This article was originally published on runaihome.com
TL;DR: The Mac Mini M4 Pro with 24GB unified memory is the only sub-$1,500 single-box purchase that runs 32B models fully in memory without CPU offloading. It draws 30–40W, idles nearly silent, and doubles as a real Mac desktop. The trade-off: it is 2–3× slower than an RTX 4090 at identical model sizes, and CUDA-dependent workflows (Stable Diffusion optimized pipelines, QLoRA fine-tuning) don't translate cleanly to Apple Silicon.
| Mac Mini M4 Pro 24GB | RTX 5060 Ti 16GB PC | Mac Mini M4 Pro 48GB | |
|---|---|---|---|
| Best for | 14B–32B chat, coding assistant, low-power always-on | 7B–14B chat, fastest small-model speeds, fine-tuning | 70B inference, large context, production AI server |
| Memory | 24GB unified | 16GB GDDR7 | 48GB unified |
| 7B tok/s | ~50 (llama.cpp Q4) | ~41–51 (GPU-only) | ~50 |
| 32B tok/s | 15–22 | ❌ (needs CPU offload, 3–8 tok/s) | 22–28 |
| Price | $1,399 all-in | ~$1,100–$1,400 total system | ~$1,799+ all-in |
| System power | 30–40W under load | ~220–280W under load | 35–45W under load |
| The catch | 3–4× slower than RTX 4090 on small models | Can't run 32B at usable speed | Memory locked at purchase |
Honest take: If you live in the 14B–32B model range and don't want to manage a Windows GPU rig, the M4 Pro 24GB is the obvious choice. If you run 7B models exclusively or need CUDA tools, a $429 RTX 5060 Ti in an existing PC beats it on speed and cost.
Why the unified memory number means something different here
When an RTX 5060 Ti has 16GB, that number describes memory physically soldered to the GPU PCB, separated from your system RAM by a PCIe 4.0 x16 bus running at 32 GB/s. When a model slightly exceeds 16GB, the framework starts offloading layers to system RAM across that PCIe bridge — and token generation drops from ~35 tok/s to 3–8 tok/s in documented benchmarks.
The M4 Pro has no such divide. The CPU, GPU, and Neural Engine draw from a single 24GB pool at 273 GB/s. There is no separate "VRAM" and "system RAM" — the entire pool runs at the same bandwidth regardless of what's accessing it. A 20GB model at Q4_K_M doesn't overflow; it simply occupies 20GB out of 24GB, and inference runs at full 273 GB/s the entire time.
This is not Apple marketing. It is a genuine architectural difference that changes which model sizes are practically usable on which hardware.
M4 Pro specifications
Apple introduced the M4 Pro alongside the M4 Max in October 2024 on a 3nm process. Two variants exist:
| Spec | M4 Pro 12c/16c GPU | M4 Pro 14c/20c GPU |
|---|---|---|
| CPU cores | 12 (8P + 4E) | 14 (10P + 4E) |
| GPU cores | 16 | 20 |
| Neural Engine | 16-core, 38 TOPS | 16-core, 38 TOPS |
| Memory bandwidth | 273 GB/s | 273 GB/s |
| Memory options | 24GB or 48GB | 24GB or 48GB |
| Mac Mini base price | $1,399 | higher-tier BTO |
For LLM inference, the GPU core count difference between 12c and 14c variants is minor — both share the same 273 GB/s memory bus, and token generation throughput is overwhelmingly memory-bandwidth limited for the model sizes that fit in 24GB. The 14c/20c GPU model matters slightly more for image generation workloads where raw shader throughput has more bearing.
Neither variant's memory is upgradeable after purchase. This is the most consequential spec decision you will make — more so than CPU or GPU tier.
What actually fits in 24GB vs 48GB
The rule of thumb for Q4_K_M GGUF: roughly 0.55–0.60 GB per billion parameters for weights, plus KV cache that scales with context window. In practice:
| Model | Q4_K_M Weights | KV @ 8K ctx | Fits in 24GB? | Fits in 48GB? |
|---|---|---|---|---|
| Llama 3.1 8B | ~4.7 GB | ~2.0 GB | ✅ Comfortable | ✅ |
| Qwen3 8B | ~5.0 GB | ~2.0 GB | ✅ Comfortable | ✅ |
| Qwen3 14B | ~9.0 GB | ~2.5 GB | ✅ Comfortable | ✅ |
| DeepSeek-R1-Distill-14B | ~8.8 GB | ~2.5 GB | ✅ Comfortable | ✅ |
| Gemma 4 27B | ~15.5 GB | ~3.0 GB | ✅ Fits | ✅ |
| Qwen3 32B | ~19.8 GB | ~3.5 GB | ⚠️ Tight (fits at 4K ctx) | ✅ Comfortable |
| Llama 3.3 70B | ~40 GB | ~5.0 GB | ❌ | ✅ Fits (45GB total) |
| Qwen3 72B | ~43 GB | ~5.0 GB | ❌ | ⚠️ Very tight |
The Qwen3 32B case deserves attention: at Q4_K_M the weights themselves are ~19.8GB. That leaves only ~4GB for KV cache in a 24GB system, which limits practical context to about 4K–8K tokens. If you use the M4 Pro 24GB for a 32B model with long-context RAG pipelines or agentic workflows generating large outputs, you will hit this ceiling. For standard chat and coding assistant use at moderate context lengths, it runs.
The 48GB model was designed exactly for the 70B tier: Llama 3.3 70B at Q4_K_M needs roughly 40GB for weights plus ~5GB for a typical context window — it fits in 48GB with modest headroom.
Benchmark numbers
The definitive open-source benchmark source for Apple Silicon LLM performance is the llama.cpp discussion thread #4167, which aggregates contributed results from verified hardware. For the M4 Pro at 7B Q4_0:
| Metric | M4 Pro (16c GPU, 273 GB/s) |
|---|---|
| 7B Q4_0 token generation | 49.64 tok/s |
| 7B Q4_0 prompt processing | 364.06 tok/s |
| 7B F16 token generation | 17.19 tok/s |
For comparison, the M4 base chip (10c GPU, 120 GB/s) hits 24.11 tok/s on the same 7B Q4_0 benchmark — roughly half the M4 Pro speed, which tracks directly with the 2.3× bandwidth ratio (273/120 = 2.28). This is important: on Apple Silicon, token generation throughput scales almost linearly with memory bandwidth.
For larger models, the same bandwidth-proportional scaling applies. If M4 Pro generates 7B Q4 at ~50 tok/s, the expected throughput at 14B (roughly twice the data to stream per token) is ~25 tok/s — consistent with the 20–30 tok/s range reported across multiple community benchmarks. For 32B models at Q4_K_M on the 24GB variant, community results cluster around 15–22 tok/s via Ollama, with MLX delivering the upper end of that range.
One important note on frameworks: Apple's MLX inference framework is optimized specifically for Metal on Apple Silicon and consistently outperforms llama.cpp's Metal backend by 20–30% for dense models, and up to 3× for MoE architectures. Ollama is shipping an MLX backend preview that showed 57% faster prefill and 93% faster generation on supported models. For maximum throughput today, using MLX directly or the MLX-accelerated Ollama build is worth the setup step over the standard Ollama release.
Where the GPU still wins
Being honest about the trade-offs is the point of this article, not a footnote.
Raw token speed on small models: An RTX 5060 Ti 16GB generates tokens at 41–51 tok/s on 7B–8B models at Q4_K_M. The M4 Pro is in the same ballpark at ~50 tok/s (llama.cpp Q4_0). For 14B models, the RTX 5060 Ti pulls ahead: ~31–35 tok/s in full GPU mode versus ~22–25 tok/s on M4 Pro. If your primary model is a 7B coding assistant and you have an existing PC, adding an RTX 5060 Ti is faster and cheaper than buying a Mac Mini.
RTX 4090 gap: The RTX 4090 generates 95–135 tok/s on 7B–8B models — roughly 2–3× faster than the M4 Pro for models where the 4090's 24GB VRAM is sufficient. If pure inference speed at the 8B–14B tier matters more than anything, the 4090 is the answer. The Mac Mini M4 Pro only outpaces it when the model is too large for 24GB VRAM (i.e., 32B at Q5 or higher).
CUDA ecosystem: QLoRA fine-tuning on Apple Silicon is possible via MLX, but the tooling is substantially less mature than the PyTorch/bitsandbytes/CUDA stack that runs on NVIDIA GPUs. If you are doing active model fine-tuning, synthetic data generation pipelines, or any workflow that depends on Triton-compiled kernels, a GPU is the right choice.
Stable Diffusion / image generation: ComfyUI on macOS via Metal runs SDXL and Flux inference, but significantly slower than a CUDA-native setup. The RTX 5060 Ti's 180W TDP and dedicated GDDR7 bandwidth gi
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