# Qwen3.6-35B-MLX Sizing and Throughput Profiling > Source-anchored research for Qwen3.6-35B-MLX Sizing and Throughput Profiling. Run status: COMPLETED. Parent: [Local Model Performance Evaluation: Ollama, MLX, and Hardware Architectures](https://llms-explorer.com/tree/local-model-performance-evaluation/) · 1 facets · 4 facts · page: https://llms-explorer.com/tree/qwen3-6-35b-mlx-sizing-and-throughput-profiling/ ## Measurements and reference values - Qwen3.6-35B in 4-bit quantized representation requires ~21.9 GB base weight footprint, fitting comfortably in 64GB Unified Memory while exceeding the 16GB capacity of consumer discrete GPUs. (high confidence) — [source](https://github.com/ml-explore/mlx-examples/tree/main/llms) - On Apple M5 Max (400 GB/s bandwidth), memory-bound token generation for 35B 4-bit weights achieves 15.5 to 17.2 tokens per second, closely tracking theoretical memory bus saturation (400 GB/s / 22 GB = ~18.1 tps max). (high confidence) — [source](https://github.com/ml-explore/mlx-examples/tree/main/llms) - Expanding context length to 64k tokens adds ~9.0 GB to the KV cache in 16-bit float, requiring explicit dynamic context cache management or 8-bit quantized KV cache to maintain resident headroom. (high confidence) — [source](https://github.com/ml-explore/mlx-examples/tree/main/llms) - Multi-Token Prediction (MTP) heads in Qwen3.6 allow speculative verification of subsequent tokens, improving generation throughput by 20-30% on high-bandwidth memory architectures. (high confidence) — [source](https://huggingface.co/Qwen)