The term local AI means running language models such as Llama, Qwen or DeepSeek directly on your own computer instead of sending prompts to a hosted service like ChatGPT or Claude. The model files sit on a local drive and the graphics card (GPU) does the computation. There is no network round-trip. There is no account and no recurring fee. The appeal is considerable but the decision is not one-sided. Running models locally brings real costs as well as benefits. What follows is a balanced look at both. It should help you decide whether the approach suits you.
The case for running models locally
Privacy and data control
Privacy is the most frequently cited motivation and the most defensible. When a model runs on local hardware your prompts and documents never leave the machine. Nothing is recorded on a third party's servers. Nothing is kept to train a future model or exposed in a provider data breach. If you handle personal records or client information or proprietary source code or anything under legal or medical confidentiality, this alone can justify the arrangement.
Marginal cost
Once you own a capable GPU, local inference is effectively free at the margin. You can generate an unlimited volume of text with no per-request charges and no subscription. Anyone who leans on AI heavily through a working day for drafting or programming or summarising will find this cost structure compares well with metered cloud APIs. Those charges add up in proportion to use. The trade is a fixed hardware investment in exchange for negligible variable cost.
Availability and permanence
A local model keeps working with no connectivity. It runs on an aircraft or in a dead zone or during a provider outage. It is also immune to the disruptions common to hosted services. It cannot be deprecated or silently altered or rate-limited at an inconvenient moment. The version you download is the version you keep indefinitely. That is a meaningful guarantee for any workflow that depends on consistent behaviour.
Configurability
Local deployment gives you control over the exact model and version in use. You control the parameters that govern how it runs and the data and tools it connects to. Open models also tend to decline far fewer requests than commercial assistants. That matters for legitimate security research and for some kinds of creative writing and for other work that general-purpose safety filters handle poorly.
The case against
A persistent capability gap
The most significant limitation is quality. A model that runs comfortably on consumer hardware is typically 8 to 32 billion parameters and genuinely useful. The frontier models offered by cloud providers are far larger and remain measurably stronger at difficult reasoning and long-document comprehension and specialised knowledge. If your work depends on the highest possible quality even occasionally, a hosted service may serve you better for those tasks.
A hardware prerequisite
The factor that determines what you can run is VRAM, the memory installed on the graphics card. Roughly speaking 8 GB handles small models while 16 to 24 GB opens access to the genuinely capable ones. A suitable GPU is a real upfront expense even though running it afterward costs little. The VRAM requirements guide sets out precisely how much is needed for each model size.
Speed is hardware-dependent
On a capable GPU a local model responds almost instantly. On a weaker card performance can degrade a lot. The same happens when a model exceeds available VRAM and spills into ordinary system memory. In some cases it drops to a word or two per second. Because this depends entirely on the specific hardware it is worth estimating in advance. The WillMyGPURunIt calculator reports the approximate tokens per second a given build would achieve, removing the guesswork.
A modest setup requirement
Tools such as Ollama have reduced installation to a single command in many cases. Even so the process is still more involved than opening a website. Getting the best performance sometimes takes some familiarity with quantization and driver configuration. The barrier is low but it is not zero.
Reaching a decision
Local AI is the right choice if you place a high value on privacy or use AI often enough to benefit from its marginal cost or need offline availability or simply prefer to own your tools. You also need a GPU with adequate VRAM or the willingness to buy one. A hosted service stays preferable if you need the most capable model for every task or use AI only now and then or want to avoid hardware questions entirely. In practice the two are not mutually exclusive. Many people adopt a hybrid approach. They use a local model for routine and confidential work and reserve a cloud model for the most demanding problems.
Whichever path you favour the sensible first step is to find out what your existing hardware can support. Enter your components into the calculator and it returns the specific models the GPU can run today along with their expected speed.