About this model
Llama 3.1 8B is Meta's widely supported general-purpose model and the most common starting point for local AI. Its ecosystem support is the broadest of any open model: every major runtime ships it, quantized variants exist for every memory size, and fine-tuned derivatives cover most use cases.
What it is good for
- General chat and drafting
- Local RAG pipelines with tool/function calling
- As a base for domain-specific fine-tunes
Limitations
- Shorter context than newer models in some quantized builds
- Weaker at code than specialized coder models of similar size
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 6 GB card | Recommended class |
|---|---|---|---|
| Q4 (GGUF) | ~4.7 GB | Good | 6 GB+ |
| Q8 (GGUF) | ~8.5 GB | Tight | 9 GB+ |
| FP16 | ~16.1 GB | Tight | 17 GB+ |
GPUs that can run it
FAQ
Can Llama 3.1 8B run on 6 GB VRAM?
Yes — the Q4 GGUF build needs about 4.5–5 GB including modest context, fitting a 6 GB card with a little headroom. An 8 GB card gives more comfortable context sizes.
Can it run CPU-only?
Yes. With 16 GB of system RAM, a Q4 8B model runs at roughly 5–12 tok/s depending on CPU memory bandwidth. Usable for patience-tolerant chat, slow for long outputs.
Which quantization should I use?
Q4_K_M is the standard choice — near-indistinguishable from higher-bit builds for most tasks while halving memory. Use Q5/Q6 when you have VRAM to spare for extra fidelity.
Which GPU is best for it?
Any 8 GB class card runs it comfortably; a 12 GB card lets you run Q8 or long contexts. The model is small enough that runtime convenience (Ollama, LM Studio) matters more than GPU tier.