If you want 32 GB of VRAM on a single dedicated GPU without Nvidia’s tax, the AMD Radeon AI Pro R9700 and Intel Arc Pro B70 are the two options on our hosting. They are priced similarly and hit similar workloads. The differences are in software stack maturity and raw bandwidth.
Contents
Specs
| Spec | R9700 Pro | Arc Pro B70 |
|---|---|---|
| VRAM | 32 GB GDDR6 | 32 GB |
| Bandwidth | ~640 GB/s | ~560 GB/s |
| Software stack | ROCm 6.x | IPEX-LLM, oneAPI, OpenVINO |
| FP8 tensor | Partial | Yes |
| TDP | ~260 W | ~220 W |
ROCm vs oneAPI
ROCm is the more mature of the two. By 2026 it supports most mainstream PyTorch workloads transparently – vLLM has official ROCm builds, Diffusers works without patches, and Flash Attention has ROCm ports. Intel’s stack is IPEX-LLM for LLM workloads and OpenVINO for production deployment. IPEX-LLM supports most well-known models. What you will not find on either platform is the bleeding-edge GitHub repo that expects CUDA on publication day. Both require small patches for niche tools.
LLM Inference
For LLM serving, the R9700 generally wins on raw throughput per model. vLLM runs on ROCm with good kernels. Llama 3 8B at INT8 hits roughly 55-70 tokens/sec on the R9700 versus 45-55 on the B70. For FP8 models, however, the B70’s more complete FP8 tensor cores claw back some of the gap. See our vLLM on ROCm guide for the setup.
Image Generation
Stable Diffusion XL runs on both. ROCm’s Diffusers path is well-travelled. OpenVINO has official SDXL support. Per image, the R9700 is roughly 20-30% faster. The B70 catches up in pipelines that lean heavily on FP8 quantised UNet variants. For a production SDXL pipeline, ROCm is easier to set up – most tutorials apply with minimal adaptation. See our R9700 vs 5080 SDXL for raw numbers.
32GB Without CUDA, Without the Nvidia Premium
Both cards on fixed UK monthly pricing with full root access on our dedicated servers.
Browse GPU ServersWhich to Choose
Pick the R9700 if you want the lower-friction path for typical PyTorch workflows and your models use BF16 or FP16. Pick the B70 if you are serving FP8 models in production via OpenVINO or IPEX-LLM, power efficiency matters, and you have already invested in the Intel stack. For pure hobbyist LLM inference, the R9700 is easier to get running. For locked-down production deployments of known models, the B70’s OpenVINO runtime is more predictable.
Compare against B70 vs 3090 and R9700 vs 5080 to see how each card holds up against Nvidia equivalents.
