Own Your AI.
From API Consumer to Model Operator
Discover, fine-tune, and serve models on your infrastructure. Hardware-aware training, OpenAI-compatible APIs, and full model lifecycle management — from one platform.
Strategic Framework
The AI Model Maturity Framework
Where is your organization on the model operations journey?
API Consumer
Low Control, Low Customization
Pay per token. Use GPT-4, Claude, or Gemini as-is. Fastest time to first call, zero customization, full vendor dependence.
Model Curator
High Control, Low Customization
Discover and deploy open models. Run Llama, Mistral, Qwen locally via Ollama. Control your inference stack, but models remain general-purpose.
Fine-Tuner
Low Control, High Customization
SFT, DPO, and GRPO on cloud infrastructure. Domain-specific models that speak your language, but still cloud-dependent for training compute.
Model Operator
High Control, High Customization
Full lifecycle ownership. Local fine-tuning on your hardware, GGUF quantization, private inference endpoints. Maximum control and customization.
Most enterprises are stuck in Q1. ModelsX provides the platform to move from API consumer to full model operator — at your own pace, on your own timeline.
The Economics
The True Cost of AI at Scale
Three strategies, one platform.
Cumulative Cost Over Time
Estimated cost per million tokens at scale
API-Only
~$15–60 / 1M tokens
- Linear cost scaling
- Zero infrastructure overhead
- Full vendor lock-in
- No data sovereignty
Hybrid
Variable, optimized
- API for experimentation
- Local for production loads
- Moderate infrastructure
- Best cost-to-flexibility ratio
Self-Hosted
Near-zero marginal
- Hardware investment upfront
- Sub-linear cost at scale
- Full data sovereignty
- Complete model control
At production scale, self-hosted inference can reduce per-token costs by 10–50x compared to API pricing. ModelsX makes that transition seamless — start with APIs, migrate to self-hosted when the economics justify it.
The Workflow
A Complete Model Operations Lifecycle
Six phases. One unified platform.
Phase 1
Discover
Browse thousands of models across Ollama and HuggingFace.
Discover
Browse thousands of models across Ollama and HuggingFace.
- Search by task, size, architecture, or quantization
- Filter by parameter count, license, and provider
- One-click model inspection with full metadata
Capabilities
Four Pillars of Model Operations
Everything you need to run models in production.
Pull and run any model from the Ollama library. Manage running instances, view model details, tags, and parameters. Automatic GPU detection and memory allocation.
- Model library browser with search and filtering
- One-click pull, delete, and version management
- Running model management with resource monitoring
- Model detail inspection with parameter metadata
- Tag and variant selection for precision deployments
Hardware Intelligence
Your Hardware. Automatically Optimized.
ModelsX detects your compute and configures the optimal training pipeline.
Apple Silicon
M1 / M2 / M3 / M4
MLX Framework
Unified memory architecture enables efficient fine-tuning on consumer hardware. Native Metal acceleration for fast LoRA training.
- Unified memory — no CPU↔GPU transfer overhead
- Metal-accelerated matrix operations
- Efficient LoRA and QLoRA fine-tuning
NVIDIA GPU
RTX / A100 / H100
Unsloth Framework
CUDA-accelerated training with 2x speed improvement and 60% less memory. Full QLoRA support for parameter-efficient fine-tuning.
- 2x faster training vs standard approaches
- 60% less memory with quantized training
- QLoRA and full fine-tuning support
Cloud / No GPU
Any Machine
HuggingFace AutoTrain
No local GPU? Train on HuggingFace infrastructure. Pay per training hour with SFT, DPO, and GRPO methods. Download results to local.
- SFT, DPO, and GRPO training methods
- T4 to H100 hardware selection
- Cost estimation before training starts
You do not need to be a hardware expert. ModelsX auto-detects your compute environment and configures the optimal training pipeline — whether that is Apple Silicon, NVIDIA, or cloud.
Under the Hood
The Fine-Tuning Pipeline
From dataset upload to deployed model — orchestrated by Temporal across hours of training.
Dataset Processing
Model Fine-Tuning
GGUF Conversion
Dataset Processing
Model Fine-Tuning
GGUF Conversion
Ecosystem
The Intelligence Layer for Every Accelerator
ModelsX powers inference and training across the entire DecisionOS ecosystem.
Your models, your infrastructure,
your competitive edge.
Start with the model library. Fine-tune when you are ready.