# NVIDIA Vera Rubin

ElevenLabs and NVIDIA are expanding their partnership to bring NVIDIA's Nemotron open models into ElevenAgents, ElevenLabs' conversational agent platform, and to add 15 MW of Vera Rubin capacity on top of existing Blackwell Ultra systems. On the ElevenAgents benchmark for realistic customer-support tasks, Nemotron 3.5 Super VL reached leading end-to-end task-completion accuracy at 70% lower cost than other leading open models. The two companies are also applying voice agents to Poland's national e-Health central appointment system, with an on-premises deployment that keeps citizen data and audio within government infrastructure.

ElevenLabs and NVIDIA are expanding their partnership to bring NVIDIA's Nemotron open models into ElevenAgents, ElevenLabs' conversational agent platform, and to add 15 MW of Vera Rubin capacity on top of existing Blackwell Ultra systems. On the ElevenAgents benchmark for realistic customer-support tasks, Nemotron 3.5 Super VL reached leading end-to-end task-completion accuracy at 70% lower cost than other leading open models. The two companies are also applying voice agents to Poland's national e-Health central appointment system, with an on-premises deployment that keeps citizen data and audio within government infrastructure.

At GTC Berlin, NVIDIA introduced DSX POD, a reference design for complete Vera Rubin AI factories that partners integrate and validate inside modular containers before delivery, cutting onsite deployment from six months to 14 days once site utilities are ready. A companion OpenShell reference architecture places a policy-controlled runtime with kernel-level sandboxing between an agent and the systems it can affect, since each launched agent carries different privileges.

At GTC Berlin, NVIDIA introduced DSX POD, a reference design for complete Vera Rubin AI factories that partners integrate and validate inside modular containers before delivery, cutting onsite deployment from six months to 14 days once site utilities are ready. A companion OpenShell reference architecture places a policy-controlled runtime with kernel-level sandboxing between an agent and the systems it can affect, since each launched agent carries different privileges.

NVIDIA released OpenShell, an open-source runtime that places an AI agent's access rights and governance in the infrastructure around it rather than relying on the agent to restrain itself. Long-running autonomous agents download code, request new privileges and touch more infrastructure, so risk compounds as they work. OpenShell uses a gateway, a supervisor running outside the sandbox, the sandbox itself and a policy prover to bound file, network and credential access; in the reference architecture, CoreWeave measured sandbox startup more than 3x faster on Vera than on x86, with a single rack supporting more than 11,000 concurrent agent environments.

NVIDIA released OpenShell, an open-source runtime that places an AI agent's access rights and governance in the infrastructure around it rather than relying on the agent to restrain itself. Long-running autonomous agents download code, request new privileges and touch more infrastructure, so risk compounds as they work. OpenShell uses a gateway, a supervisor running outside the sandbox, the sandbox itself and a policy prover to bound file, network and credential access; in the reference architecture, CoreWeave measured sandbox startup more than 3x faster on Vera than on x86, with a single rack supporting more than 11,000 concurrent agent environments.

Fireworks announced an expanded collaboration with NVIDIA to add capacity in Europe on the NVIDIA Vera Rubin and Grace Blackwell platforms, planning to use the NVIDIA DSX AI factory platform and Spectrum-X Ethernet, with the added capacity supporting European deployments beginning in 2027. The company also introduced enterprise inference capabilities: an Ultrafast inference preview on the NVIDIA Blackwell Platform reaching inference speeds exceeding 1,500 tokens per second, multi-day prompt caching and background mode for long-running agents, and higher rate limits with sub-minute cold starts. These target the latency and cost that arise when a single agent task involves repeated model calls and large amounts of shared context.

Fireworks announced an expanded collaboration with NVIDIA to add capacity in Europe on the NVIDIA Vera Rubin and Grace Blackwell platforms, planning to use the NVIDIA DSX AI factory platform and Spectrum-X Ethernet, with the added capacity supporting European deployments beginning in 2027. The company also introduced enterprise inference capabilities: an Ultrafast inference preview on the NVIDIA Blackwell Platform reaching inference speeds exceeding 1,500 tokens per second, multi-day prompt caching and background mode for long-running agents, and higher rate limits with sub-minute cold starts. These target the latency and cost that arise when a single agent task involves repeated model calls and large amounts of shared context.

NVIDIA released CUDA 13.5, moving support for the Vera Rubin platform from early developer access to general availability for enterprise production; Windows on Arm support, GA since CUDA 13.4, now extends to DGX Station for Windows. The same release adds GPU scheduling customization through locality domains and MPS v3, a preview of multi-GPU communication and SIMT library calls inside CUDA Tile kernels, and updates across CUDA Python, CCCL and the math libraries. These capabilities target developers deploying to known architectures, and the benefit depends on the chosen platform and configuration.

NVIDIA released CUDA 13.5, moving support for the Vera Rubin platform from early developer access to general availability for enterprise production; Windows on Arm support, GA since CUDA 13.4, now extends to DGX Station for Windows. The same release adds GPU scheduling customization through locality domains and MPS v3, a preview of multi-GPU communication and SIMT library calls inside CUDA Tile kernels, and updates across CUDA Python, CCCL and the math libraries. These capabilities target developers deploying to known architectures, and the benefit depends on the chosen platform and configuration.