Yes — with 223.3 GB to spare

DeepSeek V4 Flash 284B-A13B at Q4_K_M fits your M3 Ultra · 512 GB entirely in unified memory at 8K context, at an estimated 29 tokens per second. There is room for its full 1024K window.

Fully in unified memory 8K context Q4_K_M · 159.7 GB MIT Released 31 Jul 2026 New this month Not in the Ollama library

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.

What hardware do I need for DeepSeek V4 Flash 284B-A13B? →

The VRAM budget

weights 159.7 GB

Weights 159.7 GB KV cache @ 8K 0.38 GB Runtime overhead 0.6 GB Free 223.3 GB of 384.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	281.0 GB	282.0 GB	1024K	16	−0.1% ppl	Long context
Q6_K	216.9 GB	217.9 GB	1024K	21	−0.4% ppl	Long context
Q5_K_M	187.5 GB	188.4 GB	1024K	24	−0.8% ppl	Long context
Q4_K_M	159.7 GB	160.7 GB	1024K	29	−1.9% ppl	Recommended
Q3_K_M	129.3 GB	130.3 GB	1024K	35	−5.4% ppl	Long context
Q2_K	110.8 GB	111.7 GB	1024K	41	−15% ppl	Long context

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.cpp LM Studio MLX

terminal

$ pip install mlx-lm
$ mlx_lm.generate --model mlx-community/DeepSeek-V4-Flash-0731-4bit \
    --max-tokens 512 --prompt "Hello"

Apple's own array framework. The fastest path on Apple Silicon. More on MLX.

01Download is 159.7 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02macOS caps what the GPU may wire down at about 384.0 GB of 512 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.

03There is room to go to the model's full 1024K context on this card.

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