Private beta · Managed AI infrastructure

Managed infrastructure for inference, training, and RL workloads.

Nestor helps AI teams run production inference, fine-tuning, distributed training, and RL / post-training workloads on dedicated GPU infrastructure. We handle capacity, provisioning, orchestration, networking, storage, monitoring, and infrastructure support so your team can focus on models, data, and product.

Managed inference deploymentsTraining and fine-tuning clustersRL / post-training environmentsDedicated GPU capacitySLURM or KubernetesHuman infrastructure support
nestor/compute

Request architecture review

Tell us what you are trying to run

We will follow up to scope capacity, configuration, and onboarding.

By submitting, you agree to be contacted about your request.
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nestor / managed environment
env-7af · live
Workload path
01
Workload
02
Managed env
03
GPU cluster
04
API · jobs · rollouts
Status
  • Capacity reserved
    8× H200 · region us-east
  • Drivers configured
    CUDA 12.6 · NCCL 2.21
  • Cluster online
    slurm · 8 nodes · healthy
  • Monitoring active
    gpu · node · job
  • Support channel open
    shared · 1 business day
operated by nestoruptime · 14d
Services

One managed infrastructure partner for the full model lifecycle.

Inference, training and fine-tuning, and RL / post-training — each on dedicated infrastructure scoped, configured, and operated by Nestor.
01 · Inference

Managed inference infrastructure

Deploy custom models on dedicated GPU infrastructure with the runtime, networking, observability, and support layer managed by Nestor.

  • Dedicated GPUs for steady inference workloads
  • Container and model deployment support
  • vLLM, TensorRT-LLM, TGI, or custom runtime support where applicable
  • Endpoint, API, and private access patterns
  • GPU health, utilization, and infrastructure monitoring
nestor/compute

Run inference with Nestor

Tell us what you are trying to run

We will follow up to scope capacity, configuration, and onboarding.

By submitting, you agree to be contacted about your request.
02 · Training & fine-tuning

Managed training and fine-tuning clusters

Run fine-tuning, distributed training, and research workloads on dedicated GPU clusters configured around your framework, storage, and networking needs.

  • Single-node and multi-node GPU clusters
  • SLURM or Kubernetes orchestration
  • CUDA, drivers, containers, and framework setup
  • High-throughput storage and networking options
  • Support during onboarding and active runs
nestor/compute

Scope a training cluster

Tell us what you are trying to run

We will follow up to scope capacity, configuration, and onboarding.

By submitting, you agree to be contacted about your request.
03 · RL / Post-training

Infrastructure for RL and post-training

Support RLHF, GRPO, evaluation, rollout generation, and post-training workflows with dedicated GPU capacity and managed cluster operations.

  • Separate environments for generation, reward, eval, and training jobs
  • GPU capacity planning for bursty RL workloads
  • Support for multi-stage post-training pipelines
  • Cluster health and job-level infrastructure visibility
  • Hands-on setup for research and production teams
nestor/compute

Plan an RL environment

Tell us what you are trying to run

We will follow up to scope capacity, configuration, and onboarding.

By submitting, you agree to be contacted about your request.
Managed scope

What Nestor manages.

Six operational layers between your workload and the hardware — scoped during onboarding and run by Nestor for the duration of the commit.
Layer 01
Capacity planning

We help map your workload to the right GPU type, cluster size, term, storage profile, and deployment model.

Layer 02
Provisioning

Dedicated GPU environments are configured for your workload rather than dropped into a generic shared pool.

Layer 03
Orchestration

Managed SLURM or Kubernetes for training, fine-tuning, inference, and multi-user research workflows.

Layer 04
Runtime setup

CUDA, NVIDIA drivers, containers, ML frameworks, inference engines, and environment compatibility.

Layer 05
Networking and storage

Secure access, private networking options, attached storage, and data movement planning.

Layer 06
Monitoring and support

Infrastructure-level monitoring for GPU health, utilization, node status, and onboarding support.

Reliability

For workloads where cheap spot GPUs are not enough.

Nestor is built for teams that need predictable GPU access, managed environments, and a human operator when infrastructure issues block model work.
No spot interruptions

Workloads are not scheduled against a shared spot pool. Capacity stays reserved for the term you commit to.

Dedicated capacity during the commit

Your GPUs are not multiplexed with other tenants — the same nodes are available for the full duration.

Environment configured around your workload

Drivers, runtime, orchestration, storage, and access patterns are tuned to your model and framework.

Support channel for onboarding and production runs

A direct line to the Nestor team for infrastructure questions, environment changes, and incident triage.

Service models

Pick the engagement that fits the workload.

Every model is delivered on dedicated GPU infrastructure with Nestor as the infrastructure operator. Pricing and configuration are scoped during onboarding.
Service

Managed Inference

What we provide
Dedicated GPUs, model and container deployment, runtime configuration, endpoint and access patterns, infrastructure monitoring.
Best for
Production APIs, batch inference, custom model hosting, high-throughput generation.
nestor/compute

Managed Inference — Talk to us

Tell us what you are trying to run

We will follow up to scope capacity, configuration, and onboarding.

By submitting, you agree to be contacted about your request.
Service

Managed Training Clusters

What we provide
Single or multi-node GPU clusters, SLURM or Kubernetes, framework setup, attached storage, networking, and run support.
Best for
Fine-tuning, distributed training, research teams, short-term training runs.
nestor/compute

Managed Training Clusters — Talk to us

Tell us what you are trying to run

We will follow up to scope capacity, configuration, and onboarding.

By submitting, you agree to be contacted about your request.
Service

RL / Post-training Environments

What we provide
Capacity for generation, reward, eval, and training jobs; multi-stage pipeline support; cluster and job-level visibility.
Best for
RLHF, GRPO, rollout generation, reward model training, eval pipelines.
nestor/compute

RL / Post-training Environments — Talk to us

Tell us what you are trying to run

We will follow up to scope capacity, configuration, and onboarding.

By submitting, you agree to be contacted about your request.
Service

Private AI Infrastructure

What we provide
Dedicated environments, private networking options, customer-specific access, custom storage and security scoping, longer terms.
Best for
Enterprise teams, regulated workloads, private networking, custom security or storage requirements.
nestor/compute

Private AI Infrastructure — Talk to us

Tell us what you are trying to run

We will follow up to scope capacity, configuration, and onboarding.

By submitting, you agree to be contacted about your request.
Infrastructure

Available GPU infrastructure.

Nestor scopes dedicated capacity based on workload, term, region, networking, and storage requirements. Availability changes frequently, so we confirm configuration and pricing during onboarding.

Frontier training and post-training

ModelVRAMMemory BWCommon workloads
NVIDIA B300
Blackwell Ultra. Built for trillion-parameter training.
288 GB HBM3e8,000 GB/sFrontier training, large-scale post-training, high-memory inference.
nestor/compute

Check availability — NVIDIA B300

Tell us what you are trying to run

We will follow up to confirm capacity, term, and pricing.

By submitting, you agree to be contacted about your request.
NVIDIA B200
Blackwell. Native FP8 / FP4 throughput.
192 GB HBM3e8,000 GB/sTraining, FP8 inference, large model serving.
nestor/compute

Check availability — NVIDIA B200

Tell us what you are trying to run

We will follow up to confirm capacity, term, and pricing.

By submitting, you agree to be contacted about your request.
NVIDIA H200 SXM
Hopper. 1.4× HBM vs H100, ideal for long-context workloads.
141 GB HBM3e4,800 GB/sLong-context inference, fine-tuning, memory-heavy workloads.
nestor/compute

Check availability — NVIDIA H200 SXM

Tell us what you are trying to run

We will follow up to confirm capacity, term, and pricing.

By submitting, you agree to be contacted about your request.
NVIDIA H100 SXM
Hopper. NVLink, distributed training proven.
80 GB HBM33,350 GB/sDistributed training, fine-tuning, high-throughput inference.
nestor/compute

Check availability — NVIDIA H100 SXM

Tell us what you are trying to run

We will follow up to confirm capacity, term, and pricing.

By submitting, you agree to be contacted about your request.

Cost-efficient training and inference

ModelVRAMMemory BWCommon workloads
NVIDIA A100 SXM
Ampere. Proven workhorse for training and inference.
80 GB HBM2e2,039 GB/sFine-tuning, research, batch inference.
nestor/compute

Check availability — NVIDIA A100 SXM

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We will follow up to confirm capacity, term, and pricing.

By submitting, you agree to be contacted about your request.
NVIDIA RTX Pro 6000 Blackwell
Blackwell. 96 GB GDDR7. Fits 70B FP8 on a single card.
96 GB GDDR71,792 GB/sCost-efficient inference, 70B-class serving, development.
nestor/compute

Check availability — NVIDIA RTX Pro 6000 Blackwell

Tell us what you are trying to run

We will follow up to confirm capacity, term, and pricing.

By submitting, you agree to be contacted about your request.
NVIDIA L40S
Ada. 48 GB GDDR6. Steady inference and lightweight fine-tunes.
48 GB GDDR6864 GB/sInference, embeddings, lightweight fine-tuning.
nestor/compute

Check availability — NVIDIA L40S

Tell us what you are trying to run

We will follow up to confirm capacity, term, and pricing.

By submitting, you agree to be contacted about your request.
Onboarding

From workload to running infrastructure.

A direct path from architecture review to a stable, supported environment — scoped with your team before any capacity is committed.
  1. Step 01
    Architecture review
    You share model size, workload type, framework, expected usage, storage needs, and preferred term.
  2. Step 02
    Capacity and deployment plan
    Nestor recommends the GPU configuration, orchestration model, storage pattern, access model, and onboarding path.
  3. Step 03
    Environment setup
    We provision the environment, configure drivers, containers, networking, storage, access, and monitoring.
  4. Step 04
    Acceptance and handoff
    Your team validates the environment. We support setup issues and help stabilize the first workload.
  5. Step 05
    Ongoing managed support
    Nestor remains the infrastructure operator for support, monitoring, changes, and future capacity planning.
nestor/compute

Request architecture review

Tell us what you are trying to run

We will follow up within one business day to start scoping.

By submitting, you agree to be contacted about your request.
Security & access

Private environments with controlled access.

Environments are scoped to one customer. Access patterns, network boundaries, and container provenance are agreed during onboarding and operated by Nestor.
  • Dedicated customer environments
  • SSH-key based access
  • Optional VPN or private connectivity where available
  • Customer-specific users and permissions
  • No public shared notebook environment by default
  • Support for private containers and custom images
  • Export control and acceptable use restrictions
Note

Nestor is in private beta. We do not currently claim SOC 2, ISO, HIPAA, or other compliance certifications. Security and compliance roadmap is available on request.

Specific networking, storage, or access requirements are scoped with your team during onboarding rather than assumed by default.

Onboarding selected teams

Need managed GPU infrastructure for your next model workload?

Nestor is onboarding selected AI teams that need dedicated infrastructure for inference, training, fine-tuning, and RL / post-training.

nestor/compute

Request architecture review

Tell us what you are trying to run

We will follow up within one business day.

By submitting, you agree to be contacted about your request.
nestor/compute

Tell us what you are trying to run

Tell us what you are trying to run

Workload type, model, framework, GPU preference, and timing — we will scope from there.

By submitting, you agree to be contacted about your request.