M4 MacBook Pro

Original Article ↗ Hacker News Discussion ↗

Apple’s new M4-based MacBook Pro line is praised for significant CPU and memory bandwidth gains, higher RAM defaults (16 GB across M4 Macs), and options like nano‑texture matte displays, which many see as overdue quality-of-life improvements. Commenters are split on whether these changes justify upgrading from already-capable M1/M2 machines, especially given persistent complaints about soldered storage, expensive RAM and SSD tiers, and the lack of Wi‑Fi 7. A major thread centers on local AI and LLM workloads: the M4 Max’s unified memory and 128 GB ceiling are attractive for inference on larger models, but true high-end training still clearly favors dedicated GPUs and cloud setups.

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.