Unverified50% confidenceFactExact time
Models such as Llama-3.1-8B are built on a stacked Transformer decoder architecture, typically containing 32 decoder layers, with approximately 250 million parameters per layer and approximately 500MB of VRAM per layer at FP16 precision.
1
Sources
50%
Confidence
Long-term
Relevance
8/10/2026
First Seen
Sources
Soup CLI：4GB显卡微调80亿参数大模型的开源工具详解
Related Claims
UnverifiedLLaMA-2 70B 模型共有 80 个 Transformer 层，每层约 8.75 亿参数，FP16 精度下单层约占 1.75GB 显存79% similarUnverifiedLlama 3 8B has 32 layers, 8 KV heads (GQA architecture with 32 query heads), a head dimension of 128, and a KV cache of approximately 1.07 GB for a single sequence under FP16 with an 8192 context length.69% similarUnverifiedWhen using FP16 precision, the KV Cache for LLaMA-2-7B occupies approximately 0.5 MB per token.69% similarVerified以LLaMA-3 70B（80层、64头、每头128维）为例，单个token的KV Cache约占2.6MB（FP16精度），32K上下文的KV Cache总量约达83GB68% similarUnverifiedFor a typical 7B model (32 layers, 32 heads, 128 dimensions) processing a 2048-token sequence at FP16 precision with a batch size of 8, the KV Cache consumes approximately 4 GB of VRAM.68% similar
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