text model · Mistral · Windows
Can I run Devstral Small on 8GB RAM Laptop (CPU/iGPU only)?
No. Devstral Small needs ~15.4 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
Needs ~15.4 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 10.4 GB: Devstral Small needs roughly 15.4 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 Devstral Small is the Apple M4 (24GB) at 24 GB. See Devstral Small on Apple M4 (24GB).
- Q4_K_M needed
- ~15.4 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 24B
- Q4_K_M size
- 13.35 GB
- Q8_0 size
- 23.33 GB
- Context
- 128k
- Ollama tag
- devstral:24b
- 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 Devstral Small on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run Devstral Small?
No. Devstral Small needs ~15.4 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
How much memory does Devstral Small need?
8GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~13.35 GB; with KV cache and runtime overhead, budget ~15.4 GB at a 4k context.
What is the best tool to run Devstral Small 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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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/devstral-small/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.