text model · Phi-4 · Windows
Can I run Phi-4-mini-reasoning on 8GB RAM Laptop (CPU/iGPU only)?
Yes. Phi-4-mini-reasoning runs on 8GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~3.6 GB of ~5 GB usable).
Runs at Q4_K_M using ~3.6 GB of ~5 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. 8GB RAM Laptop (CPU/iGPU only) leaves ~1.4 GB of headroom.
- Q4_K_M needed
- ~3.6 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~15 W
- Electricity / 1M tokens
- ~$0.03
- Pays for itself after
- ~745M tok
At ~$0.15/kWh and the estimated ~19 tok/s, a million generated tokens costs about $0.03 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$350 8GB RAM Laptop (CPU/iGPU only) pays for itself after roughly 745 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-reasoning:latest llama-cli -hf unsloth/Phi-4-mini-reasoning-GGUF:Q4_K_M lms get unsloth/Phi-4-mini-reasoning-GGUF - Parameters
- 3.8B
- Q4_K_M size
- 2.32 GB
- Q8_0 size
- 3.8 GB
- Context
- 128k
- Ollama tag
- phi4-mini-reasoning:latest
- Memory
- 8 GB ram
- Usable for weights
- ~5 GB
- Power draw
- ~15 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Phi-4-mini-reasoning on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run Phi-4-mini-reasoning?
Yes. Phi-4-mini-reasoning runs on 8GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~3.6 GB of ~5 GB usable).
How much memory does Phi-4-mini-reasoning need?
8GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~2.32 GB; with KV cache and runtime overhead, budget ~3.6 GB at a 4k context.
What is the best tool to run Phi-4-mini-reasoning 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-reasoning/laptop-8gb) Sources
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.