A DEVICETERRA PRODUCT

Technology for emerging-market businesses and institutions.

FREE · PRIVATE · EVIDENCE-FIRST

Can this computer
run local AI?

Describe the device. LocalLens 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.

1 · DESCRIBE THE DEVICE

Use the real specifications.

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

Operating systemInstalled system RAMCPU classGraphics hardwareDedicated GPU memoryMain jobWorking context

2 · CONSERVATIVE RESULT

19 curated profiles fit safely

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

Installed RAM16 GB

−

System reserve4 GB

Model working budget12 GB

RECOMMENDED ENGINE START

LM Studio or Ollama

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

CPU use is practical for small models, but speed must be tested.

Strong starting point

Gemma 3 1B

The text-only compact Gemma 3 variant for lightweight language tasks.

Estimated total RAM: 7 GB
Headroom: 9 GB
Acceleration: CPU-only assumption

Open model profile →ollama run gemma3:1b

Strong starting point

Llama 3.2 1B

Mature small models for summarization, rewriting and personal information tasks.

Estimated total RAM: 8 GB
Headroom: 8 GB
Acceleration: CPU-only assumption

Open model profile →ollama run llama3.2:1b

Strong starting point

Gemma 3n E2B

Designed for efficient execution on everyday and edge devices.

Estimated total RAM: 8 GB
Headroom: 8 GB
Acceleration: CPU-only assumption

Open model profile →ollama run gemma3n:e2b

Strong starting point

Llama 3.2 3B

Mature small models for summarization, rewriting and personal information tasks.

Estimated total RAM: 8 GB
Headroom: 8 GB
Acceleration: CPU-only assumption

Open model profile →ollama run llama3.2:3b

Strong starting point

Phi-4 Mini 3.8B

Compact multilingual reasoning and mathematics with function calling.

Estimated total RAM: 9 GB
Headroom: 7 GB
Acceleration: CPU-only assumption

Open model profile →ollama run phi4-mini

Strong starting point

Qwen 3 4B

A widely used hybrid reasoning family with tool support and multilingual strength.

Estimated total RAM: 9 GB
Headroom: 7 GB
Acceleration: CPU-only assumption

Open model profile →ollama run qwen3:4b

Important:

The figures are planning estimates, not benchmark results. Exact model files, context, runtime version, background applications and memory bandwidth can change the result.

Read the RAM and VRAM guide →

POPULAR CHECKED DEVICES

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?

WHAT THE RESULT MEANS

A safe shortlist, not a speed guarantee.

Memory fit

The calculator reserves space for the operating system, model weights, runtime overhead and the selected context range.

GPU fit

Full GPU acceleration is shown only when reported VRAM can hold the estimated workload. Partial offload is labelled separately.

Real performance

CPU generation and workload affect speed. Benchmark the selected model on the actual device before relying on it.