For years the RTX 3090 owned the “cheap card with lots of VRAM” slot on our dedicated hosting. Intel’s Arc Pro B70 now challenges it with 32 GB at a price in the same ballpark. That 8 GB gap changes what you can host. Does Intel’s software ecosystem hold up enough to make it worth it?
What We Cover
Specs
| Spec | Arc Pro B70 | RTX 3090 |
|---|---|---|
| VRAM | 32 GB | 24 GB GDDR6X |
| Bandwidth | ~560 GB/s | ~936 GB/s |
| Software | IPEX-LLM, OpenVINO | Full CUDA ecosystem |
| FP8 | Yes | No |
| TDP | ~220 W | 350 W |
What 32 GB Unlocks
The 8 GB difference sounds modest until you map it to models. A 24 GB card hosts Qwen 2.5 32B at INT4 only with very tight KV cache. At 32 GB, the same model runs with comfortable context headroom. A 20B FP16 model that barely fits on the 3090 runs with real batching room on the B70. For a full VRAM walkthrough see our Qwen 32B VRAM page.
Speed
The 3090 wins on raw memory bandwidth. Where both cards fit a model – say, Llama 3 8B at INT8 – the 3090 decodes roughly 30-50% faster. The B70’s FP8 support mitigates some of this for models with FP8 checkpoints, because FP8 weights travel less over the memory bus. If your pipeline uses FP8, the gap narrows. If it uses INT8 or FP16 only, the 3090 stays ahead on speed.
Software
This is the B70’s real challenge. Every production LLM serving stack assumes CUDA. vLLM, TGI, SGLang, TensorRT-LLM – all CUDA-first. Intel’s path is IPEX-LLM for Python workloads and OpenVINO for deployment. Both are production-ready but you lose fast-moving community libraries. If your team has CUDA muscle memory and reads the latest GitHub on a daily basis, the 3090 saves time. If you are comfortable with a narrower but stable stack (llama.cpp with Vulkan, OpenVINO, IPEX-LLM), the B70 works.
Host a 32B Model on One Card
B70 and 3090 both available on our UK hosting with fixed monthly pricing and full root.
Browse GPU ServersThe Decision
Pick the 3090 if speed per token matters most and your models fit in 24 GB. Pick the B70 if you want to host 20-32B models on a single card without multi-GPU complexity and your serving stack is one of the supported ones. For training or fine-tuning, the 3090 is still the safer choice because bf16 mixed-precision training is better supported. For pure inference of stable models, both are legitimate.
Compare against B70 vs RTX 5080 for the other Intel comparison that matters, and R9700 vs B70 for the 32 GB non-CUDA battle.
