Expired50% confidenceFactExact time
The computational complexity of liquid convolution scales linearly with sequence length at O(n), rather than the quadratic O(n²) of self-attention, resulting in approximately 1/1000 the computation of attention layers when processing 128K tokens.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
LFM2.5 Local Deployment Hands-On: An 8B Parameter Model That Outperforms GPT-o3s in Tool Calling
bilibiliAGI_Ananas6/1/2026
Related Claims
Unverified传统NMS的时间复杂度为O(N²)，YOLOv11对于640×640输入会产生8400个候选框，密集场景下NMS计算量增加64% similarUnverifiedTaking a 7B parameter model as an example, each additional 1K tokens of context consumes approximately 0.5–1 GB of GPU memory.62% similarUnverifiedFor a typical 7B model with 32 layers and 32 attention heads, storing the KV Cache for each token requires approximately 0.5 MB of VRAM, so generating 1,000 tokens requires an additional ~500 MB of VRAM61% similarUnverifiedUsing 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.61% similarUnverifiedA unified memory pool of 128GB means that quantized models at the 70-billion-parameter scale or larger can be run locally.61% similar
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