Arc B60 vs RTX 5060 Ti for Local LLMs: Performance, Drivers, and eGPU Realities

As the demand for local large language models (LLMs) surges, consumers are increasingly turning to external GPUs (eGPUs) to augment their laptops’ limited graphics capabilities. A recent Reddit thread from r/LocalLLaMA sparked a heated debate between two emerging contenders: the Intel Arc B60 with 24GB of GDDR6 memory and the rumored NVIDIA RTX 5060 Ti with 16GB. While the Arc B60’s larger VRAM appears advantageous for running larger models in tools like LM Studio, experts caution that raw memory capacity alone doesn’t guarantee optimal performance — especially when driver support and ecosystem maturity are factored in.

Intel’s Arc series, despite significant progress since its 2022 debut, continues to face skepticism in professional AI circles. According to user reports aggregated on Zhihu’s discussion threads on GPU performance and browser technologies, Intel’s Windows drivers for Arc GPUs remain inconsistent in their handling of CUDA-equivalent workloads, particularly those involving tensor operations critical to LLM inference. While Intel has invested in oneAPI and XeSS technologies, the lack of native support for CUDA — the de facto standard for most AI frameworks — forces users to rely on less optimized software layers such as DirectML or OpenVINO, which can introduce latency and reduce throughput.

In contrast, NVIDIA’s RTX lineup, even in mid-tier iterations, benefits from years of CUDA ecosystem refinement. Tools like LM Studio, Ollama, and Text Generation WebUI are engineered with NVIDIA GPUs in mind, offering plug-and-play compatibility and performance optimizations that simply don’t exist for Intel hardware. Although the RTX 5060 Ti is still unannounced as of early 2025, industry analysts anticipate it will follow the architectural lineage of the RTX 4060 Ti, featuring improved tensor cores and DLSS 3.5 support — both of which accelerate LLM token generation. Even with 8GB less VRAM than the Arc B60, the RTX 5060 Ti’s superior per-core efficiency and memory bandwidth could outperform its Intel rival in real-world LLM inference scenarios.

Moreover, the user’s proposed connection methods — USB-C 40Gbps and Oculink — introduce another layer of complexity. While Oculink (a PCIe-based external connection) offers lower latency and higher throughput than Thunderbolt 4, many eGPU enclosures still suffer from bandwidth throttling and power delivery inconsistencies. In benchmark tests conducted by independent hardware reviewers, eGPU performance typically caps at 70–80% of internal GPU performance due to PCIe lane limitations. This means that even a theoretically superior 24GB VRAM card may not fully leverage its memory if the connection cannot sustain high data transfer rates required by LLMs.

Interestingly, the original poster also mentioned the AMD RX 7900 XTX and the upcoming RX 9000 series as alternatives. AMD’s RDNA 3 architecture has shown strong FP16 performance in AI benchmarks, and with ROCm support improving on Windows, the 7900 XTX’s 24GB VRAM could offer a compelling middle ground — provided the user is willing to experiment with ROCm-compatible LLM runners. However, AMD’s Windows driver stability for non-gaming workloads remains inconsistent, and toolchain support for local AI is still nascent compared to NVIDIA’s.

For the average user running LM Studio on Windows 11, the recommendation leans toward NVIDIA. The RTX 5060 Ti, when released, is expected to deliver a more reliable, faster, and better-supported experience despite its lower VRAM. The Arc B60 may be tempting for those pushing 13B+ parameter models, but only if they are prepared to troubleshoot driver issues, optimize memory management manually, and accept slower inference speeds. For most, the ecosystem advantage of NVIDIA outweighs the memory advantage of Intel.

Ultimately, this decision is not just about hardware specs — it’s about workflow continuity, software compatibility, and long-term maintainability. In the rapidly evolving world of local AI, the most powerful GPU is not always the one with the most VRAM — it’s the one that works without friction.