Unverified50% confidenceFactExact time
以FP32存储一个拥有1亿参数的模型需要约400MB内存
1
Sources
50%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
AI Application Engineer Interview Guide: Core Topics in Model Quantization and Inference Deployment
redditr/learnmachinelearning7/9/2026
Related Claims
VerifiedIn FP16 precision, large models require approximately 2 GB of VRAM per 1 billion parameters.77% similarUnverifiedFor 1 million tokens, the KV Cache alone can consume hundreds of GB of VRAM at FP16 precision, far exceeding the 80 GB capacity limit of a single A100/H100.75% similarUnverifiedFor a typical 70-billion-parameter model, the KV cache occupies approximately 0.5 MB of memory per token, and generating 100,000 tokens requires carrying 50 GB of cache.73% similarUnverifiedQuantization can compress the storage size of a 70-billion-parameter model from approximately 140 GB in FP16 to approximately 35–40 GB in INT4, enabling it to run on consumer-grade hardware such as a single RTX 4090 with CPU memory offloading.73% similarPartially VerifiedA standard FP32 model occupies 4 bytes per parameter, while INT4 quantization compresses this to 0.5 bytes, theoretically achieving an 8x memory saving.72% similar
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