Mistral just open-sourced Mixtral-8x22B under Apache 2.0 - 22B params, runs on a single RTX 4090, and crushes proprietary models at 1/10th the cost.
Tired of $20/hour API bills for ‘enterprise-grade’ models that underperform? Mistral just handed developers the nuclear option: Mixtral-8x22B, fully open weights, zero restrictions.
Announced this morning, Mixtral-8x22B leverages sparse MoE architecture with 44B total params but only activates 22B per inference - delivering GPT-4 level performance on MMLU (88.7%) while sipping 30% less VRAM than dense competitors. HF download spiked 500k in hours; it’s optimized for vLLM and TGI serving.
This hits prime time for production apps: chatbots costing pennies per query, local RAG on customer data, edge inference on laptops. Self-host your entire SaaS backend without vendor lock-in or data leaks. French engineering at its finest.
Stack it against Llama 3.1 70B (similar quality, double the memory) or DeepSeek-V2 (close but less polished tokenizer). Mistral leapfrogs the open pack while Meta plays catch-up. The MoE race just heated up.
Grab it from Hugging Face, spin up with vllm serve mistralai/Mixtral-8x22B --quantize q4. Benchmark against your current stack. Then scale: what’s stopping you from replacing 80% of your OpenAI spend tomorrow?
Source: Mistral AI