Qwen3.6 35B A3B

Alibaba releases Qwen3.6 35B A3B, a Mixture-of-Experts model with 35B total parameters and 3B active per token, featuring 256K native context and ranking #1 for agent workflows in 2026. The model achieves perfect tool-calling and zero catastrophic failures on a comprehensive agent benchmark, and is available under Apache 2.0 license.

Qwen3.6 35B A3B MoE enthusiast35B total, 3B active per token MoE: 256 experts, 8 routed + 1 shared , hybrid Gated DeltaNet + Gated Attention layers. 256K native context, extensible to ~1M. The agentic flagship - ranked 1 local model for agent workflows in 2026 perfect tool-calling, zero catastrophic failures on an 84-scenario / 16-category / 8-trial benchmark . Q4 K M ~20GB for GGUF runtimes; Q8 0 ~37GB for full precision. - 35.0B - 262k - apache 2.0 - Apr 2026 Scores Run it locally Per-quant memory needs and a static "can you run it?" reference - no rig entry required Can you run it? - reference rigs | Rig | Q4 K M | Q8 0 | |---|---|---| | NVIDIA Jetson Orin NX 16GB | | no - cloud cloud-pricing Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast =20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark. How can a 24GB GPU run a 744B model? It does not load the model into VRAM. The quantized weights e.g. ~410GB at Q4 sit in system RAM; the GPU only holds the small shared attention and router tensors and accelerates prompt processing. Because GLM 5.2 is a Mixture-of-Experts model, each token activates only ~40B of its 744B params, so llama.cpp streams just those active experts from system RAM to the GPU each token the -cmoe offload path . That makes decode speed bound by system-RAM bandwidth, not GPU bandwidth - single digits on DDR4, which is why these rigs show 3-8 t/s even though they “fit.” A bigger GPU e.g. 2x 3090 keeps more experts resident on-card and raises tok/s; a smaller GPU still runs it but pays the bandwidth tax. A 744B dense model could not run this way - only MoE’s small-active-params trick makes it possible. Aggressive quants 1-2 bit trade accuracy for size - roughly 17% accuracy loss at 2-bit vs full precision, and real long-context work often needs Q5 or Q6 even when lower quants “fit.” Formula estimates here are conservative; real tuned setups can exceed them one HN user reports ~6 tok/s on a 512GB DDR4 + 2x 3090 rig . Download options Inference cost over time Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.