. fit
Qwen 3 32B32.8B parametersEst. fitEst. fit
Llama 3.3 70B70B parametersEst. tightEst. fit
Muse Glimmer 30B29.6B parametersEst. fitEst. fit
Mixtral 8x22B141B parametersEst. no fitEst. tight
Qwen 3 235B-A22B235B parametersEst. no fitEst. no fit
Qwen 3 Coder 480B-A35B480B parametersEst. no fitEst. no fit
DeepSeek V4-Flash284B parametersEst. no fitEst. 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?
RTX PRO 6000 Blackwell has more memory bandwidth — 1792 GB/s against 960 GB/s. That usually helps high-refresh 1440p and 4K once VRAM is sufficient for your settings. Still verify with dated independent tests for the games you play.
Which has more VRAM for gaming and creative work, RTX 6000 Ada or RTX PRO 6000 Blackwell?
RTX PRO 6000 Blackwell — 96GB usable against 48GB on RTX 6000 Ada. Extra memory helps texture mods, video timelines, and 3D scenes before it helps esports frame rates.
For local inference only — which holds larger models, RTX 6000 Ada or RTX PRO 6000 Blackwell?
RTX PRO 6000 Blackwell. With 96GB usable against 48GB it additionally holds Mixtral 8x22B at Q4.
For local inference only — is RTX 6000 Ada or RTX PRO 6000 Blackwell faster at generating tokens?
RTX PRO 6000 Blackwell, at 1792 GB/s against 960 GB/s — roughly 1.9× the memory bandwidth. Bandwidth is the practical ceiling on token throughput once a model fits, but it only matters if the model fits in the first place.
For local inference only — can either run a 70B model?
Both can, at Q4 — a 70B model needs about 48GB including runtime overhead, and both have the memory for it. Expect little headroom for long context on either.

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.