On July 23, Multiverse Computing announced that its CompactifAI compression technology — derived from its quantum-software background — runs Meta's Llama 3.3 70B at 3.86 tokens/s output throughput on Intel Xeon 6 Performance-core servers, a 93.6% improvement over the uncompressed baseline; at 256 concurrent sessions, throughput improves 107%, latency drops 51.7%, disk footprint shrinks from 130 GiB to 65 GiB, and accuracy retention is over 97%. The solution also supports Llama 4 Scout, DeepSeek R1, Mistral Small 3.1 and other open-source flagships, paired with vLLM CPU and AMX matrix extensions, adding a hard-currency option to the "run big models without a GPU" private-deployment scenario. The technical highlight isn't "another INT4 compression" — it's the "healing" retraining after compression, which digests the quantization's accuracy loss through a targeted continuation training pass, and on benchmarks like WinoGrande the compressed model actually scores 6.86% higher than the original. This counter-intuitive "tighter and more accurate" result is a representative sample of the past year's shift in LLM inference optimization: from purely dropping precision to "compression + repair" joint optimization. For Chinese teams working on edge, CPU inference, and private deployment, this route has more engineering real-world significance than pure-GPU inference: the same Xeon server doubles capacity and concurrency, without betting on new card supply, while keeping the vast majority of Q&A quality.