Can I run Llama 3.2 1B on CPU only, dual-channel DDR5 (32GB)?
Yes: Llama 3.2 1B runs on CPU only, dual-channel DDR5 (32GB) with F16 at roughly 19 tokens/s. Estimated memory 3.2 GiB at 8,192 tokens of context.
With F16 you can push the context to about 131,072 tokens before memory runs out (F16 KV cache).
Memory by quantisation (8,192-token context)
| Quantisation | Memory | Verdict | Est. speed |
|---|---|---|---|
| F16Unquantized reference | 3.2 GiB | Fits | ~19 tok/s |
| Q8_0Near-lossless | 2.2 GiB | Fits | ~33 tok/s |
| Q6_KVery close to F16 | 1.9 GiB | Fits | ~40 tok/s |
| Q5_K_MGreat quality/size | 1.8 GiB | Fits | ~45 tok/s |
| Q4_K_MBest default for most | 1.6 GiB | Fits | ~51 tok/s |
| IQ4_XSSmaller 4-bit (imatrix) | 1.6 GiB | Fits | ~56 tok/s |
| Q3_K_MNoticeable loss | 1.5 GiB | Fits | ~59 tok/s |
| Q2_KLast resort; big loss | 1.4 GiB | Fits | ~66 tok/s |
CPU only, dual-channel DDR5 (32GB) offers about 32 GiB of memory (80% usable for a model) at 80 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 × 16 layers × 8 KV heads × 64 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 1B on other hardware
Similar models on CPU only, dual-channel DDR5 (32GB)
Running slowly or out of memory? See CUDA out of memory and the quantisation lesson.