Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Establish definitive performance baselines and SLAs for your AI training and inference jobs across any hardware or cloud configuration.
Unpredictable performance leads to blown budgets, missed deadlines, and unreliable products. Our benchmarking delivers the hard data you need to make confident infrastructure decisions.
We identify the precise hardware and configuration that delivers maximum throughput at the lowest cost for your specific models and datasets.
Move from speculative capacity planning to data-driven procurement. Our benchmarks provide the foundation for effective AI Compute FinOps and inform your Hybrid Cloud AI Architecture strategy, ensuring every dollar of compute spend delivers measurable value.
Our rigorous benchmarking delivers more than just numbers. We provide the data-driven insights you need to make confident infrastructure decisions, optimize costs, and guarantee performance for your most critical AI workloads.
Establish definitive, reproducible performance metrics for your training and inference jobs. We deliver SLAs based on empirical data, not vendor promises, ensuring your models meet production latency and throughput requirements. This eliminates guesswork in capacity planning.
Identify the most cost-effective compute configuration for each workload type. Our benchmarks compare GPU instances (A100, H100, L4), ASICs, and cloud providers to reveal where you can reduce spend by 30-50% without sacrificing performance, directly supporting your AI Compute FinOps strategy.
Move beyond high-level metrics. We pinpoint exact system bottlenecks—whether in GPU utilization, CPU-GPU data transfer, network I/O, or storage throughput—and provide specific remediation steps. This accelerates development cycles and prevents production slowdowns.
Make data-backed decisions on capital-intensive hardware purchases like NVIDIA DGX systems or cloud commitment plans. Our benchmarks provide the evidence to justify investments and ensure selected infrastructure aligns with your 2-3 year AI roadmap.
Test and validate your proposed Hybrid Cloud AI Architecture before deployment. We benchmark workloads across on-premises and cloud environments to ensure seamless performance, optimal data placement, and cost-efficient scaling patterns.
Understand how your AI platform will perform under load. We stress-test systems to forecast limits and identify failure points, providing the blueprint for building AI Infrastructure Resilience and Scalability that supports business growth.
A detailed comparison of the time, cost, and risk involved in establishing an internal AI benchmarking capability versus partnering with Inference Systems.
| Benchmarking Factor | Build In-House Team | Inference Systems Service |
|---|---|---|
Time to Initial Baseline | 3-6 months | 2-4 weeks |
Hardware & Cloud Access | Procurement & setup required | Immediate access to multi-vendor fleet |
Expertise Required | Senior ML Engineers, DevOps | Our team's specialized experience |
Comprehensive Test Suite | Develop from scratch | Pre-built for 50+ model architectures |
Actionable Bottleneck Reports | Manual analysis | Automated with root-cause identification |
Ongoing Model & HW Tracking | Manual process | Continuous monitoring & alerts |
Total First-Year Cost | $250K - $500K+ | $75K - $200K |
Performance SLA Confidence | Unverified | Guaranteed 99.9% inference latency targets |
Our rigorous, hardware-agnostic benchmarking provides the definitive performance profile for your AI workloads. We identify bottlenecks, validate configurations, and deliver the data-driven insights needed to optimize for cost, speed, and reliability before you commit to a production architecture.
We benchmark your training and inference jobs across NVIDIA, AMD, and cloud-specific ASICs (like AWS Trainium/Inferentia) to deliver an unbiased comparison of throughput, latency, and cost-per-inference. This eliminates vendor guesswork and identifies the optimal hardware for your specific model architecture and batch sizes.
We test identical workloads across different instance types (e.g., AWS p4d vs. p5, Azure ND A100 v4 series) and regions to pinpoint the most cost-effective and performant cloud configuration. Our analysis includes spot instance viability and multi-cloud cost/performance trade-offs for resilient architectures.
Our diagnostics go beyond surface metrics to isolate bottlenecks in data loading, inter-GPU communication, or kernel execution. We establish quantifiable performance baselines essential for negotiating cloud SLAs and setting realistic internal expectations for model training and serving timelines.
We evaluate the performance impact of different deep learning frameworks (PyTorch, TensorFlow, JAX) and parallelism strategies (data, model, pipeline) on your specific workload. This ensures your engineering team adopts the most efficient software stack from the start, avoiding costly re-architecture later.
We simulate production-scale load to test how your workload performs under scaling—from a single GPU to a multi-node cluster. This reveals scaling efficiency, identifies communication overheads, and validates the elasticity of your proposed infrastructure for handling peak demands.
We integrate performance data with real-time cloud pricing to build accurate TCO models comparing on-premises, hybrid, and pure-cloud deployments. This financial modeling is critical for informed capital expenditure (CapEx) versus operational expenditure (OpEx) decisions and long-term budget planning. Learn more about our related AI Compute FinOps and Cost Optimization services.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers on how our rigorous benchmarking process delivers measurable performance gains and cost savings for your AI workloads.
We employ a systematic, multi-variable analysis that isolates performance factors most internal teams lack the tools or time to test. Our process includes: 1) Hardware Profiling across GPU generations (A100, H100, L40S) and cloud instances, 2) Framework & Kernel Analysis using tools like NVIDIA Nsight Systems and PyTorch Profiler to identify inefficient ops, and 3) Scalability Stress Testing to uncover bottlenecks that only appear at scale. Unlike basic internal checks, we establish statistically significant baselines and provide actionable optimization roadmaps, not just reports. This methodology has helped clients achieve 30-50% faster training times and 60% lower inference latency.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
