The Nvidia RTX 5090, despite its powerful specifications, has become prohibitively expensive for many AI developers, pushing the Intel Arc Pro B70—traditionally overlooked as a GPU option—into the spotlight as a cost-effective alternative for AI workloads.
- RTX 5090 GPUs exceed $4,000, limiting accessibility for AI builders.
- Intel's Arc Pro B70 offers 32GB GDDR6 memory at about $1,000 per card.
- 4-card Arc Pro B70 setups provide high memory bandwidth and cost less than $3,800.
What happened
The Nvidia RTX 5090 GPU, touted as one of the most powerful consumer-grade GPUs with 32GB of GDDR7 memory, has seen retail prices soar to twice its MSRP, hovering above the $4,000 mark. This price increase has driven cost-sensitive AI developers to seek alternatives that can meet advancing AI model requirements, particularly those demanding larger memory capacities.
Intel's Arc Pro B70, featuring 32GB of GDDR6 memory and priced around $950 to $1,000, has emerged as an unexpectedly competitive option. Although Intel is primarily recognized for CPUs rather than GPUs, the Arc Pro B70 is specifically designed for professional AI workloads rather than gaming, making it a promising choice for local AI setup developers.
Why it matters
The rising cost of Nvidia's RTX 5090 creates a significant barrier for AI practitioners needing large-memory GPUs. Intel’s Arc Pro B70 delivers comparable memory capacity and higher aggregate bandwidth in multi-GPU configurations, at a substantially lower price point. This makes it an economically viable solution, especially for AI models that prioritize memory over raw compute power.
However, Nvidia maintains advantages through its robust software ecosystem. Nvidia’s CUDA platform and optimized AI libraries are widely adopted, while Intel’s software support, including oneAPI and OpenVINO, remains less mature. This ecosystem gap influences adoption despite Arc Pro B70’s attractive hardware-to-cost ratio.
What to watch next
As AI models continue to grow in complexity and memory demands, the demand for high-VRAM GPUs will increase, potentially driving further innovation and competition in this segment. Monitoring Intel’s efforts to improve software compatibility and developer support will be critical to whether it can truly challenge Nvidia’s dominance in AI GPU workloads.
Additionally, further testing and real-world benchmarking of multi-card Arc Pro B70 configurations versus top-tier Nvidia GPUs will clarify their respective strengths and use cases. The balance between compute power, memory bandwidth, software ecosystem, and cost will define the next wave of hardware choices in AI development.