Expired50% confidenceFactTime unknown
For million-token-level contexts, KV Cache can consume tens or even hundreds of gigabytes of GPU memory, becoming the primary bottleneck for long-context inference.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
DeepSeek V4 Deep Technical Breakdown: Million-Token Context and Extreme Cost Efficiency
bilibili码书z
Related Claims
UnverifiedTraditional full-parameter fine-tuning for models with tens of billions of parameters requires hundreds of gigabytes of GPU memory79% similarVerifiedThe VRAM usage of KV Cache grows linearly with sequence length; for a 70-billion-parameter model using GQA, the KV Cache usage can reach tens of GB when processing 100,000 tokens.75% similarUnverifiedWhen models with hundreds of billions of parameters process a 128K context window, KV Cache can occupy tens of gigabytes of VRAM.73% similarUnverifiedThe VRAM consumption of the KV cache is proportional to context length, number of model layers, and number of attention heads; under an 8K context, a typical 27B model may consume an additional 2–4 GB of VRAM.71% similarUnverified长上下文在首次推理时需完整计算KV Cache，消耗GPU计算资源并占用显存带宽，拉长首token延迟（TTFT）71% similar
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