Explainer
SLM vs LLM: small and large language models, compared
Both are the same kind of thing — a neural network trained to predict text. The difference is scale, and scale decides everything practical: what hardware you need, what a reply costs, how long it takes, and whether your prompt ever leaves the room. We care about this because DAP AI is not served from a rented cloud; our models run on two NVIDIA Jetson Orin Nano edge boards we own.
The short answer
- An SLM (small language model) is small enough to load onto one device you own. You trade some breadth of knowledge and long-form reasoning for speed, near-zero marginal cost, offline capability and real privacy.
- An LLM (large language model) is too big for that. It lives on datacenter GPUs and you rent access per token. You get broader knowledge and stronger multi-step reasoning, and your prompts go to somebody else's machines.
- Privacy is about where a model runs, not how big it is. A small model behind someone else's API is no more private than a large one.
Side by side
| Small language model (SLM) | Large language model (LLM) | |
|---|---|---|
| Typical size | Roughly 0.5B–8B parameters; a few GB once quantised | Tens to hundreds of billions of parameters |
| Where it runs | A single consumer device: laptop, mini PC, or an edge board like a Jetson Orin Nano | Clusters of datacenter GPUs, reached over an API |
| Latency | Fast per token on modest hardware, but throughput is limited — one request at a time on a single board | Fast and massively parallel, because the capacity is rented and shared |
| Running cost | Electricity plus the one-off hardware you already bought | Per-token billing that scales with every message |
| Privacy | Prompts can stay on hardware you physically control | Prompts leave your network and land with the provider |
| Breadth of knowledge | Narrower; strongest when focused on a task or given context | Broader recall across niche topics and languages |
| Long, multi-step reasoning | Weaker — keep tasks short and concrete | Stronger, and the usual reason to reach for one |
| Offline use | Works with no internet once the weights are local | Needs a live connection to the provider |
What running an SLM on real hardware actually feels like
This is the part benchmarks leave out. A Jetson Orin Nano is a small board with shared memory, not a rack of GPUs, and that shapes the product in three visible ways.
Throughput, not speed, is the limit. Tokens come out at a perfectly usable rate, but the board serves one request at a time. So DAP AI queues messages in fairness lanes by plan rather than pretending capacity is infinite — you see your place in the queue instead of a silent timeout.
Memory sets the context budget. Weights and conversation history compete for the same few gigabytes, so history depth is tuned per plan rather than dumping an entire chat into every request.
Nothing is forwarded. Your messages are answered by our boards; they are not relayed to OpenAI, Anthropic or Google. That is the payoff for the two constraints above, and it is not something a rented API can offer.
Which should you use?
Pick a small model for everyday questions, drafting and rewriting, summarising text you paste in, explaining concepts, routine coding help, and anything you would rather not send to a third party.
Pick a large model for long multi-step reasoning, obscure factual recall, very long documents, and work across many languages at once.
In practice most people want both, and the honest routing rule is simple: default to the small model, and escalate only when a task visibly needs more.
Try a small language model now
DAP AI 4 Lite is a small model served from our own hardware, free to use, and your first message needs no account.