Running Ollama behind a reverse proxy gives you things the raw API doesn’t: a clean domain name instead of an IP and port, HTTPS so connections are encrypted, basic authentication to keep your models private, and the ability to put Ollama on a standard port (80/443) that doesn’t need special firewall rules. If you’re running … Read more
Ollama’s behaviour is almost entirely configurable through environment variables — where it stores models, what address and port it listens on, how long it keeps models in memory, how many parallel requests it handles. The defaults are sensible for a single-user local setup, but as soon as you want to customise anything — move models … Read more
You’ve installed Ollama, you try to run a model, and nothing happens — or you get a cryptic error you’ve never seen before. This is frustrating, but nearly every Ollama startup issue falls into one of a handful of root causes. This guide works through them systematically, from the most common to the more obscure, … Read more
Gemma 3 is Google’s best open-weight model family to date, and it’s a significant step up from Gemma 2. The standout version is the 27B model, which competes with much larger open-source models on reasoning and instruction following. But the whole family is worth knowing — from the 1B model that runs on almost anything … Read more
By default, Ollama only listens on localhost — it accepts connections from your own machine and ignores everything else. That’s fine for solo use, but if you want to query Ollama from another device on your network, run Open WebUI on a different machine, access it from a phone or tablet, or share it with … Read more
Ollama on Windows runs on CPU by default, but if you have an NVIDIA GPU, you’re leaving a lot of performance on the table. With CUDA enabled, a 7B model that generates 4–6 tokens per second on CPU can hit 50–80 tokens per second on a mid-range GPU — the difference between something that feels … Read more
Mistral Small 3 is one of the most interesting models to land in the local LLM space in 2025. At 24 billion parameters, it sits in an underserved spot — larger than the 7B class where most consumer hardware plays, but far more accessible than 70B models that require server-grade RAM. What makes it notable … Read more
WSL2 (Windows Subsystem for Linux 2) gives you a full Linux kernel running inside Windows — and Ollama on WSL2 is a genuinely good setup. You get native Linux performance, GPU passthrough for NVIDIA cards, and the ability to run Ollama alongside your Linux development toolchain without leaving Windows. If you’re a developer who primarily … Read more
Phi-4 is Microsoft’s answer to a deceptively simple question: how capable can a small model get? At 14 billion parameters, it competes with — and often beats — models two to four times its size on reasoning and coding benchmarks. On a Mac with 16GB unified memory or a PC with a decent GPU, Phi-4 … Read more
Llama 3.3 70B is the largest model most people can realistically run on consumer hardware — and it’s genuinely impressive. Meta’s instruction-tuned 70B model sits in a category by itself among open-source models: strong enough to compete with frontier API models on many tasks, but running entirely on your machine. The catch is that you … Read more
