Can I run Llama-3.1-8B-Instruct locally?
meta-llama/Llama-3.1-8B-InstructLlama-3.1-8B-Instruct is a llm (text generation) model with about 8.0B parameters. The practical minimum to run it is roughly 5.0 GB of GPU or system memory, and a GPU with ≥ 7 GB VRAM runs it comfortably. Here's exactly what it needs and how to run it.
Check YOUR exact machineLlama-3.1-8B-Instruct memory & VRAM requirements
How much memory Llama-3.1-8B-Instruct needs at each quantization level (weights plus ~20% runtime overhead). Lower-bit quantization dramatically reduces VRAM at a small quality cost.
| Precision | Memory needed | Notes |
|---|---|---|
| Full precision (FP16/BF16) | 20 GB | Full quality |
| 8-bit (INT8 / Q8) | 11 GB | Smaller, slight quality loss |
| 4-bit (Q4_K_M / INT4) | 6.3 GB | Best size/quality trade-off |
| 3-bit (Q3_K) | 5.0 GB | Smaller, slight quality loss |
Will Llama-3.1-8B-Instruct run on your GPU?
Verdicts for common setups — from CPU-only laptops to an RTX 4090 and data-center GPUs. Click a GPU to see everything it can run.
| Your setup | Verdict | Needs | Est. speed | Best way to run |
|---|---|---|---|---|
| No GPU (16 GB RAM) | Workarounds | 11 GB | ~5.2 tok/s | Run on CPU (RAM) with Q8 |
| RTX 4060 (8 GB) | Runs | 6.3 GB | ~40 tok/s | Run on your GPU with Q4 |
| RTX 3060 (12 GB) | Runs | 11 GB | ~30 tok/s | Run on your GPU with Q8 |
| RTX 4070 Super (12 GB) | Runs | 11 GB | ~40 tok/s | Run on your GPU with Q8 |
| RTX 4080 Super (16 GB) | Runs | 11 GB | ~57 tok/s | Run on your GPU with Q8 |
| RTX 4090 (24 GB) | Runs | 20 GB | ~40 tok/s | Run on your GPU with FP16 |
| RTX 3090 (24 GB) | Runs | 20 GB | ~38 tok/s | Run on your GPU with FP16 |
| Apple M-series (18 GB unified) | Runs | 11 GB | ~13 tok/s | Run on your Apple Silicon (unified memory) with Q8 |
| Apple M Max (64 GB unified) | Runs | 20 GB | ~17 tok/s | Run on your Apple Silicon (unified memory) with FP16 |
| A100 (80 GB) | Runs | 20 GB | ~75 tok/s | Run on your GPU with FP16 |
How to run Llama-3.1-8B-Instruct locally
Recommended method: Run on your GPU with FP16 using vLLM or Transformers.
# Fast production serving on http://localhost:8000/v1
vllm serve meta-llama/Llama-3.1-8B-Instructfrom transformers import pipeline
pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-8B-Instruct", device_map="auto")
print(pipe("Hello", max_new_tokens=50))Llama-3.1-8B-Instruct — frequently asked questions
How much VRAM does Llama-3.1-8B-Instruct need?
Llama-3.1-8B-Instruct needs roughly 6.3 GB of VRAM at 4-bit (Q4) quantization and about 20 GB at full FP16 precision, including runtime overhead. Lower-bit quantization trades a little quality for a lot less memory.
Can I run Llama-3.1-8B-Instruct on CPU without a GPU?
Yes — Llama-3.1-8B-Instruct can run on CPU using system RAM (best with a quantized GGUF build via llama.cpp or Ollama), but generation will be noticeably slower than on a GPU.
What's the best way to run Llama-3.1-8B-Instruct locally?
The easiest path is usually vLLM or Transformers. Run on your GPU with FP16. This page's "How to run it" section has copy-paste commands.
What GPU do I need to run Llama-3.1-8B-Instruct?
A GPU with at least 7 GB of VRAM runs Llama-3.1-8B-Instruct comfortably at 4-bit quantization, or about 21 GB for full FP16 precision. On Apple Silicon, unified memory of that size works too.