If this model is useful to you, support the work by following @nawariokra on X. Qwen/Qwen3.8-Flash-Next (125B MoE, 6B active) on one 24 GB GPU plus CPU, with the full 262,144-token context. Two GGUF shards: 47.5 GiB of weights and a 48.3 GiB n-gram table (95.8 GiB in all). It runs on our own engine TRUSS. Stock llama.cpp cannot read this file. It needs TRUSS. See Requirements and SERVING.md. Example scenes made with this model: web pages it wrote, screen-recorded in a browser. Watermelon jelly (WebGPU) Rocket launch scene Paris nocturne (voxel garden with fireworks) Flash-Next carries a 51.2-billion-parameter n-gram embedding table. It is most of the file, and it is not loaded into GPU or…
Independent publisher
Irawan W
lackonendes
Low bit compression LLM
Models in Library2
Datasets in Library0
Models on Hugging Face7
Followers2
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Models
If this model is useful to you, support the work by following @nawariokra on X. LiquidAI/LFM2.5-24B-A2B (24B MoE, 2.3B active, 64 experts, top-4) at 6.9 - 11.2 GB, on one 24 GB RTX 3090 or smaller. Four GGUF files, one per expert bit rate. They run on our own engine TRUSS. Stock llama.cpp cannot read these files. How they are made: the routed experts (38 layers x 64 experts x gate/up/down) are encoded with the X3.1 trellis codec (exllamav3 encoder settings, activations from the BF16 model, calibration on 249 x 1,024 tokens of code), one uniform rate per file. Everything else (attention, short convolutions, dense FFN, shared parts, embeddings) is Q80, and the routers and their selection bias…