Better inference for long-horizon agents.
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
Built to compress.
Engineered to
reason.
Four algorithmic commitments we hold across every inference pass, from prefill to autoregressive decoding.
Lossless KV-compression, 16x factor
Built with adaptive attention subspace projection backed by zero-perplexity-drift guarantees.
10M+ long-horizon context, sustained
Proven stability across million-step agentic execution without degradation or attention sink collapse.
Zero-overhead kernel, Bare-metal CUDA
Hand-tuned FlashAttention-3 and Triton kernels engineered for H100, B200, and TPU v5p clusters.
Needle-in-haystack recall, 100% deterministic
Designed so no critical instruction or faint semantic dependency is ever dropped in the context abyss.
Intelligenceshouldscalewiththeproblem,
notthememorybudget.
Context is a resource to steward, not a buffer to fill. Parsim preserves the signals that matter, compresses redundant attention state, and keeps recall deterministic so reasoning can continue across longer horizons.
Protect the signal
Keep the instructions, decisions, and context that give each token its meaning.
Compress the overhead
Reduce redundant attention state before memory limits reasoning depth.
Extend the horizon
Let agents carry coherent reasoning further, with deterministic recall.
Complex context.
Parsimonious compute.
We engineer context optimization designed for frontier models: three critical bottlenecks, one unified engine.
Deterministic Memory Across 100,000 Step Loops
Autonomous coding and research agents repeatedly crash when prompt history fills standard attention buffers. Parsim retains permanent system prompt anchors while compacting historical reasoning chains.
Subspace Low-Rank Key-Value Head Projection
By dynamically identifying shared attention manifolds across decoder heads, Parsim eliminates up to 85% of redundant key-value tensors before they consume High Bandwidth Memory (HBM3e).
Triton-Accelerated Dynamic Sparse Kernel
Custom fused CUDA and Triton kernels route tokens to relevant receptive fields in sub-quadratic time, avoiding quadratic memory wall blowouts on frontier models like DeepSeek-R1 and Llama-3.3.
Cumulative tokens optimized
Firm, lossless inference tokens decoded and verified across enterprise deployments.
10M Needle retrieval fidelity
Exceeds standard attention benchmarks across 12-hop dependency chains.
KV-Cache memory reduction
Average footprint reduction across Llama-3.3 70B and DeepSeek-R1 inference clusters.
Deterministic token recall
Exact preservation of needle dependencies without catastrophic hallucination or context collapse.
Latest benchmarks.
Parsim engineers the mathematical foundation of long-horizon intelligence. A chronicle of what we have proven, published, and deployed.
Frequently Asked Questions
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