Local vs Cloud AI - Privacy vs Power

The ceiling: bandwidth ÷ model size

Producing a single token requires reading every weight in the model out of memory. So however fast your processor is, it cannot generate faster than memory can feed it. That one division explains the entire local-versus-cloud speed gap and most of the differences between GPUs:

• • Dual-channel system RAM moves on the order of 80-90 GB/s — this is what CPU-only inference is limited to.
• • A discrete GPU moves several hundred GB/s, and a 24GB enthusiast card is close to 1 TB/s. Check the published figure on your card's specification page.
• • Apple Silicon is the interesting middle case: one pool of unified memory shared by CPU and GPU, at bandwidths well above ordinary system RAM.

Treat the result as an upper bound you will not reach. Attention over a growing context, sampling and framework overhead all cost time the formula ignores — but it reliably tells you which side of "fast enough" a given model and machine will land on.

Your actual number, in one command

After the response, Ollama prints an eval rate line in tokens per second. That is a measurement of your machine with your model, and it beats anyone else's benchmark for predicting how the thing will feel to use. Run it on two model sizes and the bandwidth arithmetic above stops being abstract.

And on quality, honestly

Speed is measurable; quality is not, at least not by a number someone hands you. What can be said plainly is that the largest hosted models remain ahead on hard multi-step reasoning, long-context work and breadth of knowledge, and that the gap has narrowed a great deal for the everyday work most people actually do — summarizing, reformatting, classifying, drafting, explaining code. The useful test is not a leaderboard. It is running your own five most common prompts through a local model and seeing whether the output is good enough for the job you had in mind.

Is local AI really more private than cloud AI?

Structurally, yes — and that is the important word. With a local model the inference happens on your machine, so your prompt is never transmitted anywhere and there is no server-side log of it to subpoena, breach or repurpose. Cloud AI processes your text on someone else's infrastructure under whatever retention and training policy their terms currently specify. That does not make local AI magically secure: the machine itself still needs disk encryption, patches and sensible access control, and a local model will happily write your confidential data into a file that later gets synced to a cloud drive. Local removes one specific, large risk. It does not remove the rest.

What hardware do I actually need to run a model locally?

Two numbers decide it. First, whether the model fits: at the Q4_K_M quantization most tools ship by default, budget roughly 0.6GB of memory per billion parameters, plus one to two gigabytes for context. That is about 4.2GB for a 7B model, 8.4GB for a 14B and 19GB for a 32B. Second, how fast it can possibly run: generating a token requires reading the entire model out of memory, so the ceiling is memory bandwidth divided by model size. Check the bandwidth figure on your card's specification page and do the division. Apple Silicon machines are unusual here because CPU and GPU share one pool of high-bandwidth memory, which is why a Mac with plenty of unified memory can run models that would need a much more expensive discrete GPU.

Which is actually cheaper over time, local or cloud?

It depends on three numbers you can look up in five minutes: your subscription cost, your hardware cost, and your electricity. Cloud is a fixed monthly fee. Local is a one-time hardware cost plus power, where monthly kWh = average watts x 730 / 1000, multiplied by the rate on your bill. Break-even in months is hardware cost divided by the monthly saving. If you already own a capable machine, hardware cost is zero and local wins immediately. If you would be buying a GPU specifically for this, the break-even is often longer than people expect, and for occasional use cloud stays cheaper — in which case you are buying privacy and unlimited usage rather than savings.

How do local models compare to cloud models on quality?

The largest hosted models are still ahead on hard multi-step reasoning, long-context work and breadth of general knowledge, and it would be dishonest to claim otherwise. The gap has narrowed considerably for the everyday tasks that make up most usage — summarizing, reformatting, classifying, drafting, explaining code. The honest framing is that model size is a real constraint and a 7B model running on your laptop is not a frontier model, but that most of what people ask an assistant to do does not require a frontier model. Test on your own tasks; that is the only comparison that predicts your experience.

Ollama or LM Studio for a beginner?

LM Studio if you want a graphical interface: you browse models, click download, click load, and start chatting, with memory usage shown on screen as you go. Ollama if you are comfortable in a terminal and want something scriptable — it installs as a background service and exposes an HTTP API on localhost that other tools can call, which is what makes it the usual choice for automation and for wiring a model into an editor. Many people end up with both installed. Neither requires an account, and both run entirely offline once the model is downloaded.

Can I run several models at once?

Yes, subject to memory. Use the same 0.6GB-per-billion-parameters estimate for each model and add them up: two 7B models are roughly 8.4GB before context, so they will not both fit comfortably on an 8GB card. Ollama loads models on demand and unloads them after an idle timeout, which handles most of this automatically; if you are switching between models constantly, raise the keep-alive setting so you are not paying the load time repeatedly. When the total exceeds your VRAM the runtime spills into system RAM, which does not crash but does slow generation dramatically — that sudden slowdown is usually what has happened.

What makes local AI attractive to organizations?

Mostly data governance rather than cost. Inference on infrastructure you control means confidential material never crosses an organizational boundary, which removes an entire category of vendor-assessment and data-residency questions before they are asked, and makes air-gapped deployment possible for facilities that require it. Costs are also predictable — fixed hardware rather than a bill that scales with adoption. None of this is automatic compliance with any specific regime; local deployment removes one common obstacle to it and leaves the rest of the work in place.

What does a sensible hybrid setup look like?

Route by sensitivity and by difficulty, not by preference. Anything involving confidential material goes local by default, because that decision is about risk rather than quality. Everything high-volume and routine goes local too, since local has no per-use cost — code completion, drafting, quick questions, bulk reformatting. The remainder, meaning genuinely hard reasoning or work that needs the newest capabilities, goes to a cloud model. Most people land here naturally: local as the default tool, one cloud subscription kept for the jobs that earn it.