ONE ENGINEERING PARTNER FROM CAPACITY TO WORKFLOW.
BUILD | OPTIMIZE | AUTOMATE | ADOPT
ElioVP designs high-density AI infrastructure, improves workload economics, and deploys practical AI systems that fit the way your teams operate.
Start with new capacity, a workload you want to optimize, or a workflow you want to automate.
WHERE DO YOU WANT TO START?
ElioVP connects facilities, compute, model performance, and operational AI, but you do not need to buy everything at once.
Start with the problem that matters now and build from there.
Build AI capacityPlan the compute, networking, storage, software and facility fit for a new AI or HPC deployment.
Optimize inference economicsBenchmark and improve throughput, latency, utilization, and cost on the infrastructure you already own.
Automate a workflowTurn one repeatable process into a controlled, measurable AI workflow with human review built in.
Build practical AI readinessGive employees the knowledge and judgment to use AI responsibly, with progress visible to HR.TRUST & RECOGNITION
ElioVP’s infrastructure and optimization work has been featured, documented and listed through official AMD, Supermicro and NVIDIA channels.
Official article
AMD published “Introduction to Paiton by ElioVP,” highlighting ElioVP’s work on performance tooling, AI optimization and AMD platforms.
AMD Data Center also features CEO Elio Van Puyvelde on video discussing ROCm and AMD Instinct workload optimization. The AMD Tech Talk podcast explores ElioVP’s origins and end-to-end approach.
Customer success story PDF
Supermicro published a customer success story documenting ElioVP’s server, GPU and infrastructure optimization work.
Official marketplace listing
ElioVP is publicly listed in NVIDIA’s official enterprise partner marketplace.
WHY THE LAYERS MATTER
Facility, compute, model, and workflow decisions affect one another. ElioVP connects those decisions so performance, cost, control, and operational fit can be evaluated as one system.
Start with the workload, workflow, capacity requirement, or operating constraint, then agree how improvement will be measured.
Bring facility, cooling, power, compute, runtime, integration, and workflow choices into the same technical conversation.
Place infrastructure, models, data, and logs according to the workload’s security, sovereignty, latency, and commercial requirements.
Compare the delivered system or workflow with the agreed baseline before making the next investment decision.
PUBLISHED EVIDENCE
External recognition is most useful when it points to a measured result, a real engineering capability, or experience at scale.
AMD’s official engineering feature describes consistent double-digit throughput and cost improvements from Paiton across customer-owned AMD infrastructure.
Read the AMD featureAMD’s published profile records more than 250,000 GPUs deployed through ElioVP’s infrastructure work.
Verify with AMDA 2022 Supermicro customer story reports calculations up to 35% faster, together with throughput and power-efficiency benefits in the project’s original context.
Read the Supermicro caseEXPERTISE
From facility engineering and high-density compute to production software and ongoing optimization, ElioVP connects the physical and digital layers of AI infrastructure.
Designing the physical foundation for high-density AI and HPC infrastructure.
Building balanced compute platforms around real workload, capacity and performance requirements.
Connecting infrastructure to production applications, models and operational workflows.
Improving performance, reliability and infrastructure economics throughout the platform lifecycle.
Uptime Institute Accredited Tier Specialist expertise within the ElioVP engineering team.
LATEST NEWS
Paiton runs Qwen3.8 Flash Next in 4-bit on two R9700 cards: 96.8% GSM8K, 96.3% HumanEval, 189.8 tokens/s and an optional RAM prefix cache. Accuracy, speed and setup.
Read more: Qwen3.8 Flash Next in 4-bit on 2 × Radeon R9700: tested against 16-bit
Paiton runs Qwen3.8 Flash Next with 3-bit experts on two R9700 cards: 216.3 tokens/s, 565.1 aggregate tokens/s and a separate 200K mode. Benchmarks, quality and setup.
Read more: Qwen3.8 Flash Next on 2 × Radeon AI PRO R9700: 216 tokens/s, 200K context
Qwen3.8 27B on one R9700: 19.9% faster weighted decode, a new 4-bit cache, 200K context and optional vision. Benchmarks and current Paiton setup.
Read more: Qwen3.8 27B on 1 × Radeon AI PRO R9700: 3-bit weights, 20% faster decodeWe will help define the right first assessment, the evidence needed, and the smallest useful next step.
