A DEVICETERRA PRODUCT

Technology for emerging-market businesses and institutions.

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FREE · PRIVATE · EVIDENCE-FIRST

Local AI hardware
compatibility calculator

Describe the device. MamiLens will estimate a safe memory budget, shortlist models and recommend a starting engine. It will not invent a speed result.

Private by designYour hardware choices stay in this browser unless you choose to share the result.
FREE BEGINNER COURSE

New to local AI? Learn one idea at a time.

Understand the words in this guide before making a decision. Start from the beginning, or choose one of the 20 available lessons.

1 · DESCRIBE THE DEVICE

Use the real specifications.

If you do not know the GPU, choose “not sure.” MamiLens will make the safer CPU-only assumption.

Advanced calculation optionsQuantization, cache and simultaneous users
2 · CONSERVATIVE RESULT

41 curated profiles fit safely

These models pass the current memory and input rules. Start with the first recommendation and benchmark it.

Installed RAM32 GB
−
System reserve6 GB
=
Model working budget26 GB
RECOMMENDED ENGINE START

LM Studio or Ollama

A CPU-first setup is the safest assumption when dedicated GPU support is unknown.

More CPU cores can help, but memory bandwidth and engine settings still matter.
1
Recommended to test

Qwen 2.5 14B

Qwen 2.5 14B is a curated dense profile for general, writing, coding workloads.

Estimated model file
9 GB
Estimated total RAM
32 GB
Headroom
0 GB
Acceleration
CPU / RAM
Speed
Benchmark required
Installation space estimate
14 GB · not checked
Evidence
Calculated estimate
Open model profile →ollama run qwen2.5:14b
2
Recommended to test

Phi-4 14B

A compact Microsoft model aimed at strong reasoning, mathematics and instruction following.

Estimated model file
9.1 GB
Estimated total RAM
32 GB
Headroom
0 GB
Acceleration
CPU / RAM
Speed
Benchmark required
Installation space estimate
14.1 GB · not checked
Evidence
Calculated estimate
Open model profile →ollama run phi4:14b
3
Recommended to test

Cogito v1 Preview 14B

A text assistant with an optional extended reasoning mode for writing, coding, mathematics and tools. Follow its published system instruction to enable reasoning.

Estimated model file
9 GB
Estimated total RAM
32 GB
Headroom
0 GB
Acceleration
CPU / RAM
Speed
Benchmark required
Installation space estimate
14 GB · not checked
Evidence
Calculated estimate
Open model profile →ollama run cogito:14b
4
Recommended to test

Mistral Nemo 12B

Mistral Nemo 12B is a curated dense profile for general, writing, tools workloads.

Estimated model file
7.1 GB
Estimated total RAM
30 GB
Headroom
2 GB
Acceleration
CPU / RAM
Speed
Benchmark required
Installation space estimate
12.1 GB · not checked
Evidence
Calculated estimate
Open model profile →ollama run mistral-nemo:12b
5
Recommended to test

Gemma 4 12B QAT

Gemma 4 12B QAT is a curated dense profile for general, writing, reasoning workloads.

Estimated model file
7.2 GB
Estimated total RAM
32 GB
Headroom
0 GB
Acceleration
CPU / RAM
Speed
Benchmark required
Installation space estimate
12.2 GB · not checked
Evidence
Calculated estimate
Open model profile →ollama run gemma4:12b-it-qat
6
Recommended to test

Qwen 3.5 9B

Qwen 3.5 9B is a curated dense profile for general, coding, reasoning workloads.

Estimated model file
6.6 GB
Estimated total RAM
31 GB
Headroom
1 GB
Acceleration
CPU / RAM
Speed
Benchmark required
Installation space estimate
11.6 GB · not checked
Evidence
Calculated estimate
Open model profile →ollama run qwen3.5:9b
Important:

The catalogue covers 152 current, workstation, mobile, server and important legacy GPU profiles. An unknown GPU or multi-GPU system is never treated as verified acceleration. Every speed range is a bandwidth-based calculation, not a measured result. Exact model files, context, runtime version, background applications and cooling can still change the result.

HOW TO USE YOUR SHORTLIST

From hardware check to a useful first answer.

1. Enter the exact device

Choose your RAM, operating system and task. If you have a GPU, select its exact model. Count Apple unified memory once.

2. Compare the result

Read the memory estimate, execution path and limits. A model can fit in memory and still respond too slowly for your work.

3. Test one model

Follow the engine setup, use a short context and try the same few work samples. Keep the model only if the answers and waiting time are useful.

How to test the recommendation →
CHOOSE BY YOUR COMPUTER

Need a starting recommendation?

No dedicated GPUSmall model choices and CPU test steps.16 GB system RAMGeneral text, coding and image options.RTX 3060 12 GBFirst model, context and GPU checks.
EXAMPLE HARDWARE PROFILES

Start with a common hardware profile.

These permanent pages use the same conservative rules as the calculator. Choose the closest setup, then enter your exact device above.

8GB RAM, no GPUCan 8GB RAM run local AI?16GB RAM, no GPUWhat local AI can run on 16GB RAM?32GB RAM, no GPUWhich local AI models fit 32GB RAM?16GB RAM, 8GB VRAMWhat local AI runs with 8GB VRAM?32GB RAM, 12GB VRAMWhat local AI runs with 12GB VRAM?Apple Silicon, 16GBCan a 16GB Mac run local AI?Apple Silicon, 32GBWhich local AI models fit a 32GB Mac?8GB RAM for codingWhich coding model can run on 8GB RAM?16GB RAM for imagesWhich local vision model fits 16GB RAM?RTX 3060 12GBWhat LLM can an RTX 3060 12GB run?RTX 4060 8GBWhat LLM can an RTX 4060 run?RTX 4090 24GBWhat LLM can an RTX 4090 run?RX 7900 XTX 24GBCan an RX 7900 XTX run local AI?Intel Arc A770 16GBCan Intel Arc A770 run a local LLM?Apple M1, 16GBWhat local AI can an M1 Mac run?M2 Pro, 32GBWhat local AI can an M2 Pro Mac run?M3 Max, 64GBWhat local AI can an M3 Max Mac run?M4 Pro, 48GBWhat local AI can an M4 Pro Mac run?32GB RAM for codingWhich coding LLM runs on 32GB RAM?32GB private documentsWhat local AI can search private documents on 32GB RAM?64GB Linux team serverWhat local LLM can serve a small team on 64GB RAM?
WHAT THE RESULT MEANS

A safe shortlist, not a speed guarantee.

Architecture-aware memory

The calculator reserves model weights, runtime memory, operating system memory, model-specific KV cache and extra multimodal memory.

Verified GPU path

Reported VRAM is counted only when the exact GPU family and backend support are known. Unknown and multi-GPU systems require validation.

Practical CPU limit

A model that loads can still be too slow. Large CPU-only plans are marked as likely slow instead of being called a strong match.

COMMON QUESTIONS

What can this calculator tell me?

Does it detect my hardware automatically?

You enter your hardware. The calculator does not scan your computer. Use your system settings to confirm RAM and the exact GPU.

Can I run local AI without a GPU?

Yes. Small text models can run on a CPU. The result helps check memory; your own test shows whether the waiting time is acceptable.

Are the results measured benchmarks?

Results are calculated estimates. Hardware support, memory use and speed depend on the exact model, engine and settings. Read the evidence and limits.

Is 16 GB RAM the same as 16 GB VRAM?

No. System RAM is shared by the operating system and apps. Dedicated VRAM belongs to a graphics card. Apple unified memory is shared by the CPU and GPU.