SixVolts

Model · Text generation

GLM-5.3-ewaste-edition-GGUF

SixVolts

This is a quantization of GLM-5.3 that is optimized to work better on older hardware. This quant was built using i-matrix GGUF quantizations with the same principle as the GLM-5.2 e-waste edition: K-quants for the routed experts, not codebook i-quants, because on pre-AVX-512 CPUs and previous-gen datacenter GPUs (MI100 / gfx908) expert dequantization, not bandwidth, is the decode bottleneck. GLM-5.3 is GLM-5.2 with further post-training (identical architecture and config), so the 5.2 recipes carry over tensor for tensor. All are imatrix-quantized with unsloth's GLM-5.3 importance matrix (209 chunks × 3584 tokens). The per-tensor types are the 5.2 files' types read from their GGUF headers…

Model · Text generation

GLM-5.3-Flash-ewaste-edition-GGUF

SixVolts

This is a quantization of GLM-5.3-flash that is optimized to work better on older hardware. This quant was built using i-matrix GGUF quantizations with the same principle as the GLM-5.2 and GLM-5.3 e-waste editions: K-quants for the routed experts, no codebook i-quants, because on pre-AVX-512 CPUs and previous-generation datacenter GPUs (MI100 / gfx908) expert dequantization, not bandwidth, is the decode bottleneck. Format: mainline llama.cpp glm5-next (merged 2026-09-30, ggml-org/llama.cpp#27773). These files do not load in builds that only know the earlier glm5next arch from the unmerged PR #27754 (unsloth's September files are that format). Any llama.cpp from 2026-10 on loads them. The…