About Qwen3.5 397B-A17B
Qwen3.5 397B-A17B is Alibaba's 397B-parameter model released in February 2026, with a 256K-token context window. It is a mixture-of-experts model: all 397B parameters must sit in memory, but only ~17B 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 0.3 GB at an 8K context, 4.0 GB at 128K, and 8.1 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 Qwen3.5 397B-A17B need?
At Q4_K_M with an 8K context, Qwen3.5 397B-A17B needs about 268 GB (weights 242 GB + KV cache + overhead). The smallest common hardware that fits is a Apple Silicon 512GB unified.
Can an RTX 4090 (24GB) run Qwen3.5 397B-A17B?
Not fully in VRAM. Qwen3.5 397B-A17B needs about 268 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 Qwen3.5 397B-A17B?
Yes — Apple Silicon with 512 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~384 GB) runs Qwen3.5 397B-A17B 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.