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

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