No — not on this device
Kimi K3 2.8T-A104B at Q4_K_M needs 1564.0 GB against 10.7 GB usable, and the shortfall of 1553.3 GB is more than 32 GB of system RAM can cover at a tolerable speed. A smaller sibling or a lower quantisation is the honest answer here.
The largest open-weight model ever published. 69 of 93 blocks are linear attention, so its cache is tiny; its 2.8T weights are the problem.
The VRAM budget
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 2750.9 GB | 2751.7 GB | — | ~0.2 | −0.1% ppl | 2741.0 GB over |
| Q6_K | 2123.0 GB | 2123.9 GB | — | ~0.2 | −0.4% ppl | 2113.2 GB over |
| Q5_K_M | 1835.0 GB | 1835.8 GB | — | ~0.3 | −0.8% ppl | 1825.1 GB over |
| Q4_K_M | 1563.2 GB | 1564.0 GB | — | ~0.3 | −1.9% ppl | 1553.3 GB over |
| Q3_K_M | 1265.4 GB | 1266.2 GB | — | ~0.4 | −5.4% ppl | 1255.5 GB over |
| Q2_K | 1084.2 GB | 1085.0 GB | — | ~0.4 | −15% ppl | 1074.3 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. Only 24 of its 93 blocks keep a per-token KV cache; the rest are linear-attention, Mamba or convolution blocks with a fixed-size state. This model uses multi-head latent attention, so its cache is a compressed latent rather than full K and V.
How to run it
$ pip install mlx-lm $ mlx_lm.generate --model mlx-community/Kimi-K3-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.