AI system category
Mac mini and Apple Silicon setups for local AI, coding, and macOS development. No separate GPU required.
Quote-only Mac systems
Local AI Mac
Configuration: Mac mini M4 Pro 64GB / 2TB
A quiet M4 Pro Mac mini for private AI chat, coding, and Apple-friendly local AI projects.
Chip: Apple M4 Pro
Memory: 64 GB unified
Storage: 2000 GB SSD
30B-class Q4 only with caveats
Fit assumes quantization, moderate context, and runtime validation; 70B is not a normal target without 48GB+ VRAM or large unified memory.
€4,481
Planning estimate
Mac Studio M4 Max 64GB / 1TB
Exact Z1CD-12100 BTO configuration with the 16-core CPU, 40-core GPU, 64GB unified memory, and current Estonian retailer coverage.
Chip: Apple M4 Max
Memory: 64 GB unified
Storage: 1000 GB SSD
30B-class Q4 only with caveats
Fit assumes quantization, moderate context, and runtime validation; 70B is not a normal target without 48GB+ VRAM or large unified memory.
Price unavailable
Price information
Mac mini M4 Pro 48GB / 1TB
Exact CZ1JV-111000 BTO configuration with current Estonian retailer coverage.
Chip: Apple M4 Pro
Memory: 48 GB unified
Storage: 1000 GB SSD
30B-class Q4 only with caveats
Fit assumes quantization, moderate context, and runtime validation; 70B is not a normal target without 48GB+ VRAM or large unified memory.
Price unavailable
Price information
Mac Studio M4 Max 36GB / 512GB
Exact standard retail configuration MU963ZE/A.
Chip: Apple M4 Max
Memory: 36 GB unified
Storage: 512 GB SSD
30B-class Q4 only with caveats
Fit assumes quantization, moderate context, and runtime validation; 70B is not a normal target without 48GB+ VRAM or large unified memory.
Price unavailable
Price information
Mac mini M4 Pro 24GB / 512GB
Exact standard retail configuration MCX44ZE/A.
Chip: Apple M4 Pro
Memory: 24 GB unified
Storage: 512 GB SSD
13B/14B Q4 at practical context
Everyday local LLM fit assumes quantization and moderate context; 30B is not a normal target without more VRAM.
Price unavailable
Price information
Recommended model
A capable MoE model that gives 32GB and 48GB workstations a meaningfully heavier coding target.
Usable
Likely good unified-memory headroom for this quantized model at moderate context sizes.
Serious coding help, complex reasoning, and longer document analysis
A strong dense model for 24GB to 32GB GPUs and higher-memory Apple systems.
Expected experience: Fast
Likely good unified-memory headroom for this quantized model at moderate context sizes.
Private assistant chat, document understanding, and image or audio analysis
A current multimodal everyday model that makes good use of a 16GB GPU.
Expected experience: Fast
Likely good unified-memory headroom for this quantized model at moderate context sizes.
Assumptions
Family: Qwen3.6
Parameters: 35B
Structure: 35B total / 3B active MoE
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Extended context: 1,010,000 via YaRN; not a default fit assumption
Quantization: Q4_K_M
Approx. Q4 weights: 24 GB
Default estimate: 31.5 GB @ 8,192 tokens
Weights / KV / runtime / margin: 24 GB / 2.5 GB / 2 GB / 3 GB
CPU/RAM fallback: Not recommended
VRAM: 32 GB minimum / 48 GB recommended
RAM: 64 GB minimum / 64 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Usable
Full GPU offload: Only when the memory estimate and context fit
Context warning: Long repository context can use the remaining headroom quickly, especially on 32GB GPUs.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 24 GB | 1.5 GB | 2 GB | 3 GB | 30.5 GB |
| 8K | 24 GB | 2.5 GB | 2 GB | 3 GB | 31.5 GB |
| 16K | 24 GB | 5 GB | 2 GB | 3.5 GB | 34.5 GB |
| 32K | 24 GB | 10 GB | 2 GB | 4 GB | 40 GB |
Swipe the table sideways to inspect every memory estimate.
Family: Qwen3.6
Parameters: 27B
Structure: 27B dense
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Extended context: 1,010,000 via YaRN; not a default fit assumption
Quantization: Q4_K_M
Approx. Q4 weights: 17 GB
Default estimate: 23 GB @ 8,192 tokens
Weights / KV / runtime / margin: 17 GB / 2 GB / 1.5 GB / 2.5 GB
CPU/RAM fallback: Not recommended
VRAM: 24 GB minimum / 32 GB recommended
RAM: 48 GB minimum / 64 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Fast
Full GPU offload: Only when the memory estimate and context fit
Context warning: Repository-scale or very long document context can push a 24GB card beyond a comfortable fit.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 17 GB | 1 GB | 1.5 GB | 2.5 GB | 22 GB |
| 8K | 17 GB | 2 GB | 1.5 GB | 2.5 GB | 23 GB |
| 16K | 17 GB | 4 GB | 1.5 GB | 3 GB | 25.5 GB |
| 32K | 17 GB | 8 GB | 1.5 GB | 3.5 GB | 30 GB |
Swipe the table sideways to inspect every memory estimate.
Family: Google Gemma 4
Parameters: 11.95B
Structure: 11.95B dense
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Quantization: Q4_0 QAT
Approx. Q4 weights: 7.2 GB
Default estimate: 11.5 GB @ 8,192 tokens
Weights / KV / runtime / margin: 7.2 GB / 1.5 GB / 1.2 GB / 1.5 GB
CPU/RAM fallback: Not recommended
VRAM: 12 GB minimum / 16 GB recommended
RAM: 24 GB minimum / 32 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Fast
Full GPU offload: Only when the memory estimate and context fit
Context warning: Its large context window still requires substantial KV-cache headroom.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 7.2 GB | 1 GB | 1.2 GB | 1.5 GB | 11 GB |
| 8K | 7.2 GB | 1.5 GB | 1.2 GB | 1.5 GB | 11.5 GB |
| 16K | 7.2 GB | 2.5 GB | 1.2 GB | 1.5 GB | 12.5 GB |
| 32K | 7.2 GB | 5 GB | 1.2 GB | 2 GB | 15.5 GB |
Swipe the table sideways to inspect every memory estimate.
Research sources
Researched: 2026-07-30
Local AI performance is approximate. Results depend on quantization, context length, backend, drivers, and whether the model plus KV cache fits in VRAM or Apple unified memory.
