M4 vs M5 AI Compute Roundup
Mac mini M4 Still Wins for Local LLMs

This guide targets indie developers, ML engineers, and privacy-conscious teams evaluating Apple Silicon for on-device inference. You get three pain points, an M4 vs M5 vs M2 compute matrix, a memory-by-model decision table, a six-step deployment runbook, and citable benchmarks. Bottom line: Mac mini M4 — especially the 24 GB SKU — remains the 2026 value king for local LLM workloads.

Three pain points — why chip marketing misleads buyers

• 1. NPU vs GPU mismatch. M5 may double Neural Engine TOPS for Apple Intelligence features. Ollama and MLX still route inference through GPU cores and unified memory. A 15–25% GPU uplift matters far less than having enough RAM to hold weights plus KV cache.
• 2. Memory is the real ceiling. A 7B model at Q4 needs roughly 4–5 GB. Add context, system overhead, and a second model — and a 16 GB Mac mini runs tight fast. Teams that skip the memory math buy the wrong box before M5 even ships.
• 3. Buy-vs-wait tax. M5 Mac mini rumors point to higher base prices and 512 GB minimum storage. Waiting six months without a dev environment costs more than renting an M4 node today, validating workloads, then deciding on purchase.

M4 vs M5 vs M2 — local LLM compute matrix

Takeaway: M5 may add ~10% inference speed — not 2×. For most 7B–14B pipelines, M4 already clears the usability bar. Paying a launch premium for marginal gains rarely beats renting M4 now.

Memory × model decision table

Six-step runbook — deploy local LLM in one week

1. Size your models. List target parameter counts, quantization (Q4 vs Q8), and concurrent sessions. Pick RAM before picking M4 vs M5.
2. Pick a stack. Use MLX for Apple-native fine-tuning and batch inference. Use Ollama for quick API endpoints and team sharing. Both run cleanly on remote Mac via SSH.
3. Benchmark on real prompts. Measure tokens per second and time-to-first-token on your production prompt set — not synthetic marketing numbers.
4. Provision hardware. Rent LeanVPS M4_24 if you need 13B models or RAG sidecars. Start M4_16 for 7B-only prototypes.
5. Lock network access. Bind Ollama to localhost or tunnel through SSH. Never expose raw inference ports to the public internet.
6. Decide buy vs rent. After 30 days of stable throughput, compare rental TCO against M5 launch pricing. Most teams extend rental through M5 reviews instead of buying blind.

Citable numbers for your hardware memo

• Bandwidth rule: Local LLM throughput scales with unified memory bandwidth first. M4 at ~120 GB/s delivers roughly 35% higher 7B tokens/s vs M2 in community MLX benchmarks.
• Power efficiency: Mac mini M4 idle draw sits near 6–8 W — about one-third of a mid-tier NVIDIA desktop idling a 7B model, before counting GPU purchase cost.
• Rental TCO: LeanVPS M4_24 from $96.5/month beats a $799 Mac mini plus electricity over 8 months for short-cycle R&D — cancel when the experiment ends.

FAQ — M5 hype vs M4 reality

• Should I wait for M5 Mac mini? Only if your workload is Apple Intelligence system APIs — not Ollama serving. For private LLM APIs, rent M4 today and reassess at M5 ship plus 90 days of community benchmarks.
• Can I run local LLM without owning a Mac? Yes. SSH into a LeanVPS M4 node, install Ollama or MLX, forward ports locally, and treat the box as your inference server — same workflow as a machine on your desk.
• M4_16 or M4_24 for a startup? Prototype on M4_16. Move to M4_24 before production if you serve 13B models, run embedding indexes, or keep two quantized models loaded.

Summary — rent the right RAM, not the hype chip

M5 will be faster on paper. For local LLM serving in 2026, memory capacity and bandwidth decide usability — and M4 already hits the sweet spot at a price M5 is unlikely to undercut at launch. Waiting without hardware burns sprint time; buying before benchmarking burns budget.

Recommended action: Open LeanVPS M4_24 on the purchase page — SSH the same day, monthly billing, cancel anytime. Run this six-step checklist on real prompts for 30 days. If throughput holds, keep renting through M5 reviews; if you outgrow 24 GB, upgrade SKU before chasing a new chip generation.