text model · Mistral · Windows
Can I run Magistral Small on Nvidia GeForce RTX 3060 (12GB)?
No. Magistral Small needs ~16 GB even at Q4_K_M, but Nvidia GeForce RTX 3060 (12GB) only has ~11 GB usable.
Needs ~16 GB even at Q4_K_M, but only ~11 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 5 GB: Magistral Small needs roughly 16 GB at Q4_K_M and Nvidia GeForce RTX 3060 (12GB) leaves only about 11 GB usable for a model. The lightest tracked hardware that runs Magistral Small is the Apple M4 (24GB) at 24 GB. See Magistral Small on Apple M4 (24GB).
Too big for Nvidia GeForce RTX 3060 (12GB)'s VRAM, but you could offload some layers to system RAM and still run it at roughly ~7 tok/s, far slower than a model that fits. How this is estimated.
- Q4_K_M needed
- ~16 GB
- Usable on device
- ~11 GB
- Device memory
- 12 GB
Which quant fits
- Parameters
- 24B
- Q4_K_M size
- 14 GB
- Q8_0 size
- 25 GB
- Context
- 40k
- Ollama tag
- magistral:24b
- Memory
- 12 GB vram
- Usable for weights
- ~11 GB
- Power draw
- ~170 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
Run Magistral Small on other hardware
FAQ
Can Nvidia GeForce RTX 3060 (12GB) run Magistral Small?
No. Magistral Small needs ~16 GB even at Q4_K_M, but Nvidia GeForce RTX 3060 (12GB) only has ~11 GB usable.
How much memory does Magistral Small need?
Nvidia GeForce RTX 3060 (12GB) does not have enough memory. At Q4_K_M the weights are ~14 GB; with KV cache and runtime overhead, budget ~16 GB at a 4k context.
What is the best tool to run Magistral 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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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.