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.
UL-SMF is dual-licensed:
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.
📩 For Enterprise Licensing Inquiries: inquiries@lawrencearchitectures.com
| Metric | Raw FP32 Cache | UL-SMF 16D Latent | Improvement |
|---|---|---|---|
| VRAM Footprint (4096 tokens) | |||
| 48.00 MB | 0.12 MB | 384x Reduction | |
| VRAM Saved / Block | |||
| — | 47.88 MB | ||
| 99.7% Memory Saved | |||
| Semantic Retention | |||
| 100% | 94.15% - 95.84% | ||
| Cosine Similarity | |||
| Pipeline Latency | |||
| — | ~14.1 ms - 19.6 ms | ||
| CUDA Event Verified |
UL-SMF dynamically maps any model hidden dimension (Mistral, Llama, Qwen, etc.) on-the-fly using orthogonal projection:
import torch
from ul_smf import UniversalLatentBridge
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
oracle_core = torch.jit.load("aegis_kv_oracle_core.pt", map_location=device)
oracle_core.eval()
ul_smf_bridge = UniversalLatentBridge(core_module=oracle_core, core_dim=3072).to(device)
kv_cache_tensor = torch.randn(1, 32, 4096, device=device)
reconstructed_cache, compressed_latents = ul_smf_bridge(kv_cache_tensor)
print(f"Compressed down to latent space: {compressed_latents.shape}")