Detailed Introduction
Tensor Fusion is a GPU-cluster focused virtualization and pooling solution designed to improve cluster utilization and reduce inference latency through fine-grained resource allocation and shared memory/computation. The project targets high inference density and multi-tenant environments, offering dynamic scheduling and autoscaling to run long-lived inference services and agent clusters on the same physical infrastructure.
Main Features
- Dynamic GPU pooling: partition physical GPUs into shareable virtual pools allocated to inference tasks on demand.
- Low-latency inference path: optimize context loading and memory reuse to reduce cold starts and model-switch overhead.
- Autoscaling & scheduling: real-time scale and schedule tasks based on load and priority.
- Multi-model and multi-tenant support: solid isolation and concurrency handling for LLM and agent workloads.
Use Cases
- Large-scale LLM inference platforms, improving concurrent throughput and lowering operational cost.
- Service-oriented multi-model deployments that require hot model switching and memory reuse.
- Hybrid edge-cloud deployments that need an efficient inference runtime for long-running agents.
Technical Features
- Kernel and user-space cooperative scheduling to minimize context-switch overhead.
- Kubernetes integration with compatibility for common schedulers and autoscaling components.
- Memory sharding and reuse techniques to improve memory efficiency and reduce fragmentation.
- Observability interfaces for monitoring GPU utilization, memory usage, and inference latency.