text model · phi · Windows
Can I run Phi-4-mini 3.8B on 32GB RAM Laptop (CPU/iGPU only)?
Yes. Phi-4-mini 3.8B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~3.8 GB of ~28 GB usable).
Runs at Q4_K_M using ~3.8 GB of ~28 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. 32GB RAM Laptop (CPU/iGPU only) leaves ~24.2 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~3.8 GB
- Usable on device
- ~28 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~28 W
- Electricity / 1M tokens
- ~$0.06
- Pays for itself after
- ~2,500M tok
At ~$0.15/kWh and the estimated ~18 tok/s, a million generated tokens costs about $0.06 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,100 32GB RAM Laptop (CPU/iGPU only) pays for itself after roughly 2,500 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run phi4-mini:3.8b llama-cli -hf bartowski/microsoft_Phi-4-mini-instruct-GGUF:Q4_K_M lms get bartowski/microsoft_Phi-4-mini-instruct-GGUF - Parameters
- 3.8B
- Q4_K_M size
- 2.49 GB
- Q8_0 size
- 4.08 GB
- Context
- 128k
- Ollama tag
- phi4-mini:3.8b
- Memory
- 32 GB ram
- Usable for weights
- ~28 GB
- Power draw
- ~28 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Phi-4-mini 3.8B on other hardware
FAQ
Can 32GB RAM Laptop (CPU/iGPU only) run Phi-4-mini 3.8B?
Yes. Phi-4-mini 3.8B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~3.8 GB of ~28 GB usable).
How much memory does Phi-4-mini 3.8B need?
32GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~2.49 GB; with KV cache and runtime overhead, budget ~3.8 GB at a 4k context.
What is the best tool to run Phi-4-mini 3.8B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/phi-4-mini-3.8b/laptop-32gb) Sources
- amazon.com
- amd.com
- en.wikipedia.org
- huggingface.co/bartowski/Meta-Llama-3.1-70B-Instruct-GGUF
- huggingface.co/bartowski/microsoft_Phi-4-mini-instruct-GGUF
- lmstudio.ai
- notebookcheck.net
- ollama.com
- ollama.com/library/llama3.1:70b
- ollama.com/library/mixtral:8x7b
- ollama.com/library/phi4-mini
- ollama.com/library/phi4-mini/tags
- 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.