Unverified50% confidenceFactExact time
This experiment was configured with a native context of 262,144 tokens, the KV cache stored in FP8, and a maximum of two concurrent sequences.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/14/2026
First Seen
Valid until: 1/12/2027
Sources
Related Entities
Related Claims
VerifiedTaking the LLaMA-2 70B model as an example, a sequence of 2048 tokens requires approximately 4 GB of VRAM for the KV cache alone.75% similarUnverifiedFP8 KV cache is a quantization technique that compresses the Key-Value cache in the attention mechanism from FP16 or BF16 to 8 bits, which can halve the GPU memory usage of the KV cache74% similarUnverifiedFor a 32-layer, 4096-dimensional model, the KV Cache for processing 2048 tokens occupies approximately 1 GB of VRAM73% similarVerifiedTaking LLaMA-2 70B as an example, when processing a 4096-token context with a batch size of 32, the KV Cache can consume over 80 GB of memory.72% similarUnverifiedFor the LLaMA-2 70B model, when processing a context of 4096 tokens, the KV Cache requires approximately 20 GB of system memory at FP16 precision.71% similar
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