Two 32GB workstation GPUs now sell for less than NVIDIA charges for the same memory, and both run local models today. Intel’s Arc Pro B70 and AMD’s Radeon AI Pro R9700 each give you 32GB for well under a comparable RTX card. Neither is a CUDA card, so the Arc Pro B70 vs Radeon AI Pro R9700 call is less about raw VRAM and more about how far llama.cpp, ROCm, SYCL and Vulkan have come on each. In the wider field of GPUs for local LLMs, these two are the value story on memory. Pick the right backend and both run 20B to 32B models at home.
The R9700 has the higher ceiling and the more mature software. The B70 matches its 32GB for $350 less at launch prices and holds its own once you’ve set up the backend that suits it, though Amazon stock of both 32GB cards has thinned to a few reseller units at similar prices. There is also a cheaper 24GB Intel card and a 48GB dual that change the maths when your limit is VRAM rather than speed. Specs and prices below were read off Intel ARK, AMD’s product pages and the Amazon Creators API in October 2026, with every card selling above its launch price because of the memory crunch.
Short answer: we would buy the R9700 if your stack is ROCm or you want the most throughput per card, and the B70 if you can find one near its $949 launch price.
The picks at a glance
- Best 32GB card at launch price: Intel Arc Pro B70. Same VRAM as the R9700 and $350 under it at MSRP, 367 INT8 TOPS, but Amazon has only reseller units at about $2,000 now.
- Most throughput, smoothest software: AMD Radeon AI Pro R9700. 640 GB/s, 383 INT8 TOPS, ROCm 7.2.1 validated.
- Cheapest card you can buy today: Intel Arc Pro B60, 24GB for around $1,050 from a third-party seller.
- Most VRAM in one slot: Intel Arc Pro B60 Dual, two GPUs for 48GB total.
What follows is the reasoning behind those four lines. How the cards compare on the specs that matter for inference, what the software looks like on each, and which one to buy for the models you run.
How we compared these cards
Every spec here was read off the vendor’s own page on the test date. Intel ARK for the Arc Pro cards, AMD’s product page for the R9700. Prices and stock come from the Amazon Creators API, which reads the live listing, so the numbers match what you pay today. Where a card has no normal stock, we say so and compare launch prices instead. The sourcing log with every URL ships alongside the article.
We did not run our own tokens-per-second benchmarks on these four, for one practical reason: none of them is rentable. Arc Pro and Radeon AI Pro are workstation and desktop cards, and the GPU-rental clouds carry datacenter parts only, so there is no vast.ai instance to spin up the way there is for an H100. Where this guide quotes performance, it comes from a named third-party review or from the llama.cpp project’s own issue tracker, and it says so. No tokens-per-second figure here is presented as our measurement.
What you can verify without the hardware is most of the buying decision anyway. VRAM, memory bandwidth, the AI-engine throughput the vendor quotes, and how complete the local-inference software is on each architecture. If you are still working out which card you even need, start with how much VRAM a model needs at a given quantization, then come back. VRAM decides whether a model loads at all.
Arc Pro B70 vs Radeon AI Pro R9700: the specs
Those two 32GB cards are the real matchup. The R9700 leads on memory bandwidth and raw AI throughput and ships the more complete software. The B70 matches its VRAM and undercut it by $350 at launch. Below them sit the B60 as the budget option and the B60 Dual as the high-VRAM one. Read the B65 as a B60 with more memory, not a cheaper B70: it pairs the B70’s 32GB and 608 GB/s with the B60’s 160 XMX engines and 197 INT8 TOPS. At about $1,550 from a third-party seller it is the cheapest 32GB card here you can buy today, roughly $400 under either 32GB card’s reseller price.
| Spec | Arc Pro B70 | Radeon AI Pro R9700 | Arc Pro B60 | B60 Dual | Arc Pro B65 |
|---|---|---|---|---|---|
| VRAM | 32GB GDDR6 | 32GB GDDR6 | 24GB GDDR6 | 48GB (2×24) | 32GB GDDR6 |
| Bus / bandwidth | 256-bit / 608 GB/s | 256-bit / 640 GB/s | 192-bit / 456 GB/s | 2x 192-bit / 2x 456 | 256-bit / 608 GB/s |
| Architecture | Xe2 Battlemage | RDNA 4 (gfx1201) | Xe2 Battlemage | 2x Xe2 Battlemage | Xe2 Battlemage |
| AI engines | 256 XMX | 128 AI accel | 160 XMX | 2x 160 XMX | 160 XMX |
| INT8 TOPS | 367 | 383 (766 sparse) | 197 | 2x 197 | 197 |
| FP32 | 22.9 TFLOPS | 47.8 TFLOPS | 12.3 TFLOPS | 2x 12.3 | 12.3 TFLOPS |
| TBP | 230W (160-290) | 300W | 200W (120-200) | ~240-300W/GPU | 200W |
| Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | PCIe 5.0 x8 | x8/x8 bifurcation | PCIe 5.0 x16 |
| Price now | approx $2,000 (resellers) | approx $1,950 (resellers) | approx $1,050 | approx $1,950 (resellers) | approx $1,550 |
Two rows matter most for inference. Bandwidth sets how fast tokens come out once a model loads, and the R9700’s 640 GB/s is the highest here. INT8 TOPS tracks how fast prompts get processed, and the R9700 again leads on paper, with the B70 close behind and well clear of every 20-core card. The FP32 gap is wide, but it matters less for the quantized inference you will actually run.
Intel Arc Pro B70: the 32GB value pick at launch price
Start with the B70 if you want 32GB and can find one near launch price. It carries the full Battlemage BMG-G31 die with 32 Xe-cores and 256 XMX engines. That is why its 367 INT8 TOPS nearly doubles anything in the 20-core Arc Pro range. At a $949 launch price it undercut the R9700 by $350. By late October, though, the only B70s left on Amazon were a few reseller units at about $2,000, level with the R9700, so that saving now depends on finding one near launch price.
On software you get two paths. Vulkan is the portable one and tends to win on prompt processing. SYCL through Intel’s oneAPI needs the Level-Zero stack installed, but it pulls ahead on token generation. The llama.cpp project supports both on Battlemage, and the community numbers on the B60 that shares this software show real throughput on Qwen3-class models once the backend is set up. vLLM exists for these cards through Intel’s “LLM Scaler” fork, but it is still beta-grade, so treat llama.cpp as the dependable route for now.
Buy it if you find it near launch price and you’ll install oneAPI or lean on Vulkan. Skip it if your tooling assumes CUDA or ROCm and you don’t want to port anything, because the Intel inference path is viable but less trodden than AMD’s. It is a single GPU on a PCIe 5.0 x16 slot, so it drops into any board with no bifurcation to worry about. The ASRock listing we linked has sold out, and what Amazon has left is reseller stock from about $1,970, so price it at Newegg or B&H before you buy.
AMD Radeon AI Pro R9700: the throughput pick
The R9700 is the stronger card, and the one with the software AMD actually maintains for inference. It is RDNA 4 (Navi 48, gfx1201) with 64 compute units, 128 AI accelerators, a 64MB Infinity Cache, and the highest memory bandwidth here at 640 GB/s. Its 383 INT8 TOPS, or 766 with structured sparsity, give it the top AI throughput of the group. The 47.8 TFLOPS of FP32 is more than double the B70’s for the workloads that use it.
The reason to pay the premium is ROCm. AMD ships ROCm-validated prebuilt llama.cpp binaries for the gfx120X family that includes this card, at ROCm 7.2.1, so you can run a model without compiling your own stack. Vulkan works too, and the community finds it can match or beat ROCm on short text generation, while ROCm pulls ahead on long context and large prompts. Two supported paths instead of one is the practical difference between the AMD and Intel sides right now. If you already run llama.cpp, Ollama or vLLM, the ROCm path is the least work.
Buy it if you want the most capable single card and a stack maintained for AI, or if your tooling is already ROCm. Skip it if the $350 launch-price premium over the B70 buys throughput you won’t use, which is the case when your models already fit comfortably in 32GB at your target speed. It pulls 300W and wants a 750W supply, so size the power accordingly. The ASRock listing we linked has sold out, and the reseller units left on Amazon start from about $1,950, so price it at Newegg or B&H before you buy.
Intel Arc Pro B60: the cheap entry
When 24GB is enough, the B60 is the cheapest pro card that runs the same Intel software as the B70. It is the cut-down Battlemage with 20 Xe-cores, 160 XMX engines, and 456 GB/s of bandwidth, on a 200W board you can configure down to 120W. At around $1,050 it is roughly half the reseller price of the 32GB cards, and 24GB still loads a 20B model at a 4-bit quant with room for context.
(paid link)Memory is the trade-off. 24GB is fine for models up to about 20B parameters quantized, but it is exactly the limit that pushes people to a 32GB card six months later when they want something bigger. It also runs at PCIe 5.0 x8 rather than x16. That costs nothing for a single card, and it is the design detail that makes the dual version possible. Buy it to get started cheaply, or to pair with inference you already know fits in 24GB. Skip it if you expect to grow into 30B-plus models. The ASRock Creator we first linked sold out, so the link goes to GUNNIR’s 200W blower version of the same GPU, sold by a third-party seller at around $1,050, about 1.6 times what the ASRock cost. Check the live price.
Intel Arc Pro B60 Dual: the 48GB option
The B60 Dual puts two full B60 GPUs on one dual-slot board for 48GB of total VRAM, the most memory per slot in this comparison. One catch is worth understanding before you buy. The operating system sees it as two separate 24GB GPUs, not one 48GB device, and the VRAM does not pool on its own. Your inference stack has to split the model across both GPUs. llama.cpp and vLLM both do that with tensor parallelism, but it is a configuration step, not a free upgrade.
It also needs a motherboard slot that supports x8/x8 bifurcation, because that is how the two GPUs share one physical x16 connector. Check two things before ordering:
- Your board’s manual lists x8/x8 bifurcation on the target slot.
- The live price against the roughly $1,200 MSRP, because Amazon stock is third-party only.
On Amazon the Dual sells through marked-up resellers right now, close to $1,950, so price the Maxsun and Sparkle versions on Newegg before you commit. Buy it if you need to run a model that spills past 32GB and you’re comfortable configuring multi-GPU splitting. Skip it if a single 32GB card covers your models, because one card is simpler, even though the Dual now costs less per gigabyte than either 32GB card at reseller prices.
What actually decides a local-LLM GPU here
VRAM comes first, because it is binary. A model either fits in memory or it does not load, and no amount of bandwidth fixes a card that is too small. Work out the model size and quantization you want, add headroom for the context window and the KV cache, and that number sets your floor:
- 24GB: models up to around 20B at 4-bit.
- 32GB: into the low 30Bs, with context.
- 48GB: above that, or two models at once.
The quantization format you pick moves that ceiling more than any spec on the card. Bandwidth is second, and it sets token speed once the model fits. All else equal, the R9700’s 640 GB/s puts out tokens faster than the 456 GB/s B60. INT8 TOPS tracks prompt-processing speed, which you feel most on long inputs and RAG. Read both as relative rankings here, not absolute promises, because the real figure depends on the model, the quant and the backend.
Software is the deciding factor between the two brands, and AMD is ahead for inference today. ROCm 7.2 is validated for the R9700 with prebuilt llama.cpp binaries, and Vulkan is there as a second supported path. Intel’s cards run well on Vulkan and on SYCL through oneAPI, but the setup is more hands-on and the vLLM story is still beta. Confirm the stack is live before you trust it. Each side has a one-line check:
# AMD: list the ROCm-visible GPU rocminfo | grep -i "Marketing Name" # Intel: list the oneAPI/Level-Zero devices sycl-ls # Either card: confirm the Vulkan device is present vulkaninfo | grep -i "deviceName"
Last come the physical details that quietly rule a card in or out. The R9700 draws 300W and wants a 750W supply. The Arc Pro cards sit at 200W to 230W and are easier on a small build. Single cards drop into any x16 slot, but the B60 Dual needs x8/x8 bifurcation support, so check the board. Both 32GB cards support ECC, which matters if the box does anything besides inference. Building the whole machine around one of these? The local AI workstation build guide covers the CPU, RAM and PSU side. If you would rather skip a discrete card, unified-memory boxes trade bandwidth for capacity, as the DGX Spark and Strix Halo comparison lays out.
Which card to buy for your models
Match the card to the model you actually run. For a 30B-class model at a 4-bit quant with real context, the Radeon AI Pro R9700 is the pick. 32GB, the most bandwidth, the most AI throughput, and the only one here with a vendor-maintained ROCm inference path. It sells from about $1,950 through resellers right now, and it earns that if you use the ceiling.
The Arc Pro B70 gives you the same 32GB for $350 less, but only if you can find one near its $949 launch price and you are happy on Vulkan or willing to set up oneAPI. The last Amazon units sold at R9700 money, and at that price the R9700 is the better card. If 24GB is all your models need, the Arc Pro B60 is the cheapest card here you can actually buy today, at about half the reseller price of either 32GB card, and it runs the same software. If you want 32GB for less than the reseller price of either 32GB card, the B65 at about $1,550 from a third-party seller is the cheapest 32GB card in stock, as long as you treat it as a B60 with more memory and not a cheaper B70: it loads bigger models, but with the B60’s 197 INT8 TOPS it processes prompts no faster than a B60. And if one card cannot hold your model, the B60 Dual’s 48GB keeps you in a single slot, provided your board does bifurcation and you will configure the split. Prices move weekly in this market, so confirm the live number before you buy.
