text model · Qwen3.6 · Windows
Can I run Qwen3.6 35B-A3B on 32GB RAM Laptop (CPU/iGPU only)?
Yes. Qwen3.6 35B-A3B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~24.5 GB of ~28 GB usable).
Runs at Q4_K_M using ~24.5 GB of ~28 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. 32GB RAM Laptop (CPU/iGPU only) leaves ~3.5 GB of headroom.
- Q4_K_M needed
- ~24.5 GB
- Usable on device
- ~28 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run qwen3.6:35b-a3b llama-cli -hf bartowski/Qwen_Qwen3.6-35B-A3B-GGUF:Q4_K_M lms get bartowski/Qwen_Qwen3.6-35B-A3B-GGUF - Parameters
- 36B (MoE, 3B active)
- Q4_K_M size
- 22.29 GB
- Q8_0 size
- 36.9 GB
- Context
- 256k
- Ollama tag
- qwen3.6:35b-a3b
- Memory
- 32 GB ram
- Usable for weights
- ~28 GB
- Power draw
- ~28 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Qwen3.6 35B-A3B on other hardware
FAQ
Can 32GB RAM Laptop (CPU/iGPU only) run Qwen3.6 35B-A3B?
Yes. Qwen3.6 35B-A3B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~24.5 GB of ~28 GB usable).
How much memory does Qwen3.6 35B-A3B need?
32GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~22.29 GB; with KV cache and runtime overhead, budget ~24.5 GB at a 4k context. It is a Mixture-of-Experts model (36B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Qwen3.6 35B-A3B 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/qwen3.6-35b-a3b/laptop-32gb) Sources
- amazon.com
- amd.com
- en.wikipedia.org
- huggingface.co/bartowski/Meta-Llama-3.1-70B-Instruct-GGUF
- huggingface.co/bartowski/Qwen_Qwen3.6-35B-A3B-GGUF
- huggingface.co/Qwen
- huggingface.co/unsloth
- lmstudio.ai
- notebookcheck.net
- ollama.com
- ollama.com/library/llama3.1:70b
- ollama.com/library/mixtral:8x7b
- ollama.com/library/qwen3.6
- walmart.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.