Vector Core Compute

Vector Core Compute (VC2) is an enterprise inference cloud, launched by Vista Equity Partners and Cambium Capital, that splits AI inference across Intel Xeon CPUs, NVIDIA Blackwell GPUs and SambaNova RDUs instead of running everything on GPUs. It targets enterprises and AI platform teams running agentic workloads that need low-latency, US-metro-local inference capacity.

Vector Core Compute, known as VC2, launched on June 3, 2026 as what its backers describe as the world's first commercially available enterprise inference cloud built on a fully disaggregated inference architecture. Instead of running every stage of inference on GPUs, VC2 assigns each stage to the processor best suited to it: Intel Xeon 6 CPUs handle orchestration and tool execution for agentic workloads, NVIDIA Blackwell GPUs handle prefill and prompt caching, and SambaNova SN40 RDUs handle decode. Independent measurement by Artificial Analysis found the architecture to be at least two to three times faster than a GPU-only stack. The company was formed by Vista Equity Partners and Cambium Capital and is backed by a $3.5 billion compute commitment to SambaNova with support from Intel; SambaNova CEO Rodrigo Liang has called VC2 the largest commercial deployment of SambaNova. VC2 went live from a Los Angeles facility, with additional sites in development in Chicago, Seattle and Phoenix and a stated plan to distribute inference endpoints across more than 50 US metropolitan markets rather than concentrating capacity in a handful of remote mega-sites. Together AI, which serves hundreds of trillions of inference tokens a month, is VC2's first commercial customer, and Vista Equity Partners has secured early access for its 90-plus portfolio companies. The company positions itself publicly as 'building North America's agentic-first enterprise cloud.'

Category

Infrastructure & Cloud

Pricing

Contact for pricing

Target Market

CTOs, CIOs, VPs of Engineering, AI Infrastructure Leaders, Enterprise Architects

Founded

2026

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  Fully disaggregated inference

  Each inference stage is assigned to a different processor type rather than running the whole pipeline on GPUs.

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  Tri-silicon architecture

  Intel Xeon 6 CPUs for orchestration and tool execution, NVIDIA Blackwell GPUs for prefill and prompt caching, and SambaNova SN40 RDUs for decode.

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  Metro-distributed capacity

  Inference endpoints are being distributed across 50-plus US metropolitan markets instead of concentrated in remote mega-sites, putting compute near the enterprises using it.

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  Agentic-workload orchestration

  CPU-side orchestration and tool execution is designed specifically for multi-step agentic AI rather than single-shot prompts.

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  Independently benchmarked speed

  Artificial Analysis measured the disaggregated architecture at least two to three times faster than a GPU-only stack.

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  Serve production agentic AI at scale

  Run multi-step agent workloads where CPU-based tool execution and orchestration sit alongside GPU prefill and RDU decode in a single pipeline.

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  Low-latency regional inference

  Place inference endpoints in the same metro as the applications and customers consuming them to cut round-trip latency.

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  Diversify away from GPU-only capacity

  Enterprises constrained by GPU supply or GPU-only economics can buy inference capacity built on a mixed CPU/GPU/RDU fleet.

No public pricing has been published; capacity is sold to enterprise customers directly.

This page was written from 3 sources, 2 on domains other than vectorcorecompute.com.

1. 1.itbrief.co.uk — vista launches vector core compute for ai inference
2. 2.aithority.com — vista equity partners and cambium launch vector core compute
3. 3.vectorcorecompute.com — vectorcorecompute.comvendor