No — not on this device
Ling 3.0 Flash 124B-A5B at Q4_K_M needs 70.4 GB against 18.0 GB usable, and the shortfall of 52.4 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.
A 124B hybrid (5 linear-attention layers per MLA layer) with 5.1B active: SWE-bench Pro 56.6 and AIME 93 claimed. Built for 96–128 GB machines.
What hardware do I need for Ling 3.0 Flash 124B-A5B? →
Fits instead: Ling 3.0 Tiny 7.9B-A1.3B (5.1 GB)
The VRAM budget
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 122.7 GB | 123.4 GB | — | ~5.2 | −0.1% ppl | 105.4 GB over |
| Q6_K | 94.7 GB | 95.4 GB | — | ~6.7 | −0.4% ppl | 77.4 GB over |
| Q5_K_M | 81.8 GB | 82.5 GB | — | ~7.7 | −0.8% ppl | 64.5 GB over |
| Q4_K_M | 69.7 GB | 70.4 GB | — | ~9.1 | −1.9% ppl | 52.4 GB over |
| Q3_K_M | 56.4 GB | 57.1 GB | — | ~11 | −5.4% ppl | 39.1 GB over |
| Q2_K | 48.4 GB | 49.0 GB | — | ~13 | −15% ppl | 31.0 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. Only 7 of its 42 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/Ling-3.0-flash-4bit \ --max-tokens 512 --prompt "Hello"
Apple's own array framework. The fastest path on Apple Silicon. More on MLX.