DeepSeek V4 Flash deployment demonstrates the power of GraphX to push hardware further and bring down the cost of private AI
ATLANTA, GA / ACCESS Newswire / October 29, 2026 / Rival.io today launched GraphX, its proprietary kernel-optimization and silicon-placement technology, and announced the deployment of DeepSeek V4 Flash on just eight NVIDIA L4 GPUs. The achievement demonstrates GraphX’s ability to run a massive open-weight model on hardware considered incapable of supporting it, challenging assumptions about the infrastructure and cost required for private AI.
“We took hardware most organizations would dismiss for a model of this scale and made it run DeepSeek V4 Flash,” said Craig Kronenberger, CEO and co-founder of Rival.io. “That should change the conversation about AI infrastructure and the cost of private AI. Before businesses commit to more expensive hardware, they should ask how much more the hardware they have could deliver.”
GraphX optimizes how models are placed across GPUs and how they use memory and computing power. This enables enterprises to meet workload requirements with fewer or less expensive GPUs and get more capacity from hardware they already own or can readily access. Depending on the model and workload, GraphX can reduce open-weight model hosting costs by up to 95% compared with equivalent deployments managed internally or hosted by hyperscale cloud providers.
The eight L4 configuration combined 192 GB of GPU memory and supported the model’s context window and associated KV cache, a capability that previously required specialized, expensive hardware setups.
Rival.io has also deployed DeepSeek V4 Flash on eight NVIDIA RTX 6000 GPUs with full enterprise grade performance, demonstrating GraphX’s ability to support enterprise workloads with high concurrency.
“Hardware specifications alone don’t tell you what a system can deliver. How you place the model, manage memory, and execute its computations can fundamentally change what’s possible on that hardware,” said Charlie Wardell, CTO and co-founder of Rival.io. “The implications go well beyond L4s. We’re expanding the hardware capable of running large open-weight models and showing that private AI doesn’t have to depend on a narrow set of premium GPUs.”
GraphX optimizes workloads across compatible AMD, Intel, and NVIDIA hardware and supports DeepSeek, GLM, and Kimi models, giving enterprises flexibility to choose models and infrastructure based on their performance requirements, budgets, and available capacity.
Supported models can be deployed in an enterprise’s own cloud or data center, or on dedicated Rival.io infrastructure. Each deployment operates in a dedicated, isolated environment with its own secure endpoint and access credentials, providing control over where AI runs and restricting connections to authorized users and applications. Models can power workflows, agents, and applications within Rival.io or connect to third-party tools, extending private AI across the systems businesses already use.
GraphX is available on-demand at Rival.io/graph-x.
About Rival.io
Rival.io is an enterprise AI operating system that brings together everything businesses need to build, deploy, govern, and scale AI in one integrated, model-agnostic platform. Powered by CortexOne™, Rival.io enables organizations to orchestrate AI workflows without managing complex infrastructure, while giving developers and domain experts a way to publish, protect, and monetize reusable tools, functions, agents, and systems. Learn more at rival.io.
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SOURCE: Rival.io
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