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qwen3.8-flash-next-gyro-s
Qwen3.8-Flash-Next, a 125B-parameter hybrid mixture-of-experts model, in agentionai's Gyro-S quantization (trellis-coded experts), the smallest option, for machines with little memory and lower in quality (about 59 GB to download). It reads images through the vision projector published for the original model, and the Strata backend parses its reasoning and tool calls. Needs about 28 GB of VRAM and RAM together for the weights. Half of the file is the n-gram table, which stays on disk and is read for every token, so keep it on a fast local SSD. Needs a GPU the strata backend supports; NVIDIA with 12 GB of VRAM or more is the class Strata tests. Runs only on the experimental strata backend. On the first load the strata backend builds the Strata pack from the GGUF files (a few minutes, a few GB of extra disk); a finished pack is never rebuilt. The experts are read from the GGUF files in place (mapped); add --resident-experts to engine_args to copy them into RAM when there is room. Licensed under the Qwen Community License 1.0 (inherited from Qwen/Qwen3.8-Flash-Next): free use, but a Model as a Service or AI Work Assistant business needs a licence from Qwen. Read the LICENSE file in the repository first. The GGUF file is the Gyro quantization by agentionai; the vision projector is ISTA-DASLab's file for the original model; Strata is by Niko1221. The MTP draft layer is not configured here.

Repository: localaiLicense: qwen-community-1.0

qwen3.8-flash-next-gyro-m
Qwen3.8-Flash-Next, a 125B-parameter hybrid mixture-of-experts model, in agentionai's Gyro-M quantization (trellis-coded experts), about IQ3_XXS quality with about 20% less GPU memory, 16 to 21% slower than Q2_0 (about 93 GB to download). It reads images through the vision projector published for the original model, and the Strata backend parses its reasoning and tool calls. Needs about 35 GB of VRAM and RAM together for the weights. Half of the file is the n-gram table, which stays on disk and is read for every token, so keep it on a fast local SSD. Needs a GPU the strata backend supports; NVIDIA with 12 GB of VRAM or more is the class Strata tests. Runs only on the experimental strata backend. On the first load the strata backend builds the Strata pack from the GGUF files (a few minutes, a few GB of extra disk); a finished pack is never rebuilt. The experts are read from the GGUF files in place (mapped); add --resident-experts to engine_args to copy them into RAM when there is room. Licensed under the Qwen Community License 1.0 (inherited from Qwen/Qwen3.8-Flash-Next): free use, but a Model as a Service or AI Work Assistant business needs a licence from Qwen. Read the LICENSE file in the repository first. The GGUF file is the Gyro quantization by agentionai; the vision projector is ISTA-DASLab's file for the original model; Strata is by Niko1221. The MTP draft layer is not configured here.

Repository: localaiLicense: qwen-community-1.0

trellis2-4b
Microsoft TRELLIS.2-4B image-to-3D generation via trellis2.cpp (C++/ggml). Generates a textured 3D mesh (binary glTF / GLB with PBR materials) from a single conditioning image. This is the full pipeline: coarse preview, 512 fine dual-grid, 1024 cascade, and PBR texturing (~18 GB of f16 GGUFs; the 1024 textured path needs roughly 10 GB VRAM).

Repository: localaiLicense: mit

trellis2-4b-geometry
Microsoft TRELLIS.2-4B image-to-3D generation via trellis2.cpp (C++/ggml) — geometry-only variant (~7 GB): coarse preview plus the 512 fine dual-grid mesh, without the 1024 cascade or PBR texturing. The backend detects the reduced model set automatically; install trellis2-4b instead for textured output.

Repository: localaiLicense: mit