No — not on this device
DeepSeek V4.1 Flash 552B-A16B at Q4_K_M needs 311.0 GB against 7.0 GB usable, and the shortfall of 304.0 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.
Twice V4 Flash's backbone (552B, 16B active per generated token) plus 196B of Engram lookup tables (189 GiB at FP8) that DwarfStar streams from SSD, so they are not counted here. A 512 GB Mac model, and as of October 2026 no mainline llama.cpp or Ollama build: DwarfStar on a Mac, vLLM across four GPUs. DeepSeek puts the cache at 890 bytes/token; modelled conservatively as 4 latent layers plus a 128-token window.
What hardware do I need for DeepSeek V4.1 Flash 552B-A16B? →
The VRAM budget
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 546.2 GB | 546.9 GB | — | ~0.9 | −0.1% ppl | 539.9 GB over |
| Q6_K | 421.6 GB | 422.2 GB | — | ~1.2 | −0.4% ppl | 415.2 GB over |
| Q5_K_M | 364.4 GB | 365.0 GB | — | ~1.3 | −0.8% ppl | 358.0 GB over |
| Q4_K_M | 310.4 GB | 311.0 GB | — | ~1.6 | −1.9% ppl | 304.0 GB over |
| Q3_K_M | 251.3 GB | 251.9 GB | — | ~2.0 | −5.4% ppl | 244.9 GB over |
| Q2_K | 215.3 GB | 215.9 GB | — | ~2.3 | −15% ppl | 208.9 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 interleaves sliding-window layers (128 tokens, 4 of 40 layers global), which is why its cache barely grows with context. This model uses multi-head latent attention, so its cache is a compressed latent rather than full K and V.
How to run it
None of the runtimes we cover can load DeepSeek V4.1 Flash 552B-A16B on Intel hardware yet. Its model card says what does.