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AI Choice Engine

GPU comparison

Intel Arc Pro B70 vs Radeon AI PRO R9700

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 September 26, 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.

Intel

Intel Arc Pro B70

32GB VRAM · 608 GB/s

vs
Radeon AI PRO R9700

AMD

Radeon AI PRO R9700

32GB VRAM · 640 GB/s

Amazon UK search links are provided for current availability; no affiliate programme is currently configured, and no live price is claimed here. Affiliate disclosure.

GPU specification, memory, bandwidth, and price comparison
SpecificationIntel Arc Pro B70Radeon AI PRO R9700
VendorIntelAMD
Memory32GB32GB
Usable for a model32GB32GB
Bandwidth608 GB/sWinner: 640 GB/s
Segmentworkstationworkstation
ReleasedMarch 25, 2026Not verifiedUnverified
Launch MSRP~$949~£703~$1,299~£962
Notes32GB ECC GDDR6 (256-bit @ 19Gbps) at $949, announced and priced 25 March 2026; 160–290W board power depending on partner design. The cheapest >24GB discrete card for local inference, undercutting the used 3090/4090 route with a warranty. The trade is software: Intel's inference stack is far less mature than CUDA, so expect tooling friction.AMD's only 32GB discrete card (RDNA 4, 32GB GDDR6 at 640 GB/s) — the natural rival to Intel's Arc Pro B70. $1,299 MSRP, but Puget Systems listed it at ~$1,880 in July 2026 and the eBay median was ~$1,850. ROCm tooling is less mature than CUDA.
UK street priceSearch Amazon (opens in new tab)Search Amazon (opens in new tab)

Local models

What each card can actually run

Computed from published VRAM and the memory each model needs at Q4, including runtime overhead.

Local model fit comparison for both GPUs
ModelIntel Arc Pro B70Radeon AI PRO R9700
Qwen 3 4B4B parametersFitsFits
Qwen 3.8 27B27B parametersFitsFits
Gemma 4 31B31B parametersFitsFits
Llama 3.1 8B Instruct8B parametersFitsFits
Qwen 3 14B14B parametersFitsFits
Gemma 3 27B27B parametersFitsFits
Qwen 3 30B-A3B30B parametersFitsFits
Qwen 3 32B32B parametersFitsFits
Llama 3.3 70B70B parametersDoes not fitDoes not fit
Muse Glimmer 30B30B parametersFitsFits
Mixtral 8x22B141B parametersDoes not fitDoes not fit
Qwen 3 235B-A22B235B parametersDoes not fitDoes not fit
Qwen 3 Coder 480B-A35B480B parametersDoes not fitDoes not fit
DeepSeek V4-Flash284B parametersDoes not fitDoes not fit

Common questions

Intel Arc Pro B70 vs Radeon AI PRO R9700

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

Which costs less at launch MSRP, Intel Arc Pro B70 or Radeon AI PRO R9700?
Intel Arc Pro B70 launched lower at ~$949 against ~$1299 for Radeon AI PRO R9700. Launch MSRP is an anchor, not a live price.
Which is better for high-refresh gaming, Intel Arc Pro B70 or Radeon AI PRO R9700?
Radeon AI PRO R9700 has more memory bandwidth — 640 GB/s against 608 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.
For local inference only — which holds larger models, Intel Arc Pro B70 or Radeon AI PRO R9700?
Neither — both have 32GB usable and hold the same models at Q4. Decide on bandwidth and price instead.
For local inference only — is Intel Arc Pro B70 or Radeon AI PRO R9700 faster at generating tokens?
Radeon AI PRO R9700, at 640 GB/s against 608 GB/s — roughly 1.1× 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?
Neither. A 70B model needs about 48GB at Q4 including overhead, above both cards. The largest that fits is Qwen 3 32B on Intel Arc Pro B70 and Qwen 3 32B on Radeon AI PRO R9700.

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.