Comparative Performance Analysis in Large Language Model Workloads

Recent user-reported benchmarks have highlighted a significant divergence in the cost-to-performance ratio between high-end NVIDIA hardware and Intel’s professional GPU offerings for Large Language Model (LLM) tasks. According to data shared on Reddit, an Intel Arc Pro B70 with 32GB of VRAM demonstrates a competitive value proposition when compared to the NVIDIA RTX 5090, particularly in scenarios where memory capacity is prioritized over raw processing speed.

Benchmark Results for Qwen3.8-27B

In a series of unverified tests conducted on a Qwen3.8-27B model (Q6 quantization), the Intel Arc Pro B70—retailing for approximately $1,299 via Amazon.com Inc—was compared against the NVIDIA RTX 5090, which carries a reported market price exceeding $6,500 on Newegg Commerce Inc.

The performance metrics, as reported by a user on Reddit, indicate that the Intel card achieves roughly half the generation speed of the RTX 5090. Specifically, the Intel B70 recorded a generation rate of approximately 52 tokens per second (tok/s), while the RTX 5090 reached approximately 98 tok/s. In terms of prompt processing (prefill), the Intel card achieved roughly one-third the speed of the NVIDIA counterpart, recording 960 tok/s compared to the RTX 5090’s 2,830 tok/s.

Despite the lower speeds, the Intel hardware provides 32GB of VRAM at a cost of approximately $41 per GB, whereas the RTX 5090 exceeds $200 per GB.

Optimization and Software Dependencies

The reported performance gains for the Intel Arc Pro B70 were contingent upon specific software configurations. The user noted that “out of the box” performance was suboptimal, requiring several technical adjustments to reach the reported benchmarks. Key requirements included:

  • Kernel and Drivers: The use of Linux Kernel 6.17+ and Intel’s compute runtime via the kobuk-team/intel-graphics PPA.
  • Mesa Updates: Upgrading Mesa to version 26.x was cited as a critical factor, reportedly providing a three-fold increase in generation speed in certain environments.
  • Backend Selection: The user reported that utilizing llama.cpp built with SYCL (Intel oneAPI) significantly outperformed Vulkan-based backends. Under SYCL, the card was able to access the full 32GB of VRAM and handle larger context windows more efficiently.

Market Implications for AI Hardware

While NVIDIA maintains a dominant position regarding raw performance and software stack maturity, the price-to-speed ratio of the Intel Arc Pro B70 presents an alternative for users focused on high-VRAM workloads such as renders, simulations, and LLM inference.

Industry observers on social media noted that while NVIDIA remains the industry standard, the high street prices of the RTX 5090 may drive interest toward non-NVIDIA hardware. However, users should note that these performance figures are based on individual hardware setups—including specific motherboards and memory configurations—and have not been independently verified by third-party laboratory testing.