GPU Compute Design
Right-size accelerators for LLM training, fine-tuning, vision and inference SLAs — without overbuying.
Design and deploy AI servers that actually match your models — GPU compute, ECC memory, enterprise storage and production-ready infrastructure for LLMs, vision and analytics.
An AI server is a high-performance computer optimized for artificial intelligence workloads such as model training, fine-tuning and real-time inference. Unlike a general business server, an AI server combines multi-core CPUs with GPUs or accelerators, large volumes of ECC server memory, and fast enterprise NVMe storage so large language models, vision systems and analytics pipelines can run efficiently.
Organizations choose AI servers when they need private compute, predictable latency, data residency, or lower long-term cost versus pure cloud GPU rental. Unihox helps you define the right AI server architecture — whether you are launching a private LLM, scaling inference APIs, or building a research cluster.
Right-size accelerators for LLM training, fine-tuning, vision and inference SLAs — without overbuying.
ECC DDR4/DDR5 server memory and enterprise NVMe/SSD planning for datasets, checkpoints and low-latency serving.
Separate architectures for heavy training clusters and cost-efficient inference nodes or edge deployments.
Hardware that fits your MLOps, RAG, agentic workflows and enterprise security requirements.
Private model hosting, fine-tuning jobs and high-throughput chat / document generation.
Inspection lines, video analytics and multi-camera inference with GPU density.
Forecasting, fraud signals and decision intelligence with GPU-accelerated pipelines.
University and R&D clusters with shared nodes, quotas and reproducible environments.
An AI server is a high-performance system built for AI training and inference, typically using GPUs, large ECC RAM and fast enterprise storage — not a standard office server.
AI servers prioritize parallel GPU compute, memory bandwidth and storage throughput for models and datasets, with higher power and cooling needs.
Training needs more GPUs and sustained power. Inference can be smaller and cheaper for production APIs. We size both based on your models and traffic.
We advise on architecture, source key components (GPU, memory, storage), and align hardware with your AI software stack and compliance needs.
Tell us your models, GPU preference and timeline. We will recommend a practical AI server configuration and next steps.