DeepSeek-V4-Flash-0731-2.4bit-mixed

Mixed-precision MLX quantization of deepseek-ai/DeepSeek-V4-Flash-0731 (284B total, ~304B with MTP, ~13.8B active per token), at 2.44 bits/weight effective, 92.8 GB on disk. Produced with oMLX oQ level 2 enhanced and then hand-tuned: I requantized attention and the MTP experts myself, outside the oQ allocator, to get under 2.5 bpw without touching what the model actually needs. For Apple Silicon.

• 92.8 GB on disk (86.5 GiB), down from 155 GB in the source's native FP8 e4m3 + FP4-expert format
• 43 layers, 256 routed experts + 1 shared, top-6; MLA with grouped output projections, the sparse-attention indexer (index_topk 512), hyper-connections with Sinkhorn, YaRN to 1M context
• All three DSpark MTP blocks are preserved (19.8B params)
• Peak memory in my smoke test: 84.4 GB at short context, so this wants a 128 GB Mac
• Converted and tested on a Macbook Pro M5 Max 128GB 40 GPU

Requirements

oMLX 0.5.7 or newer. mlx-lm does not support the deepseek_v4 architecture. There are half a dozen open PRs (mlx-lm#1189 among them) and I haven't checked myself whether any of them work. oMLX loads this repo directly from the HF cache with no model setting.

The upstream checkpoint ships no chat template, and I kept it that way rather than inventing one. Use oMLX's apply_chat_template, as in Usage below.

Quantization

The base is 2-bit affine at group size 128 and it carries the routed experts, which are 84% of the file. Everything read on every token got promoted: attention to 6 bits at group size 128, and the shared expert, embeddings and lm_head to 8 bits at group size 64. The MTP blocks' own experts sit at 3 bits, above the main model's 2, because there are only three of them and they cost almost nothing.

Output is standard MLX affine quantization, so no custom kernels or runtime are required.

Conversion check

Smoke-tested after conversion with oMLX's loader, three ways: thinking mode greedy, chat mode greedy, and thinking mode at temperature 0.6 with repetition penalty 1.1. Coherent in all three, English throughout, solved 17 * 24 = 408 by the distributive property with the <think> block opening and closing correctly. No repetition loops. Greedy decoding is the thing to check on a build this aggressive: the earlier uniform 2-bit attempts only held together with sampling on, and collapsed into repeated fragments or drifted into Chinese mid-answer under greedy.

Performance

Measured with oMLX's benchmark harness on a Macbook Pro M5 Max 128GB 40 GPU, single request, 128 generated tokens, speculative decoding off.

Two things worth pointing out. Generation barely degrades with context, 33.8 tok/s at 4k against 31.5 at 32k, which is the sparse-attention indexer doing its job: index_topk 512 bounds how many tokens enter the attention computation regardless of prompt length. And peak memory rises by 1.03 GB across an 8x jump in context, because MLA compresses the KV cache hard. That is what makes a 304B model with 32k of context livable on a 128 GB machine. The cost sits in prefill: 97 seconds to first token at 32k.

Continuous batching at 1k prompt / 128 generated:

Accuracy

mmlu_pro, n=600 seeded samples, thinking off, greedy, identical questions on both rows. The bf16 row is the hosted API measured the same way, through an OpenAI-compatible endpoint with the same prompts, the same max_tokens and temperature 0. Standard error at this n is about 2 points on each row.

That is 7.3 points below full precision, which at this sample size sits around 2.6 standard errors: enough to point at a real loss, not enough to pin its size. A drop of roughly this shape is what you would expect from quantization at 2.44 bpw, and I would treat it as such rather than as a measured constant.

Treat this as a rough sighting, not a verdict. One benchmark at n=600 covers a narrow slice of what the model does, with no long-context work, no agentic loops and no real code, which is most of what this checkpoint was built for. I ran it to size the cost of the bit-width, not to certify the model. Test it on your own workload before trusting any of it.

Usage

# oMLX discovers this from the HF cache and serves it as deepseek-v4-flash-0731-2-4bit-mixed
omlx serve

from omlx.utils.model_loading import load_text_model
from omlx.patches.deepseek_v4.chat_template_v4 import apply_chat_template
from mlx_lm import generate
from mlx_lm.sample_utils import make_sampler

model, tok = load_text_model("mlx-community/DeepSeek-V4-Flash-0731-2.4bit-mixed")
prompt = apply_chat_template(
    [{"role": "user", "content": "Explain Bayes' theorem in two sentences."}],
    add_generation_prompt=True,
    thinking_mode="thinking",   # or "chat"
)
print(generate(model, tok, prompt, max_tokens=512, sampler=make_sampler(temp=0.0)))

License

MIT, inherited from the base model. Refer to the original model card for architecture, benchmarks, and intended use.