text model · SmolLM2 · Windows
Can I run SmolLM2 360M on AMD Radeon RX 7900 XTX (24GB)?
Yes. SmolLM2 360M runs on AMD Radeon RX 7900 XTX (24GB) at Q4_K_M (~1.2 GB of ~23 GB usable).
Runs at Q4_K_M using ~1.2 GB of ~23 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. AMD Radeon RX 7900 XTX (24GB) leaves ~21.8 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1.2 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
- ~$0.01
- Pays for itself after
- ~2,039M tok
At ~$0.15/kWh and the estimated ~2303 tok/s, a million generated tokens costs about $0.01 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 2,039 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 smollm2:360m llama-cli -hf bartowski/SmolLM2-360M-Instruct-GGUF:Q4_K_M lms get bartowski/SmolLM2-360M-Instruct-GGUF - Parameters
- 0.362B
- Q4_K_M size
- 0.271 GB
- Q8_0 size
- 0.386 GB
- Context
- 2k
- Ollama tag
- smollm2:360m
- 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 SmolLM2 360M on other hardware
FAQ
Can AMD Radeon RX 7900 XTX (24GB) run SmolLM2 360M?
Yes. SmolLM2 360M runs on AMD Radeon RX 7900 XTX (24GB) at Q4_K_M (~1.2 GB of ~23 GB usable).
How much memory does SmolLM2 360M need?
AMD Radeon RX 7900 XTX (24GB) has room to spare. At Q4_K_M the weights are ~0.271 GB; with KV cache and runtime overhead, budget ~1.2 GB at a 4k context.
What is the best tool to run SmolLM2 360M 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/smollm2-360m/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.