{"slug": "ul-smf-open-source-linear-complexity-kv-cache-compression", "title": "UL-SMF – Open-source linear-complexity KV-cache compression", "summary": "Lawrence Architectures released UL-SMF, an open-source KV-cache compression fabric that reduces memory footprint by up to 384x with over 94% semantic retention, addressing the memory bottleneck in long-context Transformer inference. The dual-licensed software (AGPLv3 for non-commercial use, commercial license for proprietary deployments) dynamically maps any model hidden dimension using Finite Scalar Quantization and 16-dimensional latent mapping, achieving 99.7% memory savings per block.", "body_md": "The **Unified Latent-State Memory Fabric (UL-SMF)** is a hardware-software co-designed memory compression fabric that solves the memory bottleneck in long-context Transformer inference. By combining **Finite Scalar Quantization (FSQ)** with dynamic 16-dimensional latent mapping, UL-SMF compresses Key-Value (KV) cache tensors by up to **384x** while maintaining **>94% semantic retention**.\n\n**UL-SMF is dual-licensed:**\n\n**Open Source (AGPLv3):** Free for non-commercial research, academic use, and open-source projects.*Note: The AGPLv3 license requires any network-accessible service using this software to open-source its entire backend application code.***Commercial Enterprise License:** Required for proprietary commercial deployments, closed-source SaaS platforms, and enterprise data center infrastructure. Commercial licenses grant full rights without AGPLv3 copyleft restrictions, plus integration support.\n\n📩 **For Enterprise Licensing Inquiries:** `inquiries@lawrencearchitectures.com`\n\n| Metric | Raw FP32 Cache | UL-SMF 16D Latent | Improvement |\n|---|---|---|---|\nVRAM Footprint (4096 tokens) |\n48.00 MB | 0.12 MB | 384x Reduction |\nVRAM Saved / Block |\n— | 47.88 MB |\n99.7% Memory Saved |\nSemantic Retention |\n100% | 94.15% - 95.84% |\nCosine Similarity |\nPipeline Latency |\n— | ~14.1 ms - 19.6 ms |\nCUDA Event Verified |\n\nUL-SMF dynamically maps **any** model hidden dimension (Mistral, Llama, Qwen, etc.) on-the-fly using orthogonal projection:\n\n``` python\nimport torch\nfrom ul_smf import UniversalLatentBridge\n\n# 1. Load your compiled Aegis-KV oracle core binary\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\noracle_core = torch.jit.load(\"aegis_kv_oracle_core.pt\", map_location=device)\noracle_core.eval()\n\n# 2. Wrap it with the Universal Dynamic Bridge (auto-adapts to any model size)\nul_smf_bridge = UniversalLatentBridge(core_module=oracle_core, core_dim=3072).to(device)\n\n# 3. Seamlessly compress any model hidden dimension (e.g., 4096 for Llama/Qwen)\nkv_cache_tensor = torch.randn(1, 32, 4096, device=device)\nreconstructed_cache, compressed_latents = ul_smf_bridge(kv_cache_tensor)\n\nprint(f\"Compressed down to latent space: {compressed_latents.shape}\")\n```\n\n", "url": "https://wpnews.pro/news/ul-smf-open-source-linear-complexity-kv-cache-compression", "canonical_source": "https://github.com/liventruth/UL-SMF-Cache-Compression", "published_at": "2026-08-17 15:56:14+00:00", "updated_at": "2026-08-17 16:11:32.201740+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-infrastructure", "ai-tools"], "entities": ["Lawrence Architectures", "UL-SMF", "Finite Scalar Quantization", "AGPLv3", "Mistral", "Llama", "Qwen"], "alternates": {"html": "https://wpnews.pro/news/ul-smf-open-source-linear-complexity-kv-cache-compression", "markdown": "https://wpnews.pro/news/ul-smf-open-source-linear-complexity-kv-cache-compression.md", "text": "https://wpnews.pro/news/ul-smf-open-source-linear-complexity-kv-cache-compression.txt", "jsonld": "https://wpnews.pro/news/ul-smf-open-source-linear-complexity-kv-cache-compression.jsonld"}}