text model · llama · Windows
Can I run Llama 3.1 8B on 16GB RAM Laptop (CPU/iGPU only)?
Yes. Llama 3.1 8B runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~6.4 GB of ~12 GB usable).
Runs at Q4_K_M using ~6.4 GB of ~12 GB usable. You have room for Q8_0 for higher quality.
- Q4_K_M needed
- ~6.4 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run llama3.1:8b llama-cli -hf bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M lms get bartowski/Meta-Llama-3.1-8B-Instruct-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 8B
- Q4_K_M size
- 4.92 GB
- Q8_0 size
- 8.54 GB
- Context
- 128k
- Ollama tag
- llama3.1:8b
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Llama 3.1 8B on other hardware
FAQ
Can 16GB RAM Laptop (CPU/iGPU only) run Llama 3.1 8B?
Yes. Llama 3.1 8B runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~6.4 GB of ~12 GB usable).
How much memory does Llama 3.1 8B need?
16GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~4.92 GB; with KV cache and runtime overhead, budget ~6.4 GB at a 4k context.
What is the best tool to run Llama 3.1 8B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Sources
Memory figures are estimates. See methodology.