TechRounder Local AI Tool

CanItRun: Pick Your Hardware and See Which Local Models Actually Fit

Stop translating vague VRAM charts. Choose the hardware you own in plain language and CanItRun will rank popular local models, tell you whether they fit, and give you the exact ollama pull command to run.

Hardware-first matching Traffic-light verdicts per model Exact copyable Ollama commands

Designed for real devices

Mac mini, RTX gaming PC, CPU-only server, Raspberry Pi, or “I don’t know yet” all map to a useful flow.

Clear verdicts

Each model is labeled as runs great, runs with trade-offs, or does not fit cleanly on the selected hardware.

Shareable setup card

Download a forum-friendly setup summary with your top recommendations and commands.

Why Most Local-LLM Calculators Feel Harder Than They Should

They assume you know the jargon

Quantization, context overhead, and VRAM math are useful, but many users just want to know whether their box can run something practical.

They stop at a raw number

Knowing a model needs 8.5 GB does not help much unless the tool also tells you whether the experience will be smooth, tight, or miserable.

They do not bridge to action

CanItRun finishes the loop by giving you the exact command to try next, not just a theoretical fit score.

1. Choose the Hardware Path

Pick the closest real-world device category, then tell CanItRun your intended workload.

Hardware Category

Mac (Apple Silicon) Mac mini, MacBook, or Studio with unified memory. Gaming PC / discrete GPU NVIDIA or AMD GPU plus system RAM. NAS / CPU-only server System RAM only, no useful GPU acceleration. Raspberry Pi 5 Pi-class edge setup for tiny models and experimentation. I do not know my specs Show me how to check before guessing.

Unified Memory

GPU

System RAM

Pi Memory

How to check fast

Mac: open About This Mac and check chip + memory.
Windows PC: open Task Manager → Performance and check GPU model + memory.
Linux server: run free -h and lspci | grep VGA.
Raspberry Pi: run cat /proc/meminfo.

Primary Use Case

Context Window Assumption

Model Verdicts

Best-ranked models for the selected hardware appear first, along with an exact command to try.

Best Match for This Hardware

Choose a device category and use case to see the strongest local model recommendations.

0 Fit

Waiting for hardware

No model ranked yet

The top recommendation will appear here with a plain-language explanation.

Available Memory 0 GB

Expected Speed Tier Unknown

Best Command -

What changes if you upgrade

Select hardware to see the next useful upgrade path.

How CanItRun Scores the Fit

Memory first

Available memory is compared against the model’s most common Q4 requirement plus a context-window overhead buffer.

Shared memory matters

Apple Silicon and CPU-only boxes use unified or system RAM, but a reserve buffer is held back for the OS.

Offload is treated honestly

If a GPU cannot hold the full model but the PC can offload into RAM, CanItRun marks it as workable rather than pretending it is fast.

Planning a bigger private AI stack?

Pair model fit with the rest of your homelab design.

CanItRun answers the model question. StackMatch helps size the full self-hosted platform around storage, backups, and the other services you want to run beside AI.

Open StackMatch AI Command Studio