Better inference for long-horizon agents.

>_
WITH PARSIM
quickstart.py
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# pip install parsim-attention
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import parsim
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model = parsim.wrap(model, horizon="10M")
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# KV cache: 960 GB → 58.5 GB
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out = model.generate(prompt)
960 GB58.5 GB16.4×
RECALL99.98%

Parsim compresses, optimizes, and scales attention state for frontier LLMs, autonomous agents, and multi-turn reasoning chains. 99.984% semantic fidelity, measured at 10M tokens

Access Parsim
RECALL P95
99.984%
MAX HORIZON
10.2M TOKENS
PARSIM ENGINE v2.8///
10.2M CONTEXT LOSSLESS///
16.4X KV REDUCTION///
SUB-QUADRATIC ATTENTION///
99.98% RECALL FIDELITY///
BARE-METAL CUDA WHEELS///
42.8T TOKENS VERIFIED///
ZERO PERPLEXITY DRIFT///
PARSIM ENGINE v2.8///
10.2M CONTEXT LOSSLESS///
16.4X KV REDUCTION///
SUB-QUADRATIC ATTENTION///
99.98% RECALL FIDELITY///
BARE-METAL CUDA WHEELS///
42.8T TOKENS VERIFIED///
ZERO PERPLEXITY DRIFT///