text model · Mistral · Windows
Can I run Magistral Small on Nvidia GeForce RTX 4090 (24GB)?
Yes. Magistral Small runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~16 GB of ~23 GB usable).
Runs at Q4_K_M using ~16 GB of ~23 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 4090 (24GB) leaves ~7 GB of headroom.
- Q4_K_M needed
- ~16 GB
- Usable on device
- ~23 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~450 W
- Electricity / 1M tokens
- ~$0.4
- Pays for itself after
- ~15,990M tok
At ~$0.15/kWh and the estimated ~47 tok/s, a million generated tokens costs about $0.4 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,599 Nvidia GeForce RTX 4090 (24GB) pays for itself after roughly 15,990 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
Loads the Q4_K_M weights.
ollama run magistral:24b - Parameters
- 24B
- Q4_K_M size
- 14 GB
- Q8_0 size
- 25 GB
- Context
- 40k
- Ollama tag
- magistral:24b
- Memory
- 24 GB vram
- Usable for weights
- ~23 GB
- Power draw
- ~450 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Magistral Small on other hardware
FAQ
Can Nvidia GeForce RTX 4090 (24GB) run Magistral Small?
Yes. Magistral Small runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~16 GB of ~23 GB usable).
How much memory does Magistral Small need?
Nvidia GeForce RTX 4090 (24GB) has room to spare. 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/magistral-small/nvidia-rtx-4090-24gb) 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.