AI infrastructure and deployment
We support on-prem, cloud-native, hybrid, and air-gapped AI deployments based on your security, latency, compliance, and cost requirements.
What you get
Faster, cheaper, and more reliable AI infrastructure
Reduce AI infrastructure cost
We analyze workloads, GPU usage, batch sizes, model serving stack, and deployment architecture to remove wasted capacity. Through quantization, batching, NVIDIA vGPU/MIG, right-sized instances, and better scheduling, teams often achieve up to 40% lower GPU spend and up to 60% smaller model footprint.
Lower latency for real-time AI
For assistants, VLMs, digital humans, robotics, medical AI, simulation, and edge AI, every millisecond counts. We tune serving, memory, scheduling, networking, and storage to improve P99 latency and real-time responsiveness.
Increase throughput per GPU
Get more tokens, frames, requests, and predictions from the same hardware. We benchmark and select the right runtime: NVIDIA NIM, NVIDIA Dynamo, Triton, TensorRT-LLM, vLLM, SGLang, ONNX Runtime, or custom containers.
Use your existing hardware better
Many organizations already own powerful GPU servers but run them below capacity. We recover headroom with vGPU, MIG, Kubernetes, Docker, VMware, Proxmox, KVM, passthrough, and workload isolation for secure multi-team sharing.
Deploy anywhere
Deploy across Azure, AWS, GCP, on-prem clusters, hybrid setups, air-gapped environments, and edge devices such as NVIDIA Jetson. We also support sourcing and configuring hardware with trusted partners.
Get production-grade observability
Monitor GPU utilization, memory, temperature, power draw, PCIe, disk, network, and model-level KPIs in real time. This keeps systems stable, measurable, and cost-efficient.
We optimize every watt, token, and millisecond
From expensive and unstable to fast and production-ready
AI infrastructure is not just about buying GPUs. It is about using every GPU efficiently. OneBonsai profiles your full stack, from model architecture and quantization to serving, containers, orchestration, networking, storage, and observability.
We choose the right engine for each workload
Our stack
One AI stack for cloud, on-prem, hybrid, and edge
From black box to live AI observability
See exactly how your AI infrastructure performs
We do not guess. We measure. Our dashboards expose GPU, memory, power, temperature, PCIe, network, disk, and model-serving metrics in real time so you can remove bottlenecks and validate improvements before and after optimization.
How to start
Start with an AI infrastructure audit
In one short engagement, we benchmark your current workload, identify bottlenecks, and deliver a practical optimization plan with quick wins and long-term roadmap.
Deliverables