Can I run Llama 3.2 3B on NVIDIA RTX 4060 Ti 16GB?
Yes: Llama 3.2 3B runs on NVIDIA RTX 4060 Ti 16GB with F16 at roughly 25 tokens/s. Estimated memory 7.6 GiB at 8,192 tokens of context.
With F16 you can push the context to about 52,736 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 | ~25 tok/s |
| Q8_0Near-lossless | 4.8 GiB | Fits | ~43 tok/s |
| Q6_KVery close to F16 | 4.1 GiB | Fits | ~52 tok/s |
| Q5_K_MGreat quality/size | 3.7 GiB | Fits | ~58 tok/s |
| Q4_K_MBest default for most | 3.4 GiB | Fits | ~65 tok/s |
| IQ4_XSSmaller 4-bit (imatrix) | 3.2 GiB | Fits | ~71 tok/s |
| Q3_K_MNoticeable loss | 3.1 GiB | Fits | ~75 tok/s |
| Q2_KLast resort; big loss | 2.9 GiB | Fits | ~82 tok/s |
NVIDIA RTX 4060 Ti 16GB offers about 16 GiB of memory (92% usable for a model) at 288 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 NVIDIA RTX 4060 Ti 16GB
Running slowly or out of memory? See CUDA out of memory and the quantisation lesson.