Emerging Hardware Options for High-VRAM Inference

A new hardware option for large-scale AI workloads has surfaced in retail channels. According to reports on Reddit, a dual-chip Intel Arc Pro B60 GPU configuration with 48GB of VRAM has been spotted listed on the Swiss retailer Digitec/Galaxus. The unit is reportedly priced at CHF 2,500 (approximately USD 3,000), though its competitiveness remains a subject of debate among industry observers.

While the price point is high for consumer-grade hardware, some analysts suggest the card may appeal to users seeking high VRAM reserves on a budget. For comparison, a single Blackwell 6000 GPU can cost upwards of $15,000. However, users have noted that utilizing multiple B60 units would require significant infrastructure, as consumer-grade CPUs often lack the necessary PCIe slots to service multiple cards at full 16x speeds.

The Escalating Cost of Enterprise AI Infrastructure

The financial barrier to entry for high-end AI hardware remains substantial. Industry discussions highlight that top-tier enterprise solutions, such as the GB 300, can cost north of $100,000, with larger variants approaching $200,000. These costs are largely driven by the massive VRAM requirements necessary for running large-scale models.

As the industry moves toward more complex architectures, such as Mixture-of-Experts (MoE) models, the demand for memory capacity is increasing. While high-end enterprise hardware provides the necessary scale, the cost of deployment remains a significant hurdle for many organizations.

The Shift Toward Inference-Optimized Hardware

As the AI sector matures, the focus of hardware competition is reportedly shifting from model training to inference—the process of running models in production. While much of the industry’s capital has been directed toward training larger models, the efficiency and speed of token delivery are becoming critical for practical AI agent integration into daily workflows.

Recent performance benchmarks, as cited by users referencing Artificial Analysis, demonstrate significant variance in inference speeds across different hardware and providers:

  • NVIDIA/Groq 3 LPX: Reported 3,400 tokens per second (tok/s) for Gemma 4 31B (100K context).
  • Cerebras Systems Inc: Reported 3,000 tok/s for GPT-OSS 120B, though independent data from Artificial Analysis suggested a lower figure of 1,715 tok/s.
  • Groq: Reported 1,800 tok/s for Llama 3.1 8B and 500 tok/s for GPT-OSS 120B.
  • SambaNova: Reported 476 tok/s for Llama 3.3 70B.

This disparity suggests that specialized chips designed specifically for serving models may become the next primary battleground in the AI hardware race. Efficiency gains in memory packaging, such as SK Hynix’s HMIB 3D packaging, are also expected to play a role in reducing the power consumption and costs associated with these high-speed inference tasks.