Run it locally

Will your machine run a local AI model?

Check whether a model is likely to fit in your computer’s memory. This is a memory estimate, not a speed, software-support, or overall suitability check.

What are you running it on?

Graphics

Memory (RAM), GB

System type

Estimated memory available to a model: ~13 GB · 16 GB system RAM (CPU inference)

Likely memory fit

· modeled to fit with headroom

Ministral 3 14B · Efficient local daily-driver

needs ~10 GB · 14B

Phi-4 · Compact reasoning model

needs ~10 GB · 14B

Local model (12B via Ollama) · Local / private budget model

needs ~9 GB · 12B

GLM-4.6V · Low-latency vision-language model

needs ~8 GB · 9B

Ministral 3 8B · Small efficient local model

needs ~7 GB · 8B

Granite 4.1 8B · Enterprise/open lightweight assistant

needs ~7 GB · 8B

Qwen3-VL 8B · Small local VLM

needs ~7 GB · 8B

Not enough memory

· modeled need is above available memory

GPT-OSS 20B · Small open-weight reasoning/tool model

needs ~15 GB · 21B

Codestral · Coding model

needs ~15 GB · 22B

Mistral Small 3.2 · Cheap small generalist

needs ~16 GB · 24B

Gemma 4 26B A4B · Efficient multimodal MoE

needs ~18 GB · 26B

Qwen3.6 27B · Strong local generalist/coder

needs ~18 GB · 27B

Gemma 3 27B · Small open model

needs ~18 GB · 27B

Qwen3-Coder 30B · Practical local coding model

needs ~20 GB · 30B

Nemotron 3 Nano 30B · Low-latency MoE reasoning/chat

needs ~20 GB · 30B

Qwen3-VL 30B · Stronger local VLM

needs ~20 GB · 30B

Gemma 4 31B · Open multimodal local generalist

needs ~21 GB · 31B

Qwen3.6 35B A3B · Local multimodal coding/general model

needs ~23 GB · 35B

DeepSeek R1 Distill Llama 70B · Larger local reasoning model

needs ~44 GB · 70B

Llama 3.3 70B · Open-weight generalist

needs ~44 GB · 70B

Qwen3-Coder-Next · Coding-agent specialist

needs ~50 GB · 80B

GPT-OSS 120B · Open-weight GPT-style model

needs ~74 GB · 120B

Nemotron 3 Super 120B · Open / enterprise-tuned generalist

needs ~74 GB · 120B

Devstral 2 · Agentic coding model

needs ~76 GB · 123B

MiniMax M2 · Agentic coding / workflows model

needs ~140 GB · 230B

Llama 4 Maverick · Open-weight / flexible deployment model

needs ~242 GB · 400B

Devices to consider

Memory-only estimates at each device’s highest listed configuration. Speed, software support, and availability are not verified here.

Device	Max memory	Modeled fit up to	From
Raspberry Pi 5 ↗ CPU-only inference — small models, and slow	16 GB	Ministral 3 14B (~10 GB)	$80
Apple Mac mini (M4) ↗ Unified memory; good value entry for local AI	32 GB	Gemma 4 31B (~21 GB)	$599
Apple Mac mini (M4 Pro) ↗ Up to 64 GB unified — mid-size models	64 GB	Qwen3.6 35B A3B (~23 GB)	$1,399
Apple Mac Studio (M4 Max) ↗ Up to 128 GB unified — large models	128 GB	Devstral 2 (~76 GB)	$1,999
NVIDIA DGX Spark ↗ Grace Blackwell, purpose-built for local AI; 128 GB unified	128 GB	Devstral 2 (~76 GB)	$4,699

Build a local setup Compare local vs cloud

Memory needs are modeled at Q4 (4-bit) quantization (roughly total parameters × 0.6 GB, plus overhead): a “will it fit” estimate, not a speed benchmark. Device specs are from vendor pages (updated 2026-07-06). Running a model that barely fits will be slow.