{"slug": "research-code-verified-384x-kv-cache-compression-with-linear-complexity-using-ul", "title": "[Research/Code] Verified 384x KV-Cache Compression with Linear Complexity using Finite Scalar Quantization (UL-SMF)", "summary": "A developer has open-sourced the Unified Latent-State Memory Fabric (UL-SMF), a hardware-software co-designed architecture that compresses standard FP32 KV caches into a 16-dimensional discrete latent space using Finite Scalar Quantization (FSQ), achieving a 384x compression ratio (reducing a 48 MB FP32 block to 0.12 MB) with 94.15% to 95.84% cosine similarity retention on real LLM attention tensors and 14.1 ms to 19.6 ms end-to-end CUDA event verified latency on a CUDA T4 GPU. The self-contained Python package allows monkey-patching standard Transformer attention layers with two lines of code, and is dual-licensed under AGPLv3 with commercial options.", "body_md": "Hey everyone,\n\nAs context windows scale to 128k+ tokens, KV cache VRAM consumption becomes a massive bottleneck for real-time inference.\n\nI’ve open-sourced the Unified Latent-State Memory Fabric (UL-SMF), a hardware-software co-designed architecture that compresses standard FP32 KV caches into a 16-dimensional discrete latent space using Finite Scalar Quantization (FSQ).\n\nLive Benchmark Results (CUDA T4 / FP32):\n\n• Compression Ratio: 384x reduction (Reduces a 48 MB FP32 block to 0.12 MB)\n\n• Semantic Retention: 94.15% - 95.84% Cosine Similarity on real LLM attention tensors\n\n• Pipeline Latency: ~14.1 ms to 19.6 ms end-to-end CUDA event verified latency\n\nThe self-contained Python package allows you to monkey-patch standard Transformer attention layers with two lines of code.\n\nTechnical Specification (Zenodo DOI): [Live Verification: The Unified Latent-State Fabric: Resolving the Inference Memory Wall via GLRP v2.0 and Aegis-KV | Zenodo](https://zenodo.org/records/21896117)\n\nFeedback, testing, and benchmarks on longer context models are welcome! Dual-licensed under AGPLv3 with commercial options available.", "url": "https://wpnews.pro/news/research-code-verified-384x-kv-cache-compression-with-linear-complexity-using-ul", "canonical_source": "https://discuss.huggingface.co/t/research-code-verified-384x-kv-cache-compression-with-linear-complexity-using-finite-scalar-quantization-ul-smf/178623#post_1", "published_at": "2026-08-13 09:48:47+00:00", "updated_at": "2026-08-13 10:12:14.778269+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-infrastructure", "ai-research", "ai-tools"], "entities": ["Unified Latent-State Memory Fabric (UL-SMF)", "Finite Scalar Quantization (FSQ)", "Zenodo", "CUDA T4", "AGPLv3"], "alternates": {"html": "https://wpnews.pro/news/research-code-verified-384x-kv-cache-compression-with-linear-complexity-using-ul", "markdown": "https://wpnews.pro/news/research-code-verified-384x-kv-cache-compression-with-linear-complexity-using-ul.md", "text": "https://wpnews.pro/news/research-code-verified-384x-kv-cache-compression-with-linear-complexity-using-ul.txt", "jsonld": "https://wpnews.pro/news/research-code-verified-384x-kv-cache-compression-with-linear-complexity-using-ul.jsonld"}}