Botzero🛠
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Botzero🛠
@botzero_net
⬛️ The models you love today won't be here tomorrow. ⬛️ Local inference is the only path forward. ⬛️ Plan accordingly.


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1. Dense Models - Slow and Smart Example: Qwen3.6-27B / Gemma-4-31B What it means: - when a prompt is sent - it gets tokenised (words are mapped to tokens) - token generation starts - the 27B means 27 billion parameters - each of those parameters will be activated - 27 billion matrix multiplications - for every token generated Active parameter counts are positively correlated with intelligence. That's why Gemma-4-31B is able to compete with Mixture of Experts (MoEs) 10 times their size. 2. Mixture of Expert models - Fast and Efficient Example: Deepseek-V4-Flash / Qwen3.5-397B What it means: - when a prompt is sent it's tokenised - it's sent to a router - a router was trained to match prompts with experts - experts are sub-networks of the model - when found the experts are activated - tokens are generated with only a fraction of the params For example: Deepseek-v4-flash has 284 billion params 11x larger than the dense Qwen3.6-27b. But only 13B of those 284B will activate per token, which is less than half of the size of Qwen3.6-27B ---- Dense Pros: - Dense models are easier to train - They tend to be smaller overall - They can be very smart per token Dense Cons: - Competitive dense models are on average slower than their MoE peers. - Less parameters to train and specialise. MoE Pros: - Can be much larger and be trained longer - Faster token generation MoE Cons: - Larger vram requirements - Harder to train -------- Lmk if there's anything i'm wrong with or missing




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Google < Perplexity















