M4 MacBook Pro

Original Article ↗ Hacker News Discussion ↗

Display & Nano‑Texture / Matte Option

  • Many are excited Apple reintroduced a matte‑like option (nano‑texture) on MacBook Pro for the first time in years.
  • Concerns: nano‑texture’s susceptibility to damage, fingerprint/oil staining, and needing a special cloth. Some prefer simple workarounds (cloth over keyboard) or tempered glass protectors.
  • Question whether nano‑texture will come to the MacBook Air; some suspect it may be used as an upsell on higher‑end models only.

RAM, Storage, and Pricing Strategy

  • Strong approval for base RAM moving to 16 GB across M4 Macs and updated M2/M3 Airs, seen as overdue and improving longevity.
  • Complaints shift to base 256 GB SSD on many models and high prices for internal storage upgrades versus much cheaper external SSDs.
  • Memory laddering is criticized: some configs (e.g., base M4 Max) are capped at 36 GB, requiring a costly CPU upgrade just to access higher RAM tiers; 96 GB options are gone.
  • Some argue 16 GB is enough for typical office/dev work; others insist laptops at these prices should start at far higher RAM or at least be user‑upgradable.

Performance, Benchmarks, and Upgrade Value

  • Apple’s marketing comparisons to old Intel and M1 machines are seen as partly targeted at those users and partly as number‑inflation; real‑world M3→M4 gains are viewed as ~10–20% in many tasks.
  • Single‑core performance of M4 is praised, but many M1/M1 Pro/Max owners say their machines still feel “fast enough” and see little reason to upgrade unless doing heavy builds, media work, or local AI.
  • Several anecdotes: M1/M2 laptops remain quiet, cool, with excellent battery life even under dev workloads; fans rarely spin up.

LLMs, Unified Memory & AI Workloads

  • M4 Max’s 128 GB unified memory and ~546 GB/s bandwidth are viewed as very attractive for local LLM inference; some already use M‑series desktops for this instead of renting cloud GPUs.
  • Still, they’re far slower than datacenter GPUs for training; consensus is LLM inference on Macs is practical, full‑scale training is not.
  • Debate over cost‑effectiveness: for occasional or privacy‑sensitive use, local makes sense; for heavy or frontier‑model use, cloud remains better.

Connectivity & Wi‑Fi 7

  • Lack of Wi‑Fi 7 on new Macs (while iPhone 16 has it) disappoints many, especially those with Wi‑Fi 7 routers wanting near‑2.5 Gbps wireless.
  • Others argue Wi‑Fi 6E is sufficient for most laptop workloads; Wi‑Fi 7 advantages (throughput, preamble puncturing, MLO) are seen as “future‑proofing” rather than essential today.

OS, Privacy, and Alternatives

  • Asahi Linux praised but currently supports only M1/M2; M3/M4 support may take time. Many treat macOS as host with Linux VMs instead.
  • Apple’s privacy posture and on‑device/“private cloud” AI are lauded by some, but others view notarization checks, closed hardware, and App Store control as incompatible with true ownership and privacy.