Unverified50% confidenceFactExact time
AWQ和GGUF均能将700亿参数模型存储体积从FP16的约140GB压缩至INT4的约35-40GB，可在单张RTX 4090上运行，精度损失通常控制在1-3%以内
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Alibaba Bans Claude Code: The Battle Over AI Coding Tool Security and Data Sovereignty
bilibiliSynthMindX7/4/2026
Related Claims
Unverified量化可将700亿参数模型的存储体积从FP16的约140GB压缩至INT4的约35-40GB，能在单张RTX 4090配合CPU内存卸载的消费级硬件上运行83% similarUnverifiedThe GGUF format supports multiple precision levels such as 4-bit and 8-bit, and can compress the memory footprint of a 7-billion-parameter model from approximately 14 GB (FP16) to approximately 4 GB (Q4_K_M quantization), with inference accuracy loss typically within 1–3%.81% similarUnverifiedOllama compresses model parameters from FP16 to INT4/INT8 precision via the GGUF quantization format, enabling models that originally required tens of gigabytes of VRAM to run on ordinary computers with 8–16 GB of memory.77% similarUnverifiedQuantization formats such as GGUF and AWQ can compress models with tens of billions of parameters to a size runnable on consumer-grade GPUs while maintaining near-original performance.76% similarUnverifiedA 70B parameter model quantized with Q4_K_M occupies approximately 40GB of storage, and can achieve an inference speed of 10–20 tokens per second on an RTX 4090 with CPU memory offloading.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/489132API
curl https://kongchang.com/api/v1/knowledge/claims/489132MCP
get_claim(id=489132)