# DFlash Apple Silicon Benchmark Report

## Machine
- Label: M3 Max 36GB
- Selected pair: qwen35-9b
- Base model: Qwen/Qwen3.5-9B
- Draft model: z-lab/Qwen3.5-9B-DFlash
- Estimated total weight footprint: 19.93 GB

## Setup
```bash
python3 -m venv .venv-dflash
source .venv-dflash/bin/activate
git clone https://github.com/z-lab/dflash.git
cd dflash
pip install -e .[mlx]
python -m dflash.benchmark --backend mlx \
    --model Qwen/Qwen3.5-9B \
    --draft-model z-lab/Qwen3.5-9B-DFlash \
    --dataset gsm8k \
    --max-samples 128 \
    --enable-thinking \
    --draft-sliding-window-size 4096
```

## Baseline comparison
Compare against **plain MLX or llama.cpp speculative decoding** on the same prompt set.

## Results
- Throughput (tok/s):
- Peak memory (GB):
- Notes on acceptance / behavior:

## Verdict
Worth operationalizing locally?
- [ ] Yes
- [ ] No
- [ ] Needs more data

## Recommendation
Explain whether this should become part of the local inference stack.