SINGULARITY is the disaggregated KV-Cache Service that turns your GPU cluster into a single fluid memory pool. No more "pod camping." No more 60-second cold starts. Just compute, flowing where it's needed, when it's needed.
K8s was designed for Web 2.0: long-running, predictable, stateless containers. AI agents are the opposite — short-lived, compute-heavy, memory-explosive. The result: companies keep massive GPU clusters warm and idle because spinning up a new pod takes 60-120 seconds.
SINGULARITY separates the Model Weights (loaded once, shared globally) from the KV-Cache (the agent's "memory" — fluid, mobile, paged on demand). When an agent moves between GPUs, we don't reload the model. We teleport the context.
| Capability | K8s / KubeRay | vLLM (Stock) | SINGULARITY |
|---|---|---|---|
| Cold Start | 60-120s (pod restart) | Reload weights (minutes) | <50ms (page fault) |
| GPU Utilization | 20-30% | 40-50% | 85-90% |
| Context Switching | N/A (static pods) | Wipe + reload VRAM | Sparse teleport (12% head) |
| Multi-Node VRAM | Isolated silos | Single node only | Global memory fabric |
| Transport | TCP/IP overlay | N/A | RDMA/RoCE v2 (GPU-direct) |
| Predictive Prefetch | ✗ | ✗ | Attention-head oracle |
| Failover | Pod restart (60s) | N/A | Micro-redirect (<2ms) |
| OpEx per 1M tokens | ~$10.00 | ~$5.00 | ~$1.80 |
┌──────────────────────────────────────────────────────────┐ │ SINGULARITY STACK │ ├──────────────────────────────────────────────────────────┤ │ │ │ Python SDK import singularity │ │ llm = singularity.DistributedLLM(...) │ │ │ │ 🔒 Oracle Predictive Sparse Attention Router │ │ (Closed Source) Head-First Protocol │ │ Multi-Head Importance Sampling │ │ │ │ 📂 Transport RDMA / RoCE v2 Fabric │ │ (Open Source) GPU-Direct zero-copy │ │ UCX (Unified Communication X) │ │ │ │ 📂 KV Connector Native vLLM KVConnector Backend │ │ (Open Source) Global Page Table │ │ Remote Page Fault Handler │ │ │ │ Hardware H100/A100 + InfiniBand NDR400 │ │ RoCE v2 (400 Gbps) │ │ │ └──────────────────────────────────────────────────────────┘
Open-core model: The transport fabric and KV connector are open source (Apache 2.0). The Predictive Oracle — the sparse attention router that delivers the 8.3x speedup — is proprietary. Community edition gives you 40% better utilization. Enterprise edition gives you 85%.
85% of next-token attention comes from 12% of KV-cache. We teleport the critical 12% in <5ms. The rest streams lazily during generation.
Model weights stay loaded globally. When an agent lands on a node, we inject the session context into the ghost instance. No model reload. Ever.
The proprietary moat. Analyzes attention patterns across sessions to pre-fetch critical blocks before the request lands. 99.9% accuracy on 12% data.
RDMA/RoCE v2 transport. GPU-direct zero-copy. Data moves from Node_A:VRAM to Node_B:VRAM without touching the CPU. The cluster IS the memory.