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// 78d agoNEWS
LocalLLaMA weighs Q2 GLM-5 against Q8 rivals
A LocalLLaMA thread asks whether a heavily quantized GLM-5 model at roughly 241GB can still beat similarly sized but less quantized MiniMax M2.5 and Qwen3.5 variants. The post itself offers no benchmark data, and the early replies mostly argue that Q4/Q8 models are the safer quality bet, especially for long-context use.
// ANALYSIS
This is useful practitioner chatter, not a meaningful result yet: it surfaces the right memory-budget tradeoff, but there is almost no evidence beyond informed guesses.
- –The core debate is whether sheer parameter count can overcome the quality loss from pushing a giant model down to Q2.
- –Early commenters lean toward Qwen3.5 MXFP4 or MiniMax M2.5 Q8 as more reliable choices than a Q2 GLM-5.
- –One counterpoint in the thread is that ultra-large models can sometimes quantize surprisingly well, so GLM-5 might still beat MiniMax on some workloads.
- –The most actionable takeaway is that anyone spending 240GB-class memory should run task-specific evals instead of trusting parameter count alone.
// TAGS
glm-5minimax-m2-5qwen3-5llmopen-weightsinference
DISCOVERED
78d ago
2026-03-10
PUBLISHED
80d ago
2026-03-08
RELEVANCE
6/ 10
AUTHOR
ImpressiveNet5886