Model Deployment
Put the right model and serving architecture onto the infrastructure you control.
- Model and hardware fit
- Distributed loading and serving
- Production API delivery
OUTPUTA deployed, integrated model service.
TOKEN FACTORY / YOUR INFRASTRUCTURE
Turn Existing GPU Infrastructure into Production AI Services.
Deploy, optimize, and operate enterprise model services on infrastructure you already own or control.
THE OPERATING GAP
GPUs Are Capacity. A Token Factory Makes Them Usable.
Installed hardware is only the starting point. A production service also needs model and hardware fit, distributed serving, memory and cache strategy, API delivery, observability, version control, and accountable operations.
Distributed serving Performance engineering Production operations Measured capacity
FOUR CONNECTED CAPABILITIES
From Installed Hardware to an Operable Model Service
Each engagement is shaped around the customer environment, with clear outputs at every layer.
Model Adaptation
Validate whether task-specific adaptation creates enough value to justify the added complexity.
- Evaluation baseline
- Data suitability review
- SFT or LoRA where justified
OUTPUTA measured model version and acceptance result.
Inference Optimization
Tune the full serving path against quality, latency, throughput, and infrastructure constraints.
- Quantization and memory
- Parallelism, batching, and cache
- Quality-performance validation
OUTPUTAn optimized, reproducible serving configuration.
Production Operations
Create the controls and operating model needed to keep the service reliable after launch.
- Observability and versioning
- Capacity planning and runbooks
- Managed operations or handover
OUTPUTAn operable service with clear ownership.
DELIVERY LIFECYCLE
Measure First. Build against Acceptance Criteria.
The work moves from a shared baseline to a production service and an agreed operating model.
- 01
Discover
Map the business workload, infrastructure, deployment boundary, candidate models, and target service levels.
Assessment scope and measurement plan - 02
Baseline
Measure representative workloads across approved software and hardware configurations.
Comparable quality and performance baseline - 03
Deploy & Optimize
Build the production service and tune it against the agreed acceptance criteria.
Production model service and optimized configuration - 04
Operate or Hand Over
Establish observability, runbooks, support ownership, and an ongoing improvement process.
Operational controls and clear ownership
DEPLOYMENT BOUNDARIES
Built for Environments You Control
Token Factory can be delivered into a private cloud, an owned data center, or another approved customer-controlled environment.
01Production API or internal service endpoint02Optimized inference configuration03Observability and operational controls04Performance and capacity baselines05Access and quota controls06Runbooks and support model
TOKEN FACTORY
Start with an Infrastructure Assessment.
Share your GPU environment, target workloads, current software stack, and operating goals.