text model · Mistral · Windows
Can I run Magistral Small on AMD Radeon RX 7900 XTX (24GB)?
Yes. Magistral Small runs on AMD Radeon RX 7900 XTX (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. AMD Radeon RX 7900 XTX (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
- ~355 W
- Electricity / 1M tokens
- ~$0.33
- Pays for itself after
- ~5,876M tok
At ~$0.15/kWh and the estimated ~45 tok/s, a million generated tokens costs about $0.33 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$999 AMD Radeon RX 7900 XTX (24GB) pays for itself after roughly 5,876 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
- ~355 W
- Best runtime
- Ollama (ROCm) / llama.cpp ROCm (Linux)
You could also run
Run Magistral Small on other hardware
FAQ
Can AMD Radeon RX 7900 XTX (24GB) run Magistral Small?
Yes. Magistral Small runs on AMD Radeon RX 7900 XTX (24GB) at Q4_K_M (~16 GB of ~23 GB usable).
How much memory does Magistral Small need?
AMD Radeon RX 7900 XTX (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/amd-rx-7900-xtx-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.