manifesto.
we've spent years at hedge funds, big tech, national, and academic labs. we've seen firsthand how broken the current state of gpu software development is.
despite massive spend on gpu infrastructure, most ml workloads underperform. performance bottlenecks, vendor lock-in, and the manual grind of kernel tuning waste millions in compute and create painful bottlenecks for both customers and providers.
folks building gpu clusters for inference or training are on the front lines of several growing challenges:
downtime & reliability issues: even minor slowdowns at the kernel level can cascade into multi-million-dollar inefficiencies, degraded slas, and unhappy customers.
performance variance: out-of-the-box frameworks often leave 30-50% of gpu performance untapped. providers lose competitive edge when latency or throughput lags.
customer onboarding friction: the barrier to entry – arcane setups, poor documentation, vendor quirks – turns away prospective clients.
cost transparency & optimization: without clear performance guarantees or tooling to maximize hardware usage, clients end up paying for idle or inefficient compute.
as a result, businesses face higher compute costs and slower response times.
our bet.
we're building the tools and infrastructure to 10x the current state of gpu engineering. from the lowest layers of gpu kernel software all the way up to the frameworks developers interact with. by deeply understanding and optimizing every layer, we unlock performance, reliability, and usability that cloud providers and ml teams have struggled to achieve.
if you're building with gpus and want to stand out – whether through performance, reliability, or developer experience – reach out.
- the herdora team