AI & Modern Workloads
GPU clusters, high-concurrency model inference serving, and distributed data compute.
Infrastructure engineered for machine learning inference, fine-tuning workloads, and high-throughput vector compute.
High-performance GPU instances optimized for deep learning, model inference, and embeddings.
Key Architecture:
- ✓GPU Hardware: Enterprise NVIDIA Tensor Core GPUs
- ✓Host Memory: 64 GB - 256 GB ECC System RAM
- ✓Model Storage: Ultra-fast NVMe local caching arrays
Containerized infrastructure optimized for low-latency model serving endpoints and API inference.
Key Architecture:
- ✓Serving Frameworks: vLLM, Triton, Ollama, FastAPI container stacks
- ✓Response Streaming: Optimized Server-Sent Events (SSE) / WebSocket proxying
- ✓Memory Protection: cgroup memory constraints and OOM mitigation
High-memory infrastructure for vector search engines (pgvector, Qdrant, Milvus) and analytics data pipelines.
Key Architecture:
- ✓Engines Supported: pgvector, Qdrant, Milvus, Redis, ClickHouse
- ✓Memory Tiers: 32 GB to 512 GB high-speed ECC RAM
- ✓Storage: Direct-attached NVMe storage for fast HNSW index traversal