No — not on this device
DeepSeek V4 Flash 284B-A13B at Q4_K_M needs 160.7 GB against 51.2 GB usable, and the shortfall of 109.5 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 V4 that 128 GB machines can actually run at Q3. Cache is modelled as a 576-wide latent; V4 compresses it further at long context, so this is conservative.
The VRAM budget
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 281.0 GB | 282.0 GB | — | ~1.3 | −0.1% ppl | 230.8 GB over |
| Q6_K | 216.9 GB | 217.9 GB | — | ~1.7 | −0.4% ppl | 166.7 GB over |
| Q5_K_M | 187.5 GB | 188.4 GB | — | ~2.0 | −0.8% ppl | 137.2 GB over |
| Q4_K_M | 159.7 GB | 160.7 GB | — | ~2.3 | −1.9% ppl | 109.5 GB over |
| Q3_K_M | 129.3 GB | 130.3 GB | — | ~2.8 | −5.4% ppl | 79.1 GB over |
| Q2_K | 110.8 GB | 111.7 GB | — | ~3.3 | −15% ppl | 60.5 GB over |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model uses multi-head latent attention, so its cache is a compressed latent rather than full K and V.
How to run it
$ llama-server \
-hf deepseek-ai/DeepSeek-V4-Flash-0731:Q4_K_M \
-c 8192 -ngl 13
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.