Running PrismML as a service, AI Infrastructure

PrismML Agency Implementation, Local AI Deployment for Client Products

Learn how to integrate Ternary Bonsai models into client applications, coding agents, and computer-use workflows without cloud API dependencies. This course teaches agencies to architect on-device AI solutions that reduce per-token costs, eliminate vendor lock-in, and keep client data private while delivering reasoning, vision, and code generation at 143 tokens/second on consumer hardware.

Open the decision record for PrismML

What does running PrismML for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Not published
Time to first value
Not published
Payback
Not modeled
Guided implementation
8 hours

Is PrismML worth running as a client service?

PrismML provides a credible open-source technical foundation, 98.2% benchmark retention at a 5.9GB footprint under Apache 2.0, that agencies can package as a managed local-AI deployment service. What remains unknown is the agency's own labor and hardware cost basis, since PrismML publishes no paid tiers, so no ROI can be modeled without those agency-supplied inputs.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • Hugging Face access to download Ternary and 1-bit Bonsai model weights under Apache 2.0
  • NVIDIA GPU with CUDA drivers or Apple device with MLX for local inference
  • GitHub access for model and kernel repositories
  • Cline or compatible coding-agent harness for agentic workflow testing
  • Client domain-specific training data for optional Bonsai post-training

People and inputs

  • ML engineering capacity to install custom low-bit kernels and validate 1.76 effective bits per weight
  • Benchmark and whitepaper review process to verify the 98.2% performance retention claim on client hardware
  • Hardware provisioning plan covering 5.9GB model footprint and 262K-token context memory requirements
  • Inference optimization expertise to tune for 143 tokens/second throughput and 40% energy reduction

Estimated investment: PrismML is open-source under Apache 2.0 with no published pricing tiers; vendor license cost is $0, so the agency's investment is engineering labor plus GPU or Apple silicon hardware. No setup fees are published.

Included with the course

7 working documents for delivering this service.

  • PrismML Client Intake Questionnaire for On-Device AIworksheet
  • Bonsai Model Selection and Hardware Compatibility Matrixchecklist
  • Local Inference Architecture SOP: CUDA and MLX Deploymentsop
  • Cost Comparison Template: Cloud APIs vs. Local Bonsai Modelstemplate
  • Post-Training Workflow Guide for Domain-Specific Bonsai Customizationguide
  • Computer-Use and Coding Agent Integration Checklistchecklist
  • Client Data Privacy and Compliance Positioning Documenttemplate

Listed by name. These documents are not yet published as individual downloads.