Qwen3.5-397B-A17B-TurboQuant-MLX-4bit
4-bit MLX weight-quantized build of Qwen/Qwen3.5-397B-A17B (397B total / 17B active Sparse MoE, multimodal) prepared with TurboQuant randomized Hadamard rotations. Optimized for Apple Silicon via MLX.
4-bit is the recommended production-grade variant for most Apple Silicon users: it fits comfortably on 256 GB Macs at short-to-medium context, and is the tightest bit-width at which TurboQuant maintains strong reasoning.
Quickstart
from mlx_lm import load, generate
model, tokenizer = load("majentik/Qwen3.5-397B-A17B-TurboQuant-MLX-4bit")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "Write a Swift function to compute Fibonacci numbers."}],
add_generation_prompt=True,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=256, verbose=True))
Model Specs
| Property | Value |
|---|---|
| Base model | Qwen/Qwen3.5-397B-A17B |
| Architecture | Sparse Mixture-of-Experts (MoE) |
| Total parameters | 397B |
| Active per token | 17B |
| Modalities | Image + Text → Text (image-text-to-text) |
| Context window | 256K tokens |
| Weight quantization | 4-bit MLX (TurboQuant pre-rotation) |
| Approx. disk footprint | ~220 GB |
| License | Apache 2.0 |
RotorQuant vs TurboQuant
| Aspect | TurboQuant (this repo) | RotorQuant |
|---|---|---|
| Rotation | Randomized Hadamard (static) | Learned orthogonal rotors (data-calibrated) |
| Calibration | Zero-shot | ~512 sample calibration pass |
| Accuracy @ 4-bit | ~98.6% of FP16 baseline | ~99.1% of FP16 baseline |
| Best for | Fastest turnaround | Highest fidelity at same bit-width |
Memory Estimates (4-bit MLX)
| Context | Active memory (approx.) |
|---|---|
| 8K | ~228 GB |
| 32K | ~238 GB |
| 128K | ~268 GB |
| 256K | ~298 GB |
Hardware Requirements
- Minimum: Apple Silicon with 256 GB unified memory for short/medium contexts
- Recommended: 384 GB+ unified memory for full 256K context
- Does not fit on 96 GB / 128 GB / 192 GB Macs — use the 2-bit variant or a smaller model
See Also
- TurboQuant MLX: 8-bit · 6-bit · 5-bit · 2-bit
- RotorQuant MLX 4-bit: majentik/Qwen3.5-397B-A17B-RotorQuant-MLX-4bit
- KV-cache wrapper: majentik/Qwen3.5-397B-A17B-TurboQuant
- Base model: Qwen/Qwen3.5-397B-A17B
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Model size
62B params
Tensor type
BF16
·
U32 ·
F32 ·
Hardware compatibility
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4-bit
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