---
license: mit
license_link: LICENSE
base_model: deepseek-ai/DeepSeek-V4-Flash
base_model_relation: quantized
library_name: mlx
pipeline_tag: text-generation
tags:
  - mlx
  - apple-silicon
  - deepseek
  - deepseek-v4
  - mixture-of-experts
  - moe
  - quantized
  - 4-bit
  - 8-bit
  - affine
language:
  - en
  - zh
inference: false
---

# DeepSeek-V4-Flash-MLX-Q4Q8

A mixed-precision MLX quantization of [`deepseek-ai/DeepSeek-V4-Flash`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash)
intended for Apple-Silicon inference via [vMLX](https://vmlx.ai/) (or any
MLX-aware runtime that loads `mlx_lm.utils.load`).

- **Architecture**: DeepSeek-V4 — 289.9 B total parameters, 256 routed
  experts (top-6 per token), 1 shared expert, 43 layers, MLA attention
  with `head_dim=512` and grouped output projection, mHC
  (Manifold-Constrained Hyper-Connections, `hc_mult=4`),
  sqrtsoftplus + hash routing for the first 3 layers.
- **Quantization**: standard MLX `affine` mode (output of `mx.quantize`,
  not TurboQuant). Tensor naming `<module>.{weight, scales, biases}`.
  Group size 32. Layout in safetensors:
  - **routed experts** (`layers.N.ffn.experts.E.{w1,w2,w3}`): 4-bit
  - **attention** (`layers.N.attn.{wq_a, wkv, wo_a, wo_b, ...}`): 8-bit
  - **shared expert, embed_tokens, lm_head**: 8-bit
  - **norms, router gate, mHC params**: fp16 (passthrough)
- **On-disk size**: 173 GB across 159 safetensors shards.
- **Context**: 1,048,576 tokens (sliding-window=128 short-prompt-safe).

## Usage with vMLX

The bundle is a drop-in replacement for the upstream FP4/FP8 release in
vMLX 1.3.97+. Two non-obvious considerations:

### 1. Runtime patch required (`jang_tools.load_jangtq`)

vMLX's bundled `jang_tools.load_jangtq._patch_quant_config_inplace`
(`/Applications/vMLX.app/.../jang_tools/load_jangtq.py`) infers
quantization overrides from raw safetensors keys
(`model.layers.N.ffn.experts.E.w1`) — these never match the
post-`sanitize()` module paths the MLX `Model` exposes
(`model.layers.N.mlp.switch_mlp.gate_proj`), so it overwrites this
bundle's correct config with unmatchable disk-keyed entries. After
overwrite, `mlx_lm`'s `class_predicate` falls through to top-level
`bits=8` and the routed experts get wrapped as 8-bit modules. The
4-bit-packed weights then silently fail to load (with `strict=False`)
and the model produces BOS-token loops at inference.

The fix is a 4-line guard at the top of `_patch_quant_config_inplace`
that returns early when the user's config already has post-sanitize
overrides:

```python
if existing_overrides and any(k.startswith("model.") for k in existing_overrides):
    return {"action": "user_provided", "existing_overrides": len(existing_overrides)}
```

The accompanying [`build_mlx_q4q8.sh`](#building-from-source) script's
`patch_loader` step applies this idempotently. See
[`requantization-plan.md`](#building-from-source) for the full diagnosis.

### 2. SimpleEngine only

vMLX auto-disables `--continuous-batching` for DSV4 because the
batched generator is incompatible with the model's 4-D mHC residual
stream. All requests go through SimpleEngine. Throughput on
Mac Studio M3 Ultra (256 GB unified memory): ~22 tok/s decode,
~75 tok/s prefill.

### Serving

```bash
/Applications/vMLX.app/Contents/Resources/bundled-python/python/bin/python3 \
  -m vmlx_engine.cli serve \
  /path/to/DeepSeek-V4-Flash-MLX-Q4Q8 \
  --served-model-name deepseek-v4-flash-mlx-q4q8 \
  --host 127.0.0.1 --port 8010 \
  --max-tokens 4096 \
  --tool-call-parser deepseek \
  --enable-auto-tool-choice
```

Then hit it with the OpenAI-compatible chat-completion API:

```bash
curl -s http://127.0.0.1:8010/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "deepseek-v4-flash-mlx-q4q8",
    "messages": [{"role": "user", "content": "What is 17+28?"}],
    "max_tokens": 120
  }'
```

The model is reasoning-capable (`<think>...</think>` blocks land in
`reasoning_content`; the final answer in `content`).

## Hardware requirements

- Apple Silicon (M1 Max / M2 Ultra / M3 Ultra recommended).
- **Unified memory**: ≥ 192 GB strongly recommended; the bundle's
  173 GB working set plus KV cache plus a 70 % wired-limit headroom
  (configured automatically by `jang_tools.load_jangtq._apply_wired_limit_safe_default`)
  needs comfortable spillover. Will technically load on 128 GB with
  reduced max-tokens, but expect SSD pressure.
- macOS 14+ for the Metal kernels used by the routed-expert SwitchGLU.

## Tool calling & reasoning

The bundle ships with the DSML tool-call grammar
(`｜DSML｜` / `<｜tool_calls｜>` / `<｜invoke｜>`); pair it with vMLX's
`--tool-call-parser deepseek --enable-auto-tool-choice`. Reasoning
modes:

- **chat** (default): direct response, no `<think>` block.
- **thinking**: emits `<think>...</think>` wrapped reasoning, parsed
  out into `reasoning_content` by `DeepSeekR1ReasoningParser`.

Both modes set the `<｜latest_reminder｜>` anchor automatically — vMLX
adds a default system prompt (`DSV4: injected default system prompt`
in the load log) to keep multi-turn chat from running away on
reasoning loops.

## Quantization details

This release is the output of:

1. Convert from upstream FP4 (routed experts) + FP8 (others) using
   `jang_tools.dsv4.convert_dsv4_jangtq --profile 4 --format jang`.
2. **Re-quantize the routed expert tensors** from the FP4 source
   through `mx.quantize(..., group_size=32, bits=4, mode="affine")`.
   The upstream converter direct-copies FP4 onto disk in MXFP4 form
   (uint8 E8M0 scales, no biases) regardless of `--format`; vMLX's
   MXFP4 dispatch is broken at 4-bit and produces gibberish. The
   re-quantization step rewrites `.weight + .scales + .biases` for
   each of the 33,024 routed expert tensors using MLX's actual affine
   formula:
   ```
   scale = max((w_max - w_min) / 15, eps)
   side  = abs(w_min) > abs(w_max)
   scale = side ? scale : -scale
   edge  = side ? w_min : w_max
   q0    = round(edge / scale)
   scale = (q0 != 0) ? edge / q0 : scale
   bias  = (q0 != 0) ? edge      : 0
   ```
   (matches `mlx/include/mlx/backend/metal/kernels/quantized.h:2387`).
3. Rebuild `model.safetensors.index.json` to include the
   newly-introduced `.biases` keys.

### Size vs. quality tradeoff

This bundle is **173 GB** on disk vs. **~149 GB** for the upstream
FP8 (non-experts) + FP4 (experts) release — about 24 GB of overhead.
The extra space comes from MLX's affine quantization scheme:

- **group_size = 32** (vs. upstream's 128×128 blocks): finer-grained
  scales mean less quantization error per group, but more
  scale/bias metadata per tensor.
- **non-experts at Q8 affine** (vs. upstream FP8 block): keeps
  attention, router, shared expert, embed/lm_head at 8-bit affine,
  which is quality-sensitive and small in total — cheap to spend
  bits on.
- **experts at Q4 affine** (vs. upstream MXFP4): same nominal width,
  but affine adds per-group `bias` tensors that MXFP4 doesn't carry.

The choice is deliberate and quality-leaning rather than
size-leaning. Rough perplexity deltas vs. bf16 (extrapolated from
published llama.cpp / MLX quantization studies — not measured on
V4-Flash specifically):

| Knob                       | Size saved | Quality cost                     |
|----------------------------|------------|----------------------------------|
| group_size 32 → 64         | ~6–8 GB    | +0.1–0.3 % PPL                   |
| group_size 32 → 128        | ~10–12 GB  | +0.3–0.8 % PPL                   |
| Non-experts Q8 → Q6        | ~3–5 GB    | +0.1–0.3 % PPL                   |
| Non-experts Q8 → Q4        | ~8–10 GB   | +0.5–2 % PPL, noticeable on long-context / reasoning |
| Experts Q4 → Q3            | ~30–40 GB  | +2–6 % PPL, real degradation     |

The current config is essentially lossless (<1 % PPL increase).
**A more space-balanced alternative for 192 GB Macs**: keep Q8
non-experts + Q4 experts but bump to `group_size=64` — saves ~6–8 GB,
quality loss is in the noise. Going below Q4 on the experts is where
MoE models fall off a cliff (each token only sees 6 of 256 experts,
so quantization noise does not average out across the population),
and gs=128 starts to bite on 1M-token contexts where small per-token
errors compound.

Net: the 24 GB overhead is the price of (a) MLX compatibility — there
is no MLX kernel for DeepSeek's native FP8-block / MXFP4 layout — and
(b) a config that errs on the side of preserving quality over
shaving space.

The community `mxfp4_to_affine.py` script that ships in some upstream
DSV4 conversion guides uses `scale = (max-min)/15, bias = min`, which
**does not** match MLX's affine convention. Bundles produced that way
load but compound quantization error across the 43 transformer layers
(activations explode by layer ~20, NaN by layer ~29) and emit BOS-loop
gibberish. Do not use that script.

## Files in this bundle

```
.
├── config.json                        # 132 quantization entries (129 routed-expert per-module + globals)
├── jang_config.json                   # vMLX chat / reasoning / tool-call schema
├── generation_config.json             # eos_token_id = [1, 128803, 128804]
├── tokenizer.json
├── tokenizer_config.json              # embedded chat_template + special tokens
├── encoding/                          # DSV4 encoding adapter
├── model-00001-of-00159.safetensors  # 159 shards, total ~173 GB
│   ...
├── model.safetensors.index.json
├── LICENSE
├── README.md                          # this file
├── README.upstream.md                 # upstream DeepSeek-V4 model card
└── DeepSeek_V4.pdf                    # upstream tech report
```

## Building from source

The full pipeline (download → convert → re-quantize → finalize → patch
→ verify) is automated in
[`build_mlx_q4q8.sh`](https://huggingface.co/Deviad/DeepSeek-V4-Flash-MLX-Q4Q8/blob/main/build_mlx_q4q8.sh) (companion script in the
project repo). Quick reference of the steps:

```
./build_mlx_q4q8.sh check         # sanity-check disks + tools
./build_mlx_q4q8.sh patch_loader  # apply the load_jangtq.py guard
./build_mlx_q4q8.sh download      # hf download deepseek-ai/DeepSeek-V4-Flash
./build_mlx_q4q8.sh convert       # ~40 min: jang_tools convert_dsv4_jangtq
./build_mlx_q4q8.sh requantize    # ~30 min: mx.quantize routed experts
./build_mlx_q4q8.sh finalize      # tokenizer / encoding asset copy
./build_mlx_q4q8.sh patch         # EOS / chat_template fixes
./build_mlx_q4q8.sh verify        # check the bundle
./build_mlx_q4q8.sh serve         # launch vMLX
```

`./build_mlx_q4q8.sh all` runs everything in order. Total runtime on
M3 Ultra: ~75 minutes plus the initial download (~160 GB at ~150 MB/s =
~18 minutes on a fast link).

See [`requantization-plan.md`](https://huggingface.co/Deviad/DeepSeek-V4-Flash-MLX-Q4Q8/blob/main/requantization-plan.md) for the
diagnostic write-up of why the requantize step is needed.

## License & attribution

This bundle is licensed under MIT, matching the upstream
[DeepSeek-V4-Flash license](LICENSE).

The original model and tech report are credited to the
[DeepSeek-AI](https://www.deepseek.com/) team. Please cite their work
when using this model:

```
@misc{deepseekv4,
  title  = {DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
  author = {DeepSeek-AI},
  year   = {2025},
  url    = {https://github.com/deepseek-ai/DeepSeek-V4}
}
```

The MLX-Q4Q8 quantization recipe is provided as-is and adds nothing
substantive to the science — it is purely a packaging artifact for
running the model on Apple-Silicon hardware.

## Acknowledgments

- DeepSeek-AI for the base model and the open-source release.
- The MLX team at Apple for the framework and the
  `mlx.core.quantize` reference implementation.
- The vMLX team for the `jang_tools` tooling and the `load_jangtq`
  loader (modulo the patch noted above).