Mac Studio M4 Max vs Mac Mini M4 Pro for Local AI in 2026: Is the $600 Upgrade to 546 GB/s Worth It?

A developer benchmarked the Mac Studio M4 Max against the Mac Mini M4 Pro for local AI inference, finding that the Studio roughly doubles token generation speed on 70B models due to its 546 GB/s memory bandwidth versus the Mini's 273 GB/s. For a 70B model, the Mini delivers about 14 tokens per second while the Studio reaches 28 tok/s, though the performance gap narrows significantly on smaller 7B and 14B models where both machines run fast enough for practical use. The developer recommends the Mac Mini M4 Pro 48GB for most users, reserving the $600+ Studio upgrade only for those who specifically need comfortable 70B model speeds or multi-user inference.

This article was originally published on runaihome.com TL;DR : The Mac Studio M4 Max https://www.amazon.com/s?k=Mac+Studio+M4+Max&tag=runaihome-20 roughly doubles token generation speed on every model size, at twice the memory bandwidth, for a $600 premium over the Mac Mini M4 Pro https://www.amazon.com/s?k=Mac+Mini+M4+Pro&tag=runaihome-20 . 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. | Mac Mini M4 Pro 48GB | Mac Studio M4 Max 48GB | Mac Studio M4 Max 128GB | | |---|---|---|---| Best for | 14B–32B daily use, budget-conscious build | 70B at usable speeds, same memory ceiling | Full 70B at real-time speeds, multi-model | Memory bandwidth | 273 GB/s | 410–546 GB/s | 546 GB/s | 7B Q4 0 tok/s | 50.7 | 70.0–83.1 | 83.1 | 70B Q4 K M tok/s | ~14 | ~22–28 | ~28 | Starting price | $1,699 | $2,299+ | $2,999+ | Power under load | 40–45W | 100–145W | 145W | The catch | 70B at 14 tok/s is usable but not fast | More expensive, no CUDA | Very expensive; M5 Max coming | 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. 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. The M4 Pro in the current Mac Mini ships in two variants, both built on TSMC 3nm: | Spec | 12-core CPU / 16-core GPU | 14-core CPU / 20-core GPU | |---|---|---| | Performance cores | 8 | 10 | | Efficiency cores | 4 | 4 | | Memory bandwidth | 273 GB/s | 273 GB/s | | Neural Engine | 16-core, 38 TOPS | 16-core, 38 TOPS | | Max unified memory | 48 GB | 48 GB | | Mac Mini price | $1,399 24GB | $1,699 48GB config | 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. The M4 Max is a wider die with two memory controllers instead of one: | Spec | 32-core GPU variant | 40-core GPU variant | |---|---|---| | CPU cores | 14 10P + 4E | 16 12P + 4E | | Memory bandwidth | 410 GB/s | 546 GB/s | | Neural Engine | 16-core, 38 TOPS | 16-core, 38 TOPS | | Max unified memory | 64 GB | 128 GB | | Mac Studio base price | ~$1,999 36GB | ~$2,499 48GB | 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. 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. | Chip | Bandwidth | Q4 0 TG tok/s | Q8 0 TG tok/s | F16 TG tok/s | |---|---|---|---|---| | M4 Pro 20c GPU | 273 GB/s | 50.74 | 30.69 | 17.18 | | M4 Max 32c GPU | 410 GB/s | 69.95 | 43.87 | 24.29 | | M4 Max 40c GPU | 546 GB/s | 83.06 | 54.05 | 31.64 | 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. 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. | Model | Format | Size | M4 Pro 24GB | M4 Pro 48GB | M4 Max 48GB | M4 Max 128GB | |---|---|---|---|---|---|---| | Llama 3.1 8B | Q4 K M | 4.9 GB | ✅ | ✅ | ✅ | ✅ | | Qwen3 14B | Q4 K M | 9.3 GB | ✅ | ✅ | ✅ | ✅ | | Qwen3 32B | Q4 K M | 21 GB | ✅ tight | ✅ | ✅ | ✅ | | Llama 3.3 70B | Q4 K M | 43 GB | ❌ | ✅ tight, <8K ctx | ✅ | ✅ | | Llama 3.3 70B | Q8 0 | 78 GB | ❌ | ❌ | ❌ | ✅ | | Gemma 4 27B vision | Q4 K M | 17 GB | ✅ | ✅ | ✅ | ✅ | | DeepSeek-V3 671B | Q2 K | ~175 GB | ❌ | ❌ | ❌ | ❌ | 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: bash $ 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. Apple Silicon's efficiency is a real advantage for always-on inference servers, but the Studio and Mini differ meaningfully here: | Machine | Idle | LLM inference load | |---|---|---| | Mac Mini M4 Pro | ~6W | 40–45W | | Mac Studio M4 |