Apple has shared new performance tests showing how the M5 chip dramatically improves local large language model processing compared with last year’s M4. According to 9to5Mac’s reporting, Apple ran a series of controlled benchmarks that measured how both chips handled on-device LLM workloads, highlighting how much faster the new architecture can compile, load and process models without relying on cloud servers.
The comparison used the same models, same memory configuration and the same system environment. With everything controlled, Apple’s tests showed the M5 completing identical tasks in far less time than the M4, illustrating the generational jump Apple built into its latest silicon.
A Jump in On-Device Model Speed
The benchmarks focused on actions such as running inference loops, handling multi-step text generation, loading models into memory and processing extended prompts. In Apple’s internal demos, the M5 consistently finished these operations at significantly higher speeds. The results closely align with earlier technical disclosures about the chip, which highlighted improved bandwidth, expanded neural engine resources and updated cores designed for high-intensity parallel workloads.
For everyday users, the gains mean that features relying on Apple Intelligence — including real-time summarization, rewriting tools, translation and contextual processing — run more smoothly and respond more quickly. Developers working with local or hybrid model architectures also benefit from the increased throughput, especially when prototyping or optimizing applications for on-device execution.
Why Local LLM Performance Matters
The shift toward running models directly on Apple hardware reduces the need for continuous cloud requests, offering faster response times and improved privacy. As device-side workloads expand, performance headroom becomes crucial. The M5 appears to deliver exactly that, enabling more ambitious Apple Intelligence features and giving third-party developers room to experiment with more advanced on-device logic.
Apple’s emphasis on local processing also aligns with broader industry movement toward edge computing. As models become more efficient — and as Apple silicon grows more capable — more tasks can be handled directly on the device without relying on data-center scale infrastructure.
Architecture Behind the Improvements
Apple’s performance gains stem from multiple architectural changes introduced with the M5 generation. These include updated CPU and GPU cores tuned for sustained load, higher memory bandwidth and a neural engine designed specifically for accelerating model inference. The combined effects produce faster token generation, more stable performance during extended runs and lower latency during interactive AI features.
The company’s testing suggests that the M5’s improvements are not limited to ideal conditions. Even under heavier system load, the chip maintains a consistent advantage over the M4 in local model tasks. This positions the M5 as a stronger foundation for the next wave of software updates centered on Apple Intelligence.
The performance leap helps establish the baseline for Apple’s 2025–2026 hardware cycle. Devices adopting the M5 — including MacBook Pro, iPad Pro and Vision Pro — are positioned to handle increasingly sophisticated AI workflows as Apple expands its ecosystem. The company’s demonstration also signals how future chips, including the expected M6 family, may push this trend further.
As Apple adds more generative and context-aware capabilities into system apps, writing tools, development frameworks and creative workflows, chips like the M5 become a practical requirement for smooth, real-time performance. Developers building tools for Apple Intelligence will likely target M5-class hardware as their baseline when designing deeper integration.
As the Apple silicon roadmap continues, local model performance is becoming just as important as traditional CPU and GPU measures. Apple’s early tests show the M5 is well ahead of its predecessor in this area, setting the stage for a more AI-centric generation of devices.
