HARDWARE FIT CHECK • Mistral Small 3 24B Instruct + Apple Mac Mini M4 (24GB Unified Memory)18.33 GB / 20 GB VRAM

Can I Run Mistral Small 3 24B Instruct on Apple Mac Mini M4 (24GB Unified Memory)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Mistral Small 3 24B Instruct (24B) on Apple Mac Mini M4 (24GB Unified Memory) (20 GB).

• TIGHT FIT18.33 GB (92%)~5 t/s

Mistral Small 3 24B Instruct on Apple Mac Mini M4 (24GB Unified Memory)

• Tight Fit — Runs 100% on GPU (Low Headroom)

• TIGHT FIT

Required VRAM

18.33 GB

92% of hardware capacity (20 GB)

Est. Decode Speed

~5 tok/s

Dense model @ 120 GB/s

Available Headroom

+1.67 GB

Tight margin — close background apps

Memory Allocation & Headroom

18.33 GB allocated of 20 GB capacity

92% utilized

Weights: 14.4 GB

KV: (32K): 2.68 GB

Overhead: 1.25 GB

Free Headroom: +1.67 GB

Architecture & Throughput Transparency: Single-user decode speed (~5 tok/s) is memory-bandwidth bound at batch size = 1 on Apple Mac Mini M4 (24GB Unified Memory) (120 GB/s). Prompt prefill / TTFT (~73 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

Tight Fit: Consider 32GB+ for Safe Headroom

High-bandwidth VRAM specs and current market prices

Consumer GPU↑ Upgrade

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↑ Upgrade

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↑ Upgrade

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

20 GB VRAM

Apple Mac Mini M4 (24GB Unified Memory)

Budget $799 home server running 14B–24B models (Mistral Small 3, Qwen 2.5 14B) with 32k context

120 GB/s25W TDP

$799Amazon Prime →

Cloud Instance Options for Mistral Small 3 24B Instruct

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	48 GB	2.68 GB	53.11 GB (266%)	0 t/s	OOM
Q8_08.5 bpw	25.44 GB	2.68 GB	29.76 GB (149%)	0 t/s	OOM
Q6_K6.56 bpw	19.68 GB	2.68 GB	23.8 GB (119%)	~1 t/s	PARTIAL
Q5_K_M5.68 bpw	17.04 GB	2.68 GB	21.07 GB (105%)	~1 t/s	PARTIAL
Q4_K_M4.8 bpw	14.4 GB	2.68 GB	18.33 GB (92%)	~5 t/s	FITS
FP44.25 bpw	12.72 GB	2.68 GB	16.6 GB (83%)	~5 t/s	FITS
IQ3_XS3.3 bpw	9.84 GB	2.68 GB	13.61 GB (68%)	~7 t/s	FITS

FAQ & Reference

Frequently Asked Questions: Mistral Small 3 24B Instruct on Apple Mac Mini M4 (24GB Unified Memory)

How much VRAM does Mistral Small 3 24B Instruct need on Apple Mac Mini M4 (24GB Unified Memory)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Mistral Small 3 24B Instruct requires 18.33 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Mistral Small 3 24B Instruct achieve on Apple Mac Mini M4 (24GB Unified Memory)?

Based on Apple Mac Mini M4 (24GB Unified Memory)'s 120 GB/s memory bandwidth, expected decode speed is approximately 5 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.