Scicom Advances Open-Source AI Leadership with Launch of Two Flagship Developer Tools and a GPU Calculator

The Lead

The Strategy

As demand for large language model (LLM) integration accelerates across industries, organizations face a growing challenge: managing the complexity of AI deployments while ensuring consistent, measurable performance. Scicom's AI Enterprise division designed pyremote and llm-benchmaq to address precisely these friction points, reducing the operational burden on engineering teams and introducing a structured, repeatable framework for AI performance measurement.

That benchmarking work was not merely theoretical. The team conducted more than 250 individual benchmark runs spanning dense and mixture-of-experts (MoE) model architectures, multiple context lengths, and varying concurrency levels. The resulting dataset captures real-world inference behavior across a broad configuration space — and it directly powers a new public tool: the AIES GPU Calculator. Given a target model, context length, and throughput requirement, the calculator estimates the number of GPUs needed and projects key inference metrics — including time to first token (TTFT), output throughput, and end-to-end latency — through both interpolation and extrapolation across the benchmark dataset. For teams planning deployments or evaluating infrastructure spend, this closes the gap between raw benchmark data and actionable procurement decisions.

These releases are particularly timely as the industry navigates a generational hardware transition. With Blackwell-generation GPUs delivering up to 2.5× faster training and significantly lower cost-per-token compared to previous-generation Hopper systems, organizations must now evaluate their infrastructure economics with greater precision. The GPU Calculator was built precisely for this moment: giving teams a data-grounded way to compare configurations before committing capital.

These releases are consistent with Scicom's broader strategic direction in digital transformation and AI-enhanced service delivery. By contributing production-ready tooling to the open-source community and publishing a freely accessible GPU planning tool, Scicom reinforces its position not only as a technology adopter, but as an active architect of the evolving AI ecosystem — one that serves enterprise clients across government, telecommunications, and financial services sectors.

Executive Voice

“We are committed to keeping our AI infrastructure tools open-source — not just because it's the right approach for the community, but because we want the world to see what we're building. These tools solve real problems we've encountered in production, and we believe they can help other organizations accelerate their AI journey. We encourage developers and enterprises everywhere to use them, build on them, and make them even better.”

“In today's GPU economy, inference costs have dropped dramatically — but only for organizations that can measure and optimize their deployments effectively. llm-benchmaq gives teams the visibility they need to make informed infrastructure decisions, whether they're comparing Hopper versus Blackwell performance or evaluating cost-per-token across different model configurations. And with more than 250 benchmarks now underpinning the GPU Calculator, we can go further: teams can arrive with a target workload and leave with a concrete GPU estimate and projected inference metrics — before writing a single line of deployment code.”

Key Highlights