About Qwen3.6 35B-A3B
Qwen3.6 35B-A3B is Alibaba's 35B-parameter model released in April 2026, with a 256K-token context window. It is a mixture-of-experts model: all 35B parameters must sit in memory, but only ~3B are active per token, which is what makes it fast for its size. It uses a hybrid-attention design — only a fraction of its layers cache the full context, so long conversations cost far less VRAM than a classic dense model: its KV cache is about 0.2 GB at an 8K context, 2.7 GB at 128K, and 5.4 GB at the full 256K window (FP16 cache).
For most people Q4_K_M is the sweet spot — the most popular quality/size trade-off — while Q8 is near-lossless if you have the memory. Totals above include the KV cache and a realistic framework overhead, so they are what you should expect to see in practice rather than just the download size. Weight sizes are calibrated against real GGUF files — see themethodology.
Frequently asked questions
How much VRAM does Qwen3.6 35B-A3B need?
At Q4_K_M with an 8K context, Qwen3.6 35B-A3B needs about 25 GB (weights 21 GB + KV cache + overhead). The smallest common hardware that fits is a RTX 5090.
Can an RTX 4090 (24GB) run Qwen3.6 35B-A3B?
Not fully in VRAM. Qwen3.6 35B-A3B needs about 25 GB even at Q4_K_M, so a 24 GB card would have to offload layers to system RAM at a large speed penalty.
Can a Mac run Qwen3.6 35B-A3B?
Yes — Apple Silicon with 36 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~27 GB) runs Qwen3.6 35B-A3B at Q4_K_M.
Related
VRAM calculator for any model ·Token counter
Last updated 2026-08-03. Architecture figures from the model's published config.json; see themethodology.