arXiv:2607.00501v1 Announce Type: new Abstract: We present BaseRT, a native Metal inference runtime for large language models (LLMs) on Apple Silicon, and report the highest inference throughput on this hardware to date. Existing runtimes, including llama.cpp and MLX-based frameworks, incur overhead from abstractions not designed for Metal's execution model or Apple Silicon's unified memory topology. By building natively on Metal with chip-specific kernel fusion, unified memory-aware optimisation, and custom dispatch logic, BaseRT recovers performance that framework-based approaches leave on t
The continuous drive for more efficient and powerful AI inference on edge devices, particularly consumer hardware like Apple Silicon, is pushing for native optimizations to overcome existing framework limitations.
Achieving significantly higher LLM inference throughput on widely available consumer hardware like Apple Silicon democratizes powerful AI capabilities, reducing reliance on cloud infrastructure and specialized GPUs.
Local LLM inference becomes substantially more performant and accessible on Apple devices, enabling advanced AI applications directly on user hardware with lower latency and improved privacy.
- · Apple
- · Apple Silicon users
- · On-device AI application developers
- · Edge computing
- · Cloud-based LLM inference providers
- · Generic cross-platform AI frameworks
- · Developers neglecting native optimizations
Individual Apple devices can run more complex and responsive LLMs locally.
A new wave of AI applications emerges that leverages powerful on-device inference, fostering a richer ecosystem independent of constant cloud connectivity.
This could accelerate the trend towards personal AI agents operating securely and privately on user hardware, influencing the architecture of future personal computing.
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