text model · DeepSeek-R1 · Windows
Can I run DeepSeek-R1-0528 on 8GB RAM Laptop (CPU/iGPU only)?
No. DeepSeek-R1-0528 needs ~384.1 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
Needs ~384.1 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 379.1 GB: DeepSeek-R1-0528 needs roughly 384.1 GB at Q4_K_M and 8GB RAM Laptop (CPU/iGPU only) leaves only about 5 GB usable for a model. No single tracked device has enough memory; DeepSeek-R1-0528 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~384.1 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 671B (MoE, 37B active)
- Q4_K_M size
- 377.13 GB
- Q8_0 size
- 664.3 GB
- Context
- 160k
- Ollama tag
- deepseek-r1:671b
- Memory
- 8 GB ram
- Usable for weights
- ~5 GB
- Power draw
- ~15 W
- Best runtime
- Ollama (llama.cpp backend)
What you can run instead
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run DeepSeek-R1-0528?
No. DeepSeek-R1-0528 needs ~384.1 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
How much memory does DeepSeek-R1-0528 need?
8GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~377.13 GB; with KV cache and runtime overhead, budget ~384.1 GB at a 4k context. It is a Mixture-of-Experts model (671B total / 37B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run DeepSeek-R1-0528 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/deepseek-r1-0528/laptop-8gb) Sources
- aider.chat
- amazon.com
- en.wikipedia.org
- huggingface.co/bartowski
- huggingface.co/deepseek-ai/DeepSeek-R1-0528
- huggingface.co/deepseek-ai/DeepSeek-R1-0528/blob
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/BF16
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q4_K_M
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q8_0
- lmstudio.ai
- notebookcheck.net
- ollama.com
- ollama.com/library/deepseek-r1:671b
- ollama.com/library/llama3.2:1b
- ollama.com/library/phi3:mini
- openrouter.ai
- store.acer.com
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.