Can I run Llama 3.2 3B on AMD RX 7900 XTX 24GB?
Yes: Llama 3.2 3B runs on AMD RX 7900 XTX 24GB with F16 at roughly 85 tokens/s. Estimated memory 7.6 GiB at 8,192 tokens of context.
With F16 you can push the context to about 104,960 tokens before memory runs out (F16 KV cache).
Memory by quantisation (8,192-token context)
| Quantisation | Memory | Verdict | Est. speed |
|---|---|---|---|
| F16Unquantized reference | 7.6 GiB | Fits | ~85 tok/s |
| Q8_0Near-lossless | 4.8 GiB | Fits | ~143 tok/s |
| Q6_KVery close to F16 | 4.1 GiB | Fits | ~175 tok/s |
| Q5_K_MGreat quality/size | 3.7 GiB | Fits | ~194 tok/s |
| Q4_K_MBest default for most | 3.4 GiB | Fits | ~216 tok/s |
| IQ4_XSSmaller 4-bit (imatrix) | 3.2 GiB | Fits | ~236 tok/s |
| Q3_K_MNoticeable loss | 3.1 GiB | Fits | ~249 tok/s |
| Q2_KLast resort; big loss | 2.9 GiB | Fits | ~273 tok/s |
AMD RX 7900 XTX 24GB offers about 24 GiB of memory (92% usable for a model) at 960 GB/s. Speed is a bandwidth-bound estimate of single-stream generation at 65% efficiency; real results vary by runtime, drivers and settings, so measure on your machine.
How this is calculated
Weights ≈ parameters × bits per weight ÷ 8. KV cache ≈ 2 × 28 layers × 8 KV heads × 128 head dim × 2 bytes per token. Decode speed ≈ efficiency × memory bandwidth ÷ bytes read per token. Learn the maths in VRAM math and memory bandwidth, or try the VRAM calculator.
Llama 3.2 3B on other hardware
Similar models on AMD RX 7900 XTX 24GB
Running slowly or out of memory? See CUDA out of memory and the quantisation lesson.