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AI Choice Engine

GPU comparison

GeForce RTX 5090 vs RTX 6000 Ada

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.

GeForce RTX 5090

NVIDIA

GeForce RTX 5090

32GB VRAM · 1792 GB/s

vs
RTX 6000 Ada

NVIDIA

RTX 6000 Ada

48GB VRAM · 960 GB/s

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GPU specification, memory, bandwidth, and price comparison
SpecificationGeForce RTX 5090RTX 6000 Ada
VendorNVIDIANVIDIA
Memory32GBWinner: 48GB
Usable for a model32GBWinner: 48GB
BandwidthWinner: 1792 GB/s960 GB/s
Segmentconsumerworkstation
ReleasedJanuary 30, 2025Not verifiedUnverified
Launch MSRP~$1,999~£1,481No launch MSRP published
Notes32GB GDDR7; NVIDIA specifies 1792GB/s theoretical memory bandwidth. Launch starting price $1,999 and announced availability January 30, 2025. These figures establish neither current stock nor current purchase price. Local-model fit is a planning estimate; model files, context, cache, backend and driver support determine actual memory use and speed.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.
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
ModelGeForce RTX 5090 (est.)RTX 6000 Ada (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. no fitEst. tight
Muse Glimmer 30B29.6B parametersEst. fitEst. fit
Mixtral 8x22B141B parametersEst. no fitEst. no fit
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

GeForce RTX 5090 vs RTX 6000 Ada

Answered from the verified figures on this page rather than general guidance.

Which is better for high-refresh gaming, GeForce RTX 5090 or RTX 6000 Ada?
GeForce RTX 5090 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, GeForce RTX 5090 or RTX 6000 Ada?
RTX 6000 Ada — 48GB usable against 32GB on GeForce RTX 5090. Extra memory helps texture mods, video timelines, and 3D scenes before it helps esports frame rates.
For local inference only — which holds larger models, GeForce RTX 5090 or RTX 6000 Ada?
RTX 6000 Ada. With 48GB usable against 32GB it additionally holds Llama 3.3 70B at Q4.
For local inference only — is GeForce RTX 5090 or RTX 6000 Ada faster at generating tokens?
GeForce RTX 5090, 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?
RTX 6000 Ada can, at Q4 — a 70B model needs about 48GB including runtime overhead. GeForce RTX 5090 does not have the memory. Expect little headroom for long context.

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.