Managed GPU infrastructure for AI workloads.

RunvexityManaged GPU infrastructure

Get more control over the AI your business relies on

We run your chosen AI model as a private service that connects to your applications and workflows. Pick from our catalog or bring your own, and we’ll handle deployment, infrastructure, monitoring, and ongoing maintenance.

See how it works

01The problem

Need more control over how your business runs AI?

Does your product depend on AI? Do your workflows involve sensitive data, or do you have a model ready to deploy?

You need a reliable setup, and someone to keep it running.

  1. Is AI powering your product?

    Get more control over the model and resources behind it, whether you use AI to summarize support tickets, suggest replies, classify leads, or extract data from invoices.

  2. Do you work with sensitive data?

    Choose where your model runs and control access through a private API when processing contracts, CVs, or client documents.

  3. Already have a model ready to run?

    Bring it to us. We’ll check compatibility, deploy it, and handle monitoring and ongoing maintenance.

Focus on what AI does for your business. We’ll handle the infrastructure that keeps it running.

02How it works

Bring your model. Or don’t.
We’ll run it either way.

Choose from our catalog or bring one you already use. We’ll handle deployment, infrastructure, and ongoing maintenance.

Path A

Already have a model? Here’s what happens next.

  1. Compatibility check

    We’ll check whether it can run on our infrastructure and what resources it needs.

  2. Deployment

    We’ll run your model on dedicated GPU infrastructure matched to your needs.

  3. Connection & maintenance

    You’ll connect your application through a private API. We’ll monitor the service and keep it running.

Path B

Don’t have a model yet? We’ll help you choose one.

  1. Model selection

    Tell us what you need AI to do. We’ll help you choose a model from our catalog that fits your requirements.

  2. Deployment

    We’ll run your chosen model on dedicated GPU infrastructure matched to your needs.

  3. Connection & maintenance

    You’ll connect your application through a private API. We’ll monitor the service and keep it running.

03Why Runvexity

Focus on getting more from AI.
We’ll handle the infrastructure.

  • Resources dedicated to your service

    Run your AI on GPU infrastructure allocated to your workload and matched to its requirements.

  • Control over access and location

    Use an authenticated private API and choose a deployment region, subject to compatible capacity.

  • Less infrastructure work for your team

    We handle deployment, monitoring, updates, and recovery, so your team can focus on your product.

  • Support as your needs change

    Work with us to adjust your setup as usage grows or your requirements change.

04Infrastructure

The compute your workload needs.

Choose GPU infrastructure based on your model, performance and location requirements.

5 configurations

ConfigurationMemoryAccessLocations
8× A100 80GB640 GB8 × 80 GBSelf-service3 locations
8× H100 80GB640 GB8 × 80 GBSelf-service2 locations
4× A100 40GB160 GB4 × 40 GBOn request1 location
8× L40S 48GB384 GB8 × 48 GBSelf-service3 locations
4× A10G 24GB96 GB4 × 24 GBOn request2 locations
  • 8× A100 80GBSelf-service
    Memory
    640 GB8 × 80 GB
    Locations
    3 locations
  • 8× H100 80GBSelf-service
    Memory
    640 GB8 × 80 GB
    Locations
    2 locations
  • 4× A100 40GBOn request
    Memory
    160 GB4 × 40 GB
    Locations
    1 location
  • 8× L40S 48GBSelf-service
    Memory
    384 GB8 × 48 GB
    Locations
    3 locations
  • 4× A10G 24GBOn request
    Memory
    96 GB4 × 24 GB
    Locations
    2 locations

Filter by GPU or region. Access type and number of locations vary by configuration.

05Performance

Performance backed by
real-world data.

We measure performance under real AI workloads, using the same GPUs and software stack that power our managed endpoints.

  • 173tok/s

    Large model speed

    Fast response generation, even with large AI models.

    gpt-oss-120b · 1× NVIDIA RTX PRO 6000 Blackwell · single-user speed

  • 128+users

    Served at the same time

    Response generation starts in under one second (p95).

    Qwen3.6 35B MoE · 1× NVIDIA RTX PRO 6000 Blackwell

  • ~1min

    Automatic recovery

    Your endpoint automatically recovers from an engine crash, without manual intervention.

    Entry-tier configuration · Engine restart to serving requests in approximately 1 minute

  • ≥96%

    Minimal overhead

    Keep nearly all of your model’s performance while using a secure, private API.

    Raw engine throughput retained · TLS encryption, API keys & usage metering

Performance across different configurations

ConfigurationModelSingle-user speedUsers with first token under 1 s (p95)Total throughput
StarterRTX 4000 Ada, 20 GBgpt-oss-20b78 tok/s32+799 tok/s
Llama 3.1 8B (FP8)37 tok/s32+846 tok/s
Standard2× RTX 4000 Ada, 40 GBQwen3 30B MoE (FP8)104 tok/s32871 tok/s
PerformanceRTX PRO 6000 Blackwell, 96 GBgpt-oss-120b173 tok/s643,039 tok/s
Qwen3.6 35B MoE (FP8)175 tok/s128+2,977 tok/s
Llama 3.3 70B (FP8)19 tok/s161,192 tok/s
Multi-GPU2× RTX PRO 6000 Blackwell, 192 GBgpt-oss-120b226 tok/s128+4,488 tok/s

  • Starter

    RTX 4000 Ada, 20 GB

    gpt-oss-20b

    Single-user speed
    78 tok/s
    Users, first token <1 s (p95)
    32+
    Total throughput
    799 tok/s

    Llama 3.1 8B (FP8)

    Single-user speed
    37 tok/s
    Users, first token <1 s (p95)
    32+
    Total throughput
    846 tok/s

  • Standard

    2× RTX 4000 Ada, 40 GB

    Qwen3 30B MoE (FP8)

    Single-user speed
    104 tok/s
    Users, first token <1 s (p95)
    32
    Total throughput
    871 tok/s

  • Performance

    RTX PRO 6000 Blackwell, 96 GB

    gpt-oss-120b

    Single-user speed
    173 tok/s
    Users, first token <1 s (p95)
    64
    Total throughput
    3,039 tok/s

    Qwen3.6 35B MoE (FP8)

    Single-user speed
    175 tok/s
    Users, first token <1 s (p95)
    128+
    Total throughput
    2,977 tok/s

    Llama 3.3 70B (FP8)

    Single-user speed
    19 tok/s
    Users, first token <1 s (p95)
    16
    Total throughput
    1,192 tok/s

  • Multi-GPU

    2× RTX PRO 6000 Blackwell, 192 GB

    gpt-oss-120b

    Single-user speed
    226 tok/s
    Users, first token <1 s (p95)
    128+
    Total throughput
    4,488 tok/s

These results are based on specific models and hardware configurations. We’ll test your workload on its intended hardware during the compatibility check.

Full benchmark report available on request.

06Managed endpoint

Your model, behind a private endpoint.

Runvexity manages the infrastructure behind your endpoint, from deployment to ongoing operations.

ENDPOINT DASHBOARDA100 80GB × 4 · US East
Running

144 msavg latency · last 4 requests

200 OK · 4/4

GPU
A100 80GB × 4
dedicated to your endpoint
Region
US East
selected deployment region
Status
Running
monitored by Runvexity
ENDPOINT URL
https://api.runvexity.com/v1/endpoints/ep_xxxxxxxx
REQUEST LOG
TimeLatencyStatus
09:14:02142ms200 OK
09:14:01138ms200 OK
09:14:00151ms200 OK
09:13:59144ms200 OK

  1. Model

    Your existing model runs in the managed environment.

  2. Endpoint

    Your application connects through a private API.

  3. Operations

    Runvexity manages the infrastructure behind it.

07FAQ

Questions before you deploy.

Can I use my own model?

Yes. Share the model name, repository or artifact format, and your requirements. We check whether it can run on our infrastructure and what resources it needs, then deploy it on dedicated GPU infrastructure behind a private API.

What models and runtimes are supported?

We run compatible open models and customer-provided model artifacts. The exact model, format, license, and runtime are reviewed during the compatibility check. The performance section lists models we have already benchmarked, such as gpt-oss, Qwen3 and Llama.

Is the endpoint private?

Yes. Your model runs on GPU resources dedicated to your service, and your application reaches it only through an authenticated private API over TLS.

Which GPUs are available?

Configurations range from single GPUs for smaller models to multi-GPU setups for large ones. The infrastructure section shows the current options, and we confirm the right GPU for your workload during the compatibility check.

Can I choose the region?

Yes. Tell us your preferred region when you request access. Deployment depends on compatible capacity in that location, which we confirm before we deploy.

Can I scale later?

Yes. As usage grows or your requirements change, we work with you to adjust the setup, for example by moving to a larger GPU or a multi-GPU configuration.

How do I get started?

Request access and tell us what you want to run and where. We review your model and region requirements, run a compatibility check, and get back to you by email.

Ready to deploy?

Put your model behind a private endpoint.

Run your AI workload on dedicated GPU infrastructure, managed by Runvexity.

Request access

Tell us what you want to run and where. We’ll confirm compatibility and GPU capacity.

By submitting, you agree to our Privacy Policy.