About this model
Llama 3.2 3B is a compact general model designed for low-memory machines. It fits comfortably even on 4 GB GPUs or CPU-only systems, making it the go-to choice for laptops without discrete graphics and for always-on assistants.
What it is good for
- Chat and Q&A on low-memory machines
- Summarization and simple extraction tasks
- Fast interactive assistants where speed matters more than depth
Limitations
- Noticeably weaker reasoning and knowledge than 7B+ models
- Not suitable for complex code generation
Memory by quantization (estimates)
Weights at a given bit-width plus estimated runtime and context overhead. These are estimates — see the methodology.
| Quantization | Approx. memory | Fits a 3 GB card | Recommended class |
|---|---|---|---|
| Q4 (GGUF) | ~2 GB | Good | 3 GB+ |
| Q8 (GGUF) | ~3.2 GB | Tight | 4 GB+ |
| FP16 | ~6.4 GB | Tight | 7 GB+ |
GPUs that can run it
FAQ
Can Llama 3.2 3B run without a GPU?
Yes. A Q4 build needs about 2 GB of RAM and runs interactively on modern CPUs — one of the best CPU-only options for casual use.
How good is it compared to an 8B model?
It handles everyday chat and summarization well, but for nuanced reasoning, code or long documents, 7B–8B models are clearly stronger. Choose 3B when memory or speed is the constraint.
What speed can I expect?
On any modern GPU it is very fast (often 60–100+ tok/s at Q4); CPU-only typically lands around 10–20 tok/s depending on the machine.
Does the long context work on small hardware?
Context memory still applies — at 8K+ tokens the KV cache becomes a meaningful share of memory even for a 3B model, so keep context as small as your task allows.