text model · llama · Windows
Can I run Llama 3.2 1B on 8GB RAM Laptop (CPU/iGPU only)?
Yes. Llama 3.2 1B runs on 8GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.8 GB of ~5 GB usable).
Runs at Q4_K_M using ~1.8 GB of ~5 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1.8 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run llama3.2:1b llama-cli -hf unsloth/Llama-3.2-1B-Instruct-GGUF:Q4_K_M lms get unsloth/Llama-3.2-1B-Instruct-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 1B
- Q4_K_M size
- 0.81 GB
- Q8_0 size
- 1.32 GB
- Context
- 128k
- Ollama tag
- llama3.2:1b
- Memory
- 8 GB ram
- Usable for weights
- ~5 GB
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Llama 3.2 1B on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run Llama 3.2 1B?
Yes. Llama 3.2 1B runs on 8GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.8 GB of ~5 GB usable).
How much memory does Llama 3.2 1B need?
8GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~0.81 GB; with KV cache and runtime overhead, budget ~1.8 GB at a 4k context.
What is the best tool to run Llama 3.2 1B 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.