Engineering Paradigm & Business Impact
We architect enterprise-grade AI infrastructure for training and high-throughput inference, optimizing GPU utilization and latency across distributed Kubernetes clusters.
Technology Toolchain
What We Deliver in Production
High-Throughput LLM Serving Architecture
Deploying optimized inference servers using vLLM, TensorRT-LLM, and Multi-Head Latent Attention (MLA) on Kubernetes.
Kubernetes GPU Orchestration & Autoscaling
Dynamic GPU node scheduling with NVIDIA Operator, Karpenter GPU bin-packing, and KEDA queue-based scaling.
Enterprise RAG & Vector Database Scaling
High-availability vector database infrastructure using pgvector, Qdrant, and hybrid search caching pipelines.
AI Model Observability & Cost Tracking
Real-time token latency tracking, GPU thermal/memory telemetry, and cost-per-prompt analytics.
Our 4-Stage Engagement Process
Model & Workload Profiling
We analyze model weights, context window requirements, concurrent user load, and target inference latency.
GPU Cluster & Serving Architecture Design
We architect Kubernetes GPU nodepools, vLLM deployment templates, and vector database replication.
Pipeline Deployment & Benchmarking
We run load tests under simulated concurrency to optimize KV-cache memory, batch sizes, and tensor parallelism.
Monitoring & Continuous Model Upgrades
We set up automated model weight synchronization, rollback gates, and token cost telemetry.
Visabridge.ai
Cross-account database migration with strict uptime requirements, multi-AZ VPC architecture, ALB, and zero data loss.
DeepSeek-R1 & V3 in Production: Multi-Head Latent Attention (MLA), FlashMLA & vLLM Kubernetes Deployments
The definitive architectural guide to self-hosting DeepSeek-R1 and V3 at scale: compressing KV cache via MLA, optimizing FlashMLA GPU kernels, native FP8 quantization, and orchestrating vLLM clusters on Kubernetes with KubeRay.
Frequently Asked Questions
How do you optimize GPU costs for large model inference?
We leverage vLLM PagedAttention, dynamic continuous batching, and intelligent spot GPU provisioning with automated fallback to keep inference costs low.