Wësche(@WescheNex1q):RED-SNOW 2.49bpw, 1 DGX Spark: 85.1 GLM-5.3-Flash 4bpw, 2 Sparks (TP2): 84.8 @ViC305 SAGE process shows in this test that almost 40% fewer bits can perform just as well or even better than a flat 4bpw quant. -Overall: 85.1 vs 84.8 -Long output: 61 vs 42 -Instructions: 97 vs 89 -Visual: 90 vs 80 -Tool use: 85 vs 78 Full Spark-Bench, 80 tests, thinking off, uncapped, zero errors. RED-SNOW is @Blackfrost_AI security fine-tune of GLM-5.3-Flash, so this is fine-tune + quant vs base + quant Engines differ (exllamav3 vs TensorFold)

Wësche

@WescheNex1q

Day time artist and night time AI enthusiast. Building & benchmarking frontier LLMs on 4x DGX Spark clusters + Mac. Creator of Vesica Studio. Houston

Joined January 2013

731 Following 3.1K Followers

Wësche@WescheNex1q

2026.10.08 03:52

RED-SNOW 2.49bpw, 1 DGX Spark: 85.1 GLM-5.3-Flash 4bpw, 2 Sparks (TP2): 84.8 @ViC305 SAGE process shows in this test that almost 40% fewer bits can perform just as well or even better than a flat 4bpw quant. -Overall: 85.1 vs 84.8 -Long output: 61 vs 42 -Instructions: 97 vs 89 -Visual: 90 vs 80 -Tool use: 85 vs 78 Full Spark-Bench, 80 tests, thinking off, uncapped, zero errors. RED-SNOW is @Blackfrost_AI security fine-tune of GLM-5.3-Flash, so this is fine-tune + quant vs base + quant Engines differ (exllamav3 vs TensorFold)