Docker offers the quickest path to setting up this model locally.
Just follow the guidelines provided below.
Then, execute the docker-compose up command to launch the model.
🛠Hash code: 7b75c0409f1979c14d2b0d6506d54d74 — Last modification: 2026-06-23 - Processor: high single-core performance needed for token latency
- RAM: 32 GB highly recommended for 26B+ GGUF models
- Storage: extra room for future model updates and datasets
- Graphics: CUDA Compute Capability 8.0+ required for flash-attention
|
The
Qwen3.6-35B-A3B-MLX-8bit model delivers
state‑of‑the‑art performance while maintaining a compact footprint thanks to its
8‑bit quantization. With
35 billion parameters and optimized architecture, it achieves
high accuracy on a wide range of NLP tasks. Built on the
MLX framework, the model benefits from
enhanced hardware compatibility and reduced memory usage. Its
inference latency is notably low, enabling real‑time applications in production environments. The following table summarizes the key technical specifications that differentiate this model from earlier versions. Users can expect
consistent results across diverse benchmarks, making it a reliable choice for both research and commercial deployment.
| Parameter | Value |
|---|
| Model Name | Qwen3.6-35B-A3B-MLX-8bit |
| Parameters | 35B |
| Quantization | 8-bit |
| Framework | MLX |
| Context Length | 8K tokens |
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