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PRIVATE CLOUD + ON-PREMISES LLM SERVING

Private LLM deployment inside your security boundary.

g factor promotes qualified open-model adapters and checkpoints to infrastructure governed by your organization. Sensitive prompts, outputs, model weights, and operational evidence remain inside your private cloud or on-premises environment while applications use controlled, versioned endpoints.

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01Customer-controlled infrastructure
02Private cloud or on-premises
03Owned portable model artifacts
04Governed versioned endpoints

WHY G FACTOR

Built for owned, measurable model skills.

01

Enable workloads public APIs cannot accept

Keep sensitive inference within your organization so regulated and data-sensitive teams can adopt Deeply Specialized AI without routing proprietary context through a public model endpoint.

02

Retain model and data sovereignty

Own the adapters, checkpoints, tokenizer, deployment configuration, prompts, outputs, and operational records instead of depending on behavior available only through a closed provider.

03

Control the privacy-performance tradeoff

Operate efficient inference inside your boundary while balancing capacity, throughput, latency, and cost for each production workload.

CAPABILITIES

Production LLM serving under your organization’s control.

Qualified model promotion

Deploy an approved artifact and preserve its identity, benchmark evidence, and provenance throughout serving.

  • Approved model and adapter revision
  • Linked benchmark and provenance records
  • Reviewable deployment approval

Customer-controlled serving

Operate specialized model skills inside private cloud or on-premises infrastructure for enterprise applications and agents.

  • Controlled endpoint access
  • Sensitive data stays within the chosen boundary
  • Usage and operational monitoring

Portable, efficient inference

Move owned artifacts between compatible serving targets and optimize the deployment without changing the qualified skill contract.

  • Portable adapters and checkpoints
  • Private cloud and on-premises targets
  • Privacy, throughput, latency, and cost controls

BEFORE YOU START

Common questions

What is private LLM deployment?

Private LLM deployment runs a model on infrastructure controlled by your organization. Applications use governed endpoints, while prompts, outputs, model weights, and operational evidence stay within the configured private cloud or on-premises boundary.

Can I deploy on-premises instead of using a public model API?

g factor supports private cloud and on-premises serving targets for qualified open-model artifacts. Deployment planning covers the model, GPU capacity, access controls, data boundary, throughput, latency, and operating cost for your workload.

What should be checked before a model reaches production?

Confirm the approved model and adapter revision, linked benchmark evidence, endpoint access, monitoring, and rollback boundaries. Keep the tokenizer and serving configuration tied to the qualified artifact so the deployed version is traceable.

EXPLORE THE PLATFORM

SFT + REINFORCEMENT LEARNINGLLM Post-Training Platform

Fine-tune and reinforce open-source LLMs with SFT, GRPO, governed datasets, distributed GPU training, and portable LoRA adapters.

Explore COMMERCIAL OPENENV MARKETPLACEPrivate AI Training Environments

Train specialized agents in private OpenEnv environments for Verilog, Modelica, Lean, OpenFOAM CFD, CAD/CAM, Blender, SKY130 analog IC design, and quant finance.

Explore EVALUATION + PROMOTIONReproducible LLM Evaluation

Qualify fine-tuned and reinforced LLMs with frozen held-out benchmarks, base-versus-adapter comparisons, promotion gates, and immutable provenance.

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PRIVATE BETA

Turn your workflow into a skill your model can own.

Bring a model, a workflow, or an evaluation problem. We'll map it to a focused post-training and qualification plan.

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