Production-Ready LLM Infrastructure
Deploy GPU-accelerated LLM inference with Kubernetes-native orchestration. From air-gapped environments to distributed edge deployments.
What's Working Today
Production-validated features ready for your deployments
GPU Acceleration
NVIDIA CUDA support with automatic GPU layer offloading. Achieve 17x faster inference on NVIDIA L4 GPUs.
- ✓ 64 tok/s on Llama 3.2 3B (vs 4.6 tok/s CPU)
- ✓ Automatic layer offloading (29/29 layers)
- ✓ GKE + NVIDIA GPU Operator ready
Full Observability
Complete monitoring stack with Prometheus, Grafana, and DCGM GPU metrics. SLO alerts included.
- ✓ GPU utilization, temp, power monitoring
- ✓ Pre-built Grafana dashboards
- ✓ Automated SLO alerts
Kubernetes Native
Custom Resource Definitions for Model and InferenceService. Works with kubectl, GitOps, and existing K8s tooling.
- ✓ Declarative YAML configuration
- ✓ GitOps-ready deployments
- ✓ Standard K8s patterns
Automatic Model Management
Download models from HuggingFace or any HTTP source. Automatic caching and validation included.
- ✓ HuggingFace integration
- ✓ GGUF format support
- ✓ Persistent volume caching
OpenAI Compatible
Drop-in replacement for OpenAI API. Use existing tools and libraries without code changes.
- ✓ /v1/chat/completions endpoint
- ✓ Streaming responses
- ✓ Compatible with LangChain, etc.
CLI Tool
Simple command-line interface for deploying and managing LLM workloads. Multi-platform support.
- ✓ Deploy with --gpu flag
- ✓ List, status, delete commands
- ✓ macOS, Linux, Windows binaries
What's Coming Next
Multi-GPU support, edge computing, and production hardening
Multi-GPU Single Node
Deploy larger models (13B+) with layer splitting across 2-4 GPUs on a single node.
- • Automatic layer distribution
- • GPU memory optimization
- • Target: 40-50 tok/s on 13B models
Production Hardening
Enhanced reliability, security, and resource management for production deployments.
- • Auto-scaling based on GPU utilization
- • Health checks and readiness probes
- • Pod Security Standards compliance
Multi-Node GPU Sharding
Distribute large models (70B+) across multiple GPU nodes with intelligent layer scheduling.
- • Layer-aware cross-node scheduling
- • P2P KV cache sharing (RDMA)
- • 70B models across 4 GPU nodes
SLO Enforcement
Automatic SLO monitoring, enforcement, and intelligent fallback mechanisms.
- • GPU-aware horizontal pod autoscaling
- • Automatic fallback to smaller models
- • Latency-based request routing
Edge Optimization
Distribute inference workloads across edge nodes with intelligent scheduling.
- • Geo-aware model placement
- • Bandwidth-optimized routing
- • Edge-specific resource management
Advanced Observability
Deep insights into performance, costs, and quality metrics for production workloads.
- • Per-request cost tracking
- • Quality monitoring (hallucination detection)
- • Advanced performance dashboards
Development Philosophy: We're building incrementally with production validation at each step. All features go through comprehensive testing on real workloads before release. Roadmap timeline is subject to change based on community feedback and technical discoveries.
Validated Performance
Real benchmarks from production deployments
Ready to get started?
Deploy your first GPU-accelerated LLM in minutes