Verified75% confidenceFactExact time
The model weights alone of a 175-billion-parameter model (in BF16 precision) require 350 GB of storage, and the Adam optimizer must maintain two sets of states—first-order moments and second-order moments—totaling more than 1 TB.
3
Sources
75%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
非均匀张量并行：NVIDIA提升LLM训练Goodput的容错新方案
rss7/6/2026
Related Claims
VerifiedUsing float16 precision as a baseline, each parameter occupies approximately 2 bytes, meaning a 7B model requires approximately 14GB of VRAM/memory, and a 70B model requires approximately 140GB.82% similarUnverifiedInferencing a 1T-parameter MoE model requires hundreds of gigabytes of VRAM even with BF16 half-precision storage, far exceeding the capacity limit of consumer-grade GPUs.77% similarVerifiedIn FP16 precision, large models require approximately 2 GB of VRAM per 1 billion parameters.77% similarUnverifiedEven with the most aggressive quantization, a model with a total of 177B parameters still occupies approximately 72.5GB.76% similarVerifiedA model with 70 billion parameters that originally requires 80 GB of VRAM requires only about 40 GB of memory after 4-bit quantization (Q4_K_M level), with inference quality loss typically within 3%–8% on most benchmarks.75% similar
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