Syed Sultan Shah

Why I built this

I have spent thirteen years in IT — infrastructure, web platforms, and for the last several years, running open-weight language models on hardware I actually own. The thing that pushed me into building All Local AI was mundane: I kept downloading models that would not fit. A 30 GB file, an evening of bandwidth, and then an out-of-memory error the moment context was allocated. Every listing told me the parameter count. None of them told me the thing I needed to know.

So the site starts from the opposite end. Before anything else, it answers: does this fit the card in your machine, at the quantization you intend to use, with the context length you actually need?

How I work with local models

I run models locally for real work, not as a hobby demo: drafting and editing, code review, summarising documentation, and batch processing where sending data to a third-party API would be the wrong call. That means I care about the unglamorous parts — how a runner behaves after eight hours, what happens to throughput when the KV cache grows, whether a quantization that benchmarks well actually holds up on long inputs.

Most of my testing sits in the consumer range, from 8 GB cards up to 24 GB, because that is what most people reading this own. I would rather be precise about the hardware people have than impressive about hardware they do not.

What I focus on

• VRAM and fit maths — parameter count, bytes per parameter by quantization, KV cache growth, and the headroom your desktop environment quietly consumes.
• Inference runners — llama.cpp, Ollama, vLLM, LM Studio, and the tradeoffs between throughput, ease of setup, and format support.
• Hardware value — where bandwidth matters more than VRAM, when a used card is a bargain, and when it is a trap.
• Practical privacy — the workloads that genuinely belong on your own machine rather than an API.

Where I am

I am based in Pakistan and work with a mix of local and international teams. That location shapes the site more than you would think: unreliable bandwidth makes a 40 GB download a real cost, and import pricing makes GPU choices consequential. Both are good reasons to check whether something fits before you commit to it.

Corrections

If a figure on this site is wrong, tell me. I would rather fix it today than defend it. Write to info@alllocalaimodels.com or use the contact form, and include the page and a source. The editorial policy explains how research, review, and AI assistance work here.