text model · Llama · Windows
Can I run Llama 3.3 70B on 8GB RAM Laptop (CPU/iGPU only)?
No. Llama 3.3 70B needs ~45.3 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
Needs ~45.3 GB even at Q4_K_M, but only ~5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 40.3 GB: Llama 3.3 70B needs roughly 45.3 GB at Q4_K_M and 8GB RAM Laptop (CPU/iGPU only) leaves only about 5 GB usable for a model. The lightest tracked hardware that runs Llama 3.3 70B is the Apple M4 Max (64GB) at 64 GB. See Llama 3.3 70B on Apple M4 Max (64GB).
- Q4_K_M needed
- ~45.3 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 70B
- Q4_K_M size
- 42.52 GB
- Q8_0 size
- 74.98 GB
- Context
- 128k
- Ollama tag
- llama3.3:70b
- Memory
- 8 GB ram
- Usable for weights
- ~5 GB
- Power draw
- ~15 W
- Best runtime
- Ollama (llama.cpp backend)
What you can run instead
Run Llama 3.3 70B on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run Llama 3.3 70B?
No. Llama 3.3 70B needs ~45.3 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
How much memory does Llama 3.3 70B need?
8GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~42.52 GB; with KV cache and runtime overhead, budget ~45.3 GB at a 4k context.
What is the best tool to run Llama 3.3 70B 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.
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[](https://localmodel.run/can-i-run/llama-3.3-70b/laptop-8gb) Sources
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amazon.com · 1 source
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en.wikipedia.org · 1 source
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gorilla.cs.berkeley.edu · 1 source
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huggingface.co · 2 sources
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lmarena.ai · 1 source
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lmstudio.ai · 1 source
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notebookcheck.net · 1 source
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store.acer.com · 1 source
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.