← Can It Run? — GPU compatibility matrix · Local LLMs guide
Llama 3.2 3B needs 2.2 GB VRAM at Q4. The Radeon RX 7900 XTX 24GB has 24 GB.
🦙 Llama 3.2 3B (Meta) is a 3B-parameter model for lightweight chat, on-device assistants, quick tasks. Surprisingly capable for its size; great on low-VRAM cards. The question is whether the Radeon RX 7900 XTX 24GB’s 24 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | Radeon RX 7900 XTX 24GB |
|---|---|---|
| Q4_K_M (recommended) | 2.2 GB | ✅ |
| Q8_0 (near-lossless) | 3.4 GB | ✅ |
Expected performance
Running Llama 3.2 3B at Q4 on the Radeon RX 7900 XTX 24GB, expect roughly ~64 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 2.2 GB used of 24 GB leaves about 21.8 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.
About the Radeon RX 7900 XTX 24GB
Architecture: RDNA 3 · VRAM: 24 GB · Price: ~$900.
Good: 24GB at a good price; works with llama.cpp + ROCm/Vulkan
Watch out: AMD software stack is less plug-and-play than CUDA
Check current Radeon RX 7900 XTX 24GB price on Amazon →
Related checks
Other models on the Radeon RX 7900 XTX 24GB
🎯 LLM Hardware Checker
Enter your exact GPU + RAM, see every model you can run.
💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the Radeon RX 7900 XTX 24GB run Llama 3.2 3B?
Yes — runs at Q4 and full Q8 quality. Llama 3.2 3B needs 2.2 GB VRAM at Q4_K_M; the Radeon RX 7900 XTX 24GB has 24 GB.
How much VRAM does Llama 3.2 3B need?
Llama 3.2 3B needs about 2.2 GB at Q4_K_M (recommended) and 3.4 GB at Q8_0.
How many tokens per second will the Radeon RX 7900 XTX 24GB get on Llama 3.2 3B?
Roughly ~64 tokens/sec at Q4 in Ollama or llama.cpp.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.