TOKEN FACTORY
Open models. Production ready.
Enterprise-scale inference for open models, from open weights to production delivery. Build with an OpenAI-compatible API on Aolani’s managed GPU infrastructure.
Overview
Build on your API gateway in minutes
A focused home for keys, balance, routing, and service health.
● ● ●curl https://<gateway-host>/v1/chat/completions \
+ -H "Content-Type: application/json" \
+ -H "Authorization: Bearer sk-demo••••" \
+ -d '{"model":"nvidia/nemotron-3-ultra-550b-a55b","messages":[…]}'Interactive Aolani console overview: API onboarding and platform shortcuts.
PLATFORM
From open weights to production delivery.
A single managed platform for model access, endpoint operations and production assurance.
NVIDIA ECOSYSTEM
H100 / GB200 / GB300-class + Dynamo
72 GPUs · One rack in service
Built on NVIDIA Dynamo, the open-source distributed inference framework for production GPU inference across H100, GB200, and GB300-class systems. The technology path follows NVIDIA’s roadmap.
Expand from H100 through GB200 and GB300-class systems to next-generation platforms. Hardware upgrades stay transparent at the API layer, so applications do not need to change.
Reported platform validation maintained high-concurrency throughput during elastic rescaling. Capacity is designed to grow linearly as resources are added.
01
Inference service
Text, code and reasoning models through an OpenAI-compatible API. Choose shared or dedicated endpoints.
02
Endpoint and model operations
Day-0-grade onboarding for new models, with a serving stack optimized per model and dual inference engines.
03
Operations assurance
Full lifecycle management from planning to deployment and operations, monitoring and alerting, a 99.9% SLA and repeatable delivery processes.
CORE CAPABILITIES
Build the product. We’ll run the inference.
A complete operating foundation for production AI, with model serving, scaling, security and support in one place.
Open models in production
Serve mainstream open models through an OpenAI-compatible API. Scale compute with demand, with prefill and decode scaling independently.
Scale rack by rack
Start with the configuration your workload needs—from H100 through GB200 and GB300-class infrastructure. Add capacity as demand grows, with a path to next-generation GPU platforms.
Clear token pricing
Pay per million tokens, with separate input and output rates. No hidden GPU or infrastructure fees; dedicated endpoints are priced by capacity.
Long context and agents
Work with million-token-scale context, native reasoning and function calling. Keep reasoning separate from the main response for a clear output structure.
Bring your own models
Deploy custom checkpoints directly to endpoints with the same security and performance guarantees as standard endpoints. Aolani manages the infrastructure behind your model.
Fully managed operations
Keep production covered with monitoring, alerting and closed-loop asset and incident management. A 99.9% SLA supports operations while Aolani manages the GPU infrastructure.
MODEL CATALOG
The open models your product depends on.
Six current open-weight model families for production workloads, with additional models available on request.
Explore model selection and request parameters in the Aolani playground. Responses are authored examples; no model is called.
1M context · reasoning
GLM 5.2
Large-scale reasoning for long-horizon agents, project-level software engineering, and complex multi-step automation.
1M context · multimodal · 2.8T
Kimi K3
A 2.8T open-weight multimodal reasoning model for complex coding, knowledge work, and long-horizon agent workflows.
1M context · 49B active MoE
DeepSeek V4 Pro 0423
A 1.6T-parameter sparse MoE with 49B active parameters for advanced reasoning, coding, and long-horizon agent workflows.
1M context · 95B active MoE
Qwen3.8 2.4T A95B
An open-weight 2.4T sparse MoE with 95B active parameters for coding, research, complex reasoning, and agentic workflows.
262K context · 55B active MoE
NVIDIA Nemotron 3 Ultra
NVIDIA’s 550B hybrid Transformer–Mamba MoE, with 55B active parameters for orchestration, coding agents, deep research, and enterprise tasks.
131K context · 5.1B active MoE
OpenAI gpt-oss-120b
OpenAI’s 117B open-weight MoE, activating 5.1B parameters per pass for reasoning, agents, tool use, and structured outputs.
ENDPOINT OPTIONS
Same model. Same outputs. Your performance profile.
Match the endpoint configuration to concurrency, responsiveness and cost. The figures below are reported configuration results; validate them against your model and traffic.
8 GPUs
Standard
1 Prefill + 1 Decode
4,600+ tok/s at high concurrency · 94 tok/s single-stream
For concurrent workloads, batch processing and cost efficiency.
16 GPUs
Performance
2 Prefill + 2 Decode
6,200+ tok/s at high concurrency · 120 tok/s single-stream
For low-latency chat and retrieval-augmented generation with long documents.
Need dedicated capacity?
Whole-node isolation, reserved capacity, bring-your-own-model support and a 99.9% SLA. Configure resources and scaling policies around your traffic profile.
ENGINEERED FOR INFERENCE
Performance comes from the whole stack.
Optimizations are applied per model, and enabled only after quality preservation is confirmed.
01
Prefill-decode disaggregation
Reported validation preserved high-concurrency throughput while prefill and decode scaled independently.
02
KV and prefix caching
Reported cache hit rates of 99–100% on repeated prefixes.
03
NVFP4 quantization
4-bit weights and activations make room for larger models and longer contexts within the same memory.
04
Continuous batching
Reported aggregate throughput of 6,200+ tok/s on 16 GPUs.
05
Cross-node communication fusion
Up to approximately 1.9× faster single-stream decoding in multi-node tensor-parallel scenarios.
06
Million-token context
A 150K-token document parsed in 9 seconds in the reported validation.
Reported validation also reached 100% request success and a peak of 7,600+ tok/s. The peak is separate from the documented 16-GPU configuration result. Model, workload and test conditions affect performance; review your requirements with Aolani.
DEVELOPER EXPERIENCE
Your existing API workflow. A new inference foundation.
Use /v1/chat/completions with streaming and the full vLLM parameter set. Keep the familiar OpenAI client and change the endpoint.
PYTHON / OPENAI SDK
Example configuration. Replace <gateway-host> and YOUR_API_KEY with your provisioned endpoint and key.
Separate reasoning output
reasoning_content keeps reasoning separate from the main response.
Native function calling
Automatic tool choice is enabled for agent workflows.
One protected gateway
TLS encryption, API-key authentication and per-project admission control through a single entry point.
Interactive open-model API playground with OpenAI-compatible examples.
SECURITY AND DATA
Control over the resources. Clarity on the data.
Dedicated deployment and gateway controls give production teams a clear operational boundary.
Dedicated resources
Requests and outputs stay within your exclusive cluster resources on dedicated endpoints, with whole-node isolation.
Never used for training
Customer requests and outputs are never used for model training.
Protected API access
TLS encryption, API-key authentication and per-project token-capacity admission control protect transport and access.
GETTING STARTED
A direct path to your production endpoint.
Bring your workload requirements. We’ll help define the model, endpoint and operating profile.
01
Scope your workload
Share model, context length, concurrency, latency and data-handling requirements.
02
Choose your endpoint
Select the Standard or Performance profile and shared or dedicated resources.
03
Integrate and validate
Connect with the OpenAI-compatible API and validate against your own production workload.
04
Operate and expand
Use managed monitoring and support, then adjust capacity as demand grows.
FAQ
Answers before you move to production.
Practical details on model access, performance, security and commercial options.
WHAT’S NEXT
The next layer of the platform.
Planned capabilities. Speak with Aolani about timing and availability.
PLANNED
Embedding and RAG stack
A complete retrieval-augmented delivery path, combining embedding models, storage and inference on one platform.
PLANNED
Multi-region deployment
Expanded regional deployment to meet customer compliance and latency requirements.
BUILD WITH AOLANI
Put your next model into production.
Discuss your model, traffic and data requirements with the Token Factory team. We’ll help you find the right endpoint and capacity.