8x22B params, MoE magic – runs inference at 150 tokens/sec on an A100, beating Llama 3.1 405B.
Tired of waiting hours for Llama inference on your laptop? Mistral just fixed that with Mixtral-8x22B, the open source model that squeezes monster performance into consumer hardware.
Released under Apache 2.0 today, Mixtral-8x22B uses sparse Mixture-of-Experts to activate just 44B params per token while delivering 88% of GPT-4o’s quality. It crushes MMLU (87.2%), GSM8K (96%), and runs at 150+ tokens/sec on a single H100 – that’s 3x faster than dense 70B models.
Developers, this is your production RAG agent dream: quantize to 4-bit, deploy on 24GB VRAM, and handle 10k+ QPS. Fine-tune it on your domain data in hours, not days. No more begging for cloud credits.
Llama 3.1 405B needs 800GB+ for full precision; Mixtral does better math on one GPU. DeepSeek-V3 (similar MoE) trails on multilingual tasks, while closed models lock you into APIs. Mistral’s betting big on open weights to dominate edge AI.
Hugging Face weights are live – mistralai/Mixtral-8x22B-v1. Spin up a local Ollama instance and benchmark against your stack. Can open source finally kill the API middleman?
Source: Mistral AI