Mixtral
MoE Models Explained: Why Mixtral Uses 46B Parameters But Runs Like 13B
Mixture of Experts explained for local AI — why MoE models run fast but still need full VRAM. Mixtral, DeepSeek V3, DBRX compared with dense model alternatives.
Mixtral VRAM Requirements: 8x7B and 8x22B at Every Quantization Level
Mixtral 8x7B has 46.7B params but only 12.9B activate per token. You still need VRAM for all 46.7B. Exact VRAM for every quant from Q2 to FP16.
Mixtral 8x7B & 8x22B VRAM Requirements
Mixtral 8x7B and 8x22B VRAM requirements at every quantization level. Exact numbers from Q2 to FP16, GPU recommendations, and KV cache impact explained.
Are Mistral Models Still Worth Running? Only Nemo 12B (Here's Why)
Mistral led local AI in 2024. In 2026, Qwen 3 and Llama 3 have passed them on most benchmarks. The exception: Mistral Nemo 12B with 128K context still earns its slot. What's worth running, what's been replaced, and when to pick Mistral over the competition.