Presear designs and integrates AI hardware stacks — GPU clusters, FPGAs, custom accelerators, and inference servers — to unlock maximum throughput at minimum cost.
Technical Depth
From GPU cluster architecture to thermal management — we handle every layer of the AI hardware stack so your models get maximum compute at minimum waste.
Design, procure, and integrate high-density GPU clusters for large-scale model training — from single-rack A100/H100 nodes to multi-rack DGX SuperPOD configurations. We specify networking topology (InfiniBand vs. Ethernet), NVLink configurations, storage interconnects, and power infrastructure, then oversee physical integration and commissioning with full benchmarking validation.
Deploy custom inference pipelines on Xilinx Alveo and Intel Stratix FPGAs for workloads demanding deterministic ultra-low latency — HFT scoring, radar signal processing, and sequence-to-sequence models. We implement custom bitstreams with pipelined dataflow architectures that sustain throughput without the jitter of GPU scheduling, achieving sub-microsecond inference for latency-critical applications.
Advise on custom AI ASIC pathways for organisations running models at internet scale — evaluating whether NPU-class custom silicon justifies the non-recurring engineering cost, identifying suitable foundry partners, and structuring IP ownership. We also evaluate and benchmark third-party AI accelerators including Graphcore IPUs, Cerebras WSE, and SambaNova to determine fit for specific training and inference workloads.
Maximise throughput and minimise latency on inference serving hardware through TensorRT graph optimisation, dynamic batching, quantisation-aware deployment, and multi-model concurrent execution. We profile inference workloads end-to-end — from model loading to response delivery — identifying and eliminating bottlenecks in the preprocessing, inference, and postprocessing pipeline to extract full hardware utilisation.
AI workloads are frequently memory-bandwidth-bound rather than compute-bound — training large transformers hits HBM bandwidth limits long before CUDA core utilisation peaks. We analyse memory access patterns, implement activation checkpointing, gradient compression, and mixed-precision training to fit larger models into available VRAM, reducing the GPU count required for a given model size by 30–50%.
High-density GPU clusters generate extraordinary heat — a 10-node H100 cluster can draw 80kW. We design thermal management strategies covering airflow optimisation, liquid cooling integration, rack power distribution, and UPS sizing to ensure stable sustained performance without thermal throttling. We also implement DCGM-based power capping policies that balance throughput with data centre PUE targets.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
Before specifying any hardware, we profile your AI workloads in detail — characterising compute intensity, memory bandwidth requirements, model size, batch processing patterns, and latency constraints. This profiling determines whether your bottleneck is compute, memory bandwidth, or networking — each pointing to different hardware solutions. Getting this wrong means spending on the wrong resource.
Armed with workload profiles, we evaluate hardware candidates against your requirements — benchmarking FLOPS, memory capacity, bandwidth, interconnect speed, and total cost of ownership. We provide objective comparison reports across NVIDIA, AMD, and Intel accelerators, and advise on procurement strategy: direct purchase, leased hardware, or cloud-equivalent alternatives for variable workloads.
Physical cluster integration requires precise attention to rack layout, power distribution, cable management, and cooling topology. We configure InfiniBand or high-speed Ethernet fabrics for inter-GPU communication, set up NVLink bridges for intra-node connectivity, configure storage interconnects (NVMe-oF or Lustre), and validate network bandwidth meets the all-reduce communication requirements of distributed training workloads.
Hardware without the right software stack delivers a fraction of its theoretical performance. We install and configure CUDA, ROCm, or oneAPI drivers, NCCL for collective communication, Slurm or Kubernetes for job scheduling, and the full ML framework stack — PyTorch, TensorFlow, DeepSpeed, Megatron-LM — verified against your target workloads with reproducible baseline benchmarks before handover.
We validate that the delivered cluster achieves the performance targets set at workload profiling. Running MLPerf training benchmarks and workload-specific throughput tests, we identify underperforming components, tune NCCL collective algorithms for your specific topology, optimise Slurm scheduling policies, and document achievable MFU (model FLOPS utilisation) baselines that give your team predictable performance expectations for production workloads.
Real-World Impact
Production AI hardware deployments across sectors — each built to maximise compute utilisation and minimise cost per model.
Core Challenge
Training large language models requires tightly coordinated GPU clusters where networking bottlenecks — not compute — are the dominant constraint. Poor InfiniBand configuration or suboptimal NCCL collective settings can reduce effective MFU from 55% to under 30%, doubling training time and cost for the same hardware spend.
Who Benefits
Enterprises building proprietary LLMs for domain-specific applications, AI labs requiring on-premise training infrastructure for data sovereignty, and research institutions that need reproducible, managed GPU cluster environments for long-running pretraining and fine-tuning experiments.
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Serving ML inference at scale — hundreds of thousands of requests per second with sub-10ms SLAs — requires hardware configured specifically for inference throughput rather than training, with TensorRT optimised model graphs, multi-model concurrency, and dynamic batching policies that maximise GPU utilisation without violating latency budgets.
Who Benefits
Cloud AI service providers, SaaS companies with embedded ML features, and enterprises running high-QPS AI APIs who need a dedicated inference infrastructure layer that delivers predictable latency SLAs under variable traffic load with cost-efficient hardware utilisation.
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Large-scale video AI — transcription, scene detection, content moderation — requires hardware pipelines that sustain throughput on hours of video content with mixed-precision inference, video decode acceleration, and storage bandwidth that matches GPU processing speed without creating I/O bottlenecks that idle expensive compute.
Who Benefits
Streaming platforms, broadcast media companies, and video content aggregators that process large libraries or live streams and need dedicated AI processing infrastructure capable of handling diverse video formats with consistent throughput and quality-of-service guarantees.
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Scientific AI workloads — molecular dynamics, climate modelling, protein structure prediction — have distinct memory and compute profiles from deep learning, often requiring FP64 precision and massive aggregate memory capacity that standard AI GPU configurations do not address without specific hardware selection and MIG partitioning strategies.
Who Benefits
Research institutions, national laboratories, and pharmaceutical companies running AI-accelerated scientific simulations that need hardware clusters designed for their specific precision, memory, and interconnect requirements — separate from the GPU configurations optimised for commercial AI training.
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Leading accelerators, interconnects, software stacks, and observability tooling — covering the full hardware-to-application stack.
Frequently Asked
Answers to the questions infrastructure teams, CIOs, and ML platform engineers ask before starting an AI hardware engagement with Presear Softwares.
Ask Our Hardware TeamPartner with Presear Softwares to design, integrate, and optimise AI hardware infrastructure that delivers maximum throughput at minimum cost — from single-node to hyperscale clusters.