Self-deploy frontier models on hardware you can afford | Tiyuvta

Tiyuvta helps organizations select, adapt, evaluate, and self-deploy open AI models on their own hardware or cloud accounts with engineering support.

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Self-deploy frontier models on hardware you can afford | Tiyuvta

Introduction

Overview

Tiyuvta is an engineering service for choosing and self-deploying open AI models on hardware controlled by the customer or in the customer’s cloud account. Its work connects model selection, hardware constraints, workload quality, operating cost, deployment, and continuing support.

Recommendations are based on measured configurations rather than model size alone. The service considers whether a model fits memory as well as response time, request size, concurrent load, answer quality, and total operating cost.

Services

  • Model and hardware assessments compare candidates, establish a workload baseline, and provide a cost recommendation before hardware is purchased.
  • Deployment and optimization match models and serving engines to available hardware, then provide an installed configuration, measurements, and operating instructions.
  • Fine-tuning and evaluation test the quality gap before training and produce evaluation results and deployment artifacts when adaptation is supported by evidence.

Supported deployment targets range from compact local systems and individual GPUs to multi-GPU servers and cloud GPU environments.

Delivery Process

The engagement begins by defining the task, examples, expected load, quality checks, hardware, and budget. Candidate models and hardware options are then compared against those requirements.

Adaptation may include fine-tuning, architecture changes, quantization, pruning, or serving-engine changes when measurements identify a relevant gap. The selected system is installed, tested, documented, and handed over with support terms specified in the proposal.

Research and Cost Evaluation

Tiyuvta publishes inspectable experiments covering serving engines, quantization, pruning, speculative decoding, and Hebrew models. Reports describe the test setup and limitations so that experimental measurements are not presented as guarantees for a different deployment.

The service can also compare an existing API bill with the full cost of operating an appropriate open model. Its proposals separate setup and running costs while accounting for adaptation, infrastructure, deployment, operation, and support. Customers retain control of the resulting deployment, data, and logs.