Aggressive quantization reduces hardware requirements for very large open models, but runtime support and evaluation metrics are lagging. This creates a gap between theoretical efficiency and practical local deployment.
ISTA-DASLab's release of GGUF quantized weights for DeepSeek-V4.1-Flash-1017 is not just a quantization milestone. It exposes the real bottleneck in local deployment: runtime integration. With llama.cpp still lacking official support and references p...
KLD is often treated as a universal metric for ranking quantized models, but it only measures the average divergence between the probability distributions of the base model and the quantized one. The number is fragile: it depends on the dataset, is e...
ISTA DASLab has released three GGUF builds of DeepSeek-V4.1-Flash-1017 with GSQ and RCO quantization, at 1.75 to 2.25 bits per weight over the 551.9 billion backbone parameters. A shared 110.6 GB Engram file stays memory-mapped on disk. The model run...
ISTA-DASLab has published GGUF quants of DeepSeek-V4.1-Flash-1017 using the GSQ-RCO quantization approach. However, llama.cpp does not yet support the architecture: the relevant pull requests are still open. The model card mentions Strata, but the or...
