HARDWARE FIT CHECK • Microsoft Phi-4 Mini 3.8B + Apple Mac Mini M4 (16GB Unified Memory)3.92 GB / 13 GB VRAM

Can I Run Microsoft Phi-4 Mini 3.8B on Apple Mac Mini M4 (16GB Unified Memory)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Microsoft Phi-4 Mini 3.8B (3.8B) on Apple Mac Mini M4 (16GB Unified Memory) (13 GB).

✓ FITS IN VRAM3.92 GB (28%)~29 t/s

Microsoft Phi-4 Mini 3.8B on Apple Mac Mini M4 (16GB Unified Memory)

✓ Fits Comfortably in VRAM

✓ FITS IN VRAM

Required VRAM

3.92 GB

28% of hardware capacity (14 GB)

Est. Decode Speed

~29 tok/s

Dense model @ 120 GB/s

Available Headroom

+10.08 GB

Safe for OS display & full context

Memory Allocation & Headroom

3.92 GB allocated of 14 GB capacity

28% utilized

Weights: 2.28 GB

KV: (32K): 0.81 GB

Overhead: 0.83 GB

Free Headroom: +10.08 GB

Architecture & Throughput Transparency: Single-user decode speed (~29 tok/s) is memory-bandwidth bound at batch size = 1 on Apple Mac Mini M4 (16GB Unified Memory) (120 GB/s). Prompt prefill / TTFT (~421 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

Local Hardware Configurations & Pricing

High-bandwidth VRAM specs and current market prices

Consumer GPU

32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP

$1,999Amazon Prime →

Consumer GPU

24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP

$1,699Amazon Prime →

Multi-GPU Rig

48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0

1648 GB/s700W TDP

$1,550Amazon Prime →

Apple Silicon

13 GB VRAM

Apple Mac Mini M4 (16GB Unified Memory)

Silent 25W desktop running 7B–8B models at full speed with 32k context

120 GB/s25W TDP

$599Amazon Prime →

Cloud Instance Options for Microsoft Phi-4 Mini 3.8B

On-Demand

Deploy this model on an on-demand cloud GPU without local hardware constraints or power usage:

RunPod Cloud GPUsBest 1-Click Ollama & vLLM

$0.38/hr (RTX 4090 Spot) – $2.39/hr (H100)

Launch 1-Click GPU Pod →

Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090

$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)

Rent Spot GPU →

Lambda GPU CloudEnterprise 8x H100 Clusters

$2.49/hr (H100) – $19.92/hr (8x H100)

Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Est. Speed	Fit Status
FP1616 bpw	7.6 GB	0.81 GB	9.43 GB (67%)	~9 t/s	FITS
Q8_08.5 bpw	4.03 GB	0.81 GB	5.73 GB (41%)	~17 t/s	FITS
Q6_K6.56 bpw	3.12 GB	0.81 GB	4.79 GB (34%)	~22 t/s	FITS
Q5_K_M5.68 bpw	2.7 GB	0.81 GB	4.35 GB (31%)	~25 t/s	FITS
Q4_K_M4.8 bpw	2.28 GB	0.81 GB	3.92 GB (28%)	~29 t/s	FITS
FP44.25 bpw	2.01 GB	0.81 GB	3.64 GB (26%)	~33 t/s	FITS
IQ3_XS3.3 bpw	1.56 GB	0.81 GB	3.17 GB (23%)	~41 t/s	FITS

FAQ & Reference

Frequently Asked Questions: Microsoft Phi-4 Mini 3.8B on Apple Mac Mini M4 (16GB Unified Memory)

How much VRAM does Microsoft Phi-4 Mini 3.8B need on Apple Mac Mini M4 (16GB Unified Memory)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Microsoft Phi-4 Mini 3.8B requires 3.92 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Microsoft Phi-4 Mini 3.8B achieve on Apple Mac Mini M4 (16GB Unified Memory)?

Based on Apple Mac Mini M4 (16GB Unified Memory)'s 120 GB/s memory bandwidth, expected decode speed is approximately 29 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.