Mac Studio & Mac Mini Local LLM Configurator

M5 Architecture • Unified Memory Bandwidth • Multitasking Workload Overhead

Hardware & Model Selection
Mac mini M5
Apple Silicon Chip 153 GB/s Bandwidth
Unified Memory (RAM) GPU Max VRAM: ~24.0 GB
Target Local LLM Model 8.0B Parameters
Quantization 4-bit
Context Length 8,192 tokens
Concurrent Developer Workloads
5.8 GB RAM
IntelliJ IDEA / WebStorm / PyCharm 1 Window (~2.5 GB)
1 inst.
VS Code Windows / Workspaces 2 Windows (~1.6 GB)
2 win.
Docker Containers (Postgres, Redis, API) 2 Containers (~2.5 GB)
2 cont.
OBS Studio Screen Recording Apple Silicon Hardware ProRes/HEVC Buffer (~1.2 GB)
Chrome / Safari Dev Tabs (15-20 tabs) Fixed ~2.2 GB
macOS Base System & Metal Headroom Fixed ~3.8 GB
Optimal Local LLM Fit
Full model weights and 8k context fit comfortably within Apple Silicon Unified Memory without swapping or throttling background developer tooling.
Token Generation
42.8tok/s
Memory Bandwidth Bound
Prompt Processing
280tok/s
Metal Compute Bound
VRAM Utilization
56%
18.0 / 32 GB Used
Available Headroom
14.0GB
Zero Swap Active
Unified Memory Allocation Breakdown
Total: 32 GB
LLM Weights
KV Cache (Context)
Docker VMs
IntelliJ + VS Code
OBS Studio
macOS & Browsers
Free Headroom
Subsystem / Process Allocation Type Memory (GB) % of Total
Ready-To-Run MLX / Ollama Command
# Run natively on Apple Silicon Metal (Zero Swap Verified):
mlx_lm.generate --model mlx-community/Llama-3.1-8B-Instruct-4bit --prompt "Write a concurrent Go worker pool" --max-tokens 512
Memory Bandwidth Bottleneck
With 153 GB/s unified bandwidth and a 5.4 GB model weight footprint, theoretical generation throughput peaks at ~28.3 tok/s before Metal scheduling overhead.
Recommended Form Factor
Mac mini with M5 (32GB) offers the highest performance-per-dollar ratio for models up to 14B parameters.
Context Cache Overhead
KV cache consumes ~1.2 GB for 8k tokens in 16-bit FP cache. For 32k+ windows, consider 8-bit quantized KV cache to save 50% RAM.