This article was originally published on runaihome.com

TL;DR: The Mac Studio M4 Max roughly doubles token generation speed on every model size, at twice the memory bandwidth, for a $600 premium over the Mac Mini M4 Pro. For 70B models, that gap is the difference between 14 tok/s (usable but slow) and 28 tok/s (genuinely comfortable). For 7B and 14B models, both machines run fast enough that the gap barely matters in practice.

Honest take: Buy the Mac Mini M4 Pro 48GB unless you specifically need 70B models at comfortable speeds or run inference for more than one person. The Studio's bandwidth advantage only meaningfully shows at the 70B tier — and the Mini handles everything else nearly as well for $600 less.

Why memory bandwidth is the whole game

LLM inference isn't like traditional GPU workloads. Token generation — the part where the model outputs word by word — doesn't require multiplying huge matrices continuously. Each new token reads the entire set of model weights once from memory, applies a relatively small computation, and produces one output token. That means the bottleneck isn't raw GPU shader throughput. It's how fast the memory subsystem can deliver the weights.

The formula is roughly: tokens per second ≈ memory bandwidth ÷ model size in bytes.

At Q4_K_M quantization (~0.55 GB per billion parameters), a 70B model occupies ~43 GB. Feed that through 273 GB/s (M4 Pro) and you get a ceiling around 6 tokens/sec purely from bandwidth — but GPU efficiency and other factors lift real throughput to ~14 tok/s in practice. Feed it through 546 GB/s (M4 Max) and the ceiling doubles, yielding the observed ~28 tok/s.

For a 7B model at ~4.7 GB, even 273 GB/s has headroom to spare. Compute overhead and framework efficiency then matter more, which is why the 7B bandwidth advantage at the tok/s level is closer to 1.6× rather than a clean 2×.

This is why the Mac Studio's advantage is nonlinear across model sizes: it matters most where you need it most (70B), and matters least where the Mini already runs fast enough.

Specs: what's actually inside

M4 Pro (Mac Mini)

The M4 Pro in the current Mac Mini ships in two variants, both built on TSMC 3nm:

Both variants share the same memory bus — you cannot get more than 273 GB/s from the M4 Pro regardless of GPU core count. For LLM inference, the 16-core vs 20-core GPU distinction is largely irrelevant unless you're doing heavy ComfyUI image generation.

M4 Max (Mac Studio)

The M4 Max is a wider die with two memory controllers instead of one:

The bandwidth difference between the two M4 Max variants is significant: 410 GB/s vs 546 GB/s. The 40-core GPU model also unlocks 96GB and 128GB memory configurations — the 32-core tops out at 64GB. If 70B models are the target, the 40-core variant is the one worth buying.

Real benchmark numbers

The llama.cpp GitHub community maintains a systematic benchmark thread (Discussion #4167) that covers Apple Silicon chips on identical tests: LLaMA 7B models, batch size 512 for prompt processing (PP) and batch size 1 for token generation (TG). All numbers below are from that thread.

These are all on the same LLaMA 7B model. The TG (token generation) numbers are what you feel in a chat session. Prompt processing (PP) is fast on every chip — the difference you notice is the output speed.

At F16 precision, the M4 Pro gets 17.18 tok/s versus the M4 Max 40c's 31.64 tok/s — a 1.84× ratio that tracks the bandwidth ratio almost exactly (546/273 = 2.0). At Q4_0, the ratio narrows to 1.64× because the smaller model weights partially reduce bandwidth pressure. At 70B models with Q4_K_M (the typical deployment format), the ratio returns toward 2×: the M4 Pro delivers roughly 14 tok/s and the M4 Max 40c delivers roughly 28 tok/s on Llama 3.3 70B.

Those 28 tok/s on the M4 Max feel like a conversation. Those 14 tok/s on the M4 Pro feel like watching someone type fast — usable, but noticeably slower than a cloud API.

What fits where: the memory decision

Memory is locked at purchase on both machines. Buying wrong means either not fitting your target models or paying for headroom you'll never use.

A few practical notes:

The Mac Mini M4 Pro 48GB and 70B: 70B at Q4_K_M takes ~43 GB of weights, which leaves only ~5 GB for KV cache. At 4K context that's workable. At 8K+ context you'll hit memory pressure warnings. On the 48GB Mac Mini M4 Pro with Ollama 0.6.x, pull and check actual memory usage:

$ ollama run llama3.3:70b-instruct-q4_K_M
$ ollama ps

Expected output on Mac Mini M4 Pro 48GB:

NAME                                ID              SIZE      PROCESSOR    UNTIL
llama3.3:70b-instruct-q4_K_M       a6eb4748fd29    43 GB     100% GPU     4 minutes from now

The SIZE column shows 43 GB allocated — leaving roughly 5 GB for KV cache and system overhead. Context is limited accordingly. The 48GB gives you room for reasonable context lengths at Q4_K_M; if you need 8K+ context reliably, 64GB+ (M4 Max only) is the practical floor.

The Mac Studio M4 Max 48GB and 70B: Same story as the 48GB Mini — both run 70B at Q4_K_M but constrain context. The difference is that the Studio runs it at 28 tok/s vs the Mini's 14 tok/s.

When 128GB matters: Q8_0 70B (near-lossless quality) needs 78 GB. Running two 32B models concurrently in Open WebUI needs 42+ GB. Hosting a 70B alongside a 14B specialist for routing needs 53+ GB. If any of those are your use case, 128GB is the right tier, and the M4 Max is the only consumer Apple Silicon chip that gets there.

Power and cost to run

Apple Silicon's efficiency is a real advantage for always-on inference servers, but the Studio and Mini differ meaningfully here: