Token Factory
One API.
Every model.
Token Factory turns GPU capacity into production-grade model services: training, fine-tuning, evaluation, and inference, delivered as metered output rather than raw GPU rentals. One integration covers SiamLLM, frontier, and multimodal models.
Tokens here are AI inference output, not cryptocurrency.
1{
2 "model": "siam-llm-th",
3 "messages": [{ "role": "user", "content": "สรุปสัญญาฉบับนี้" }],
4 "stream": true
5}The path
From GPU capacity to production tokens
Running AI in production takes more than access to a model. It takes compute that is ready when you are, routing that survives a bad day, tenancy and quotas your finance team can read, and records an auditor can follow. Token Factory carries all of it on one contract, so your team integrates once and ships product.
Delivery
The delivery stack, top to bottom
Seven capabilities organized in three layers. Resources keep it stable, the platform keeps it governed, and a single API keeps it simple.
Access
High-availability inference API
One unified entry point for chat, generation, knowledge retrieval, function calling, and embeddings. Low latency at high concurrency, ready for production on day one.
Platform
Unified model hub
Language, embedding, image, video, and industry models behind one protocol. Switch freely without re-integration.
Enterprise workspace
Isolated workspaces with their own keys, permissions, quotas, logs, and bills, managed per project and team.
Token metering & billing
Usage-based pricing with tiered discounts and project-level split billing. Every call is traceable to a cost.
Model ops & optimization
Fine-tuning, quantization, caching, and acceleration tuned to your scenarios, improving quality while cutting long-run cost.
Foundation
Private deployment
Local deployment and private model hosting for data security, compliance, and dedicated performance, with dedicated operations.
GPU compute services
Training, inference, and batch capacity on demand, backing high concurrency and custom models with elastic expansion.
How the layers support each other
GPU and private capacity form the resource base
Compute, data retention, isolation, and dedicated performance.
The model hub and optimization form the capability pool
Unified access, continuously tuned for key scenarios.
Workspace and billing complete governance
Teams, keys, quotas, bills, and audit in one place.
The inference API delivers to your business
One stable endpoint, ready for production traffic.
Model lineup
Every major model, one contract
SiamLLM for Thai, plus frontier and multimodal models behind a unified protocol, cutting selection, migration, and maintenance cost.
| Rank | Model | Best for | Access |
|---|---|---|---|
| 01 | SLSiamLLMSiam GPT |
Thai language, local context, long documents | |
| 02 | CFClaude Fable 5.1Anthropic |
Long context, complex reasoning | |
| 03 | COClaude Opus 5Anthropic |
Code and agents | |
| 04 | G5GPT-5.6 SolOpenAI |
General purpose, wide ecosystem | |
| 05 | GMGemini 3.1 ProGoogle |
Multimodal, science | |
| 06 | DSDeepSeek V4DeepSeek |
Code, cost efficiency | |
| 07 | QWQwen3.8 MaxAlibaba |
Multimodal, Chinese and regional languages | |
| 08 | LLLlama 3.1Meta, served via NVIDIA NIM |
Open weights, private deployment |
Bars indicate platform availability and integration depth. The lineup expands as models are validated.
NeMo Retriever embedding
Vector representations for search and retrieval-augmented generation, served as NVIDIA NIM microservices.
NeMo Retriever reranking
Second-pass ranking that lifts answer quality on large Thai and English knowledge bases.
Multilingual embeddings
Thai and English in one vector space, so a Thai query finds an English document and back again.
Image generation
Creative and marketing image generation through the same API, key, and bill as text models.
Video generation
Short-form video generation for content teams, with batch submission and concurrency control.
Document & vision understanding
Thai document parsing, OCR, and image understanding for forms, contracts, and receipts.
Domain adaptation on SiamLLM
Fine-tuning on your own corpus, evaluated against your task set before anything reaches production.
Regulated deployments
Government, finance, and healthcare workloads served from isolated capacity with auditable operations.
Enterprise knowledge bases
Retrieval pipelines built with NeMo Retriever, curated with NeMo Curator, and kept current as your data changes.
Service level
More than a connection. A service level.
From GPU to token, self-operated technology and enterprise governance keep every call stable, transparent, and scalable.
Intelligent multi-dimensional routing
Load, latency, capacity, health, and cost are weighed together, with dynamic routing and failover that switches in seconds.
Enterprise-grade governance
Tenants, permissions, keys, quotas, audit trails, billing, and risk control, covered end to end.
Full-stack, GPU to token
Infrastructure and model operations are optimized as one path, enabling customization and fast response.
One protocol, every vendor
Standardized calls across providers remove duplicate development and migration cost.
Three delivery modes
Public cloud, brand partnership under your own domain, or fully private deployment.
Metered, optimized billing
Pay for what you use, while cost-aware scheduling continuously reduces long-run spend.
Solutions
Built for your operating reality
Stability, cost, compliance, and delivery requirements differ by team. Token Factory adapts from standard API access to fully private deployment.
SCENARIO
MATCHING
Pain points
01Peak traffic stalls inference interfaces
02Maintaining several vendor APIs is expensive
03Team usage and budgets are hard to track
Token Factory response
01One unified model API, integrated once
02Intelligent scheduling with pressure absorption and failover
03Project-level permissions, quotas, and billing
Stable support for AI customer service, assistants, knowledge bases, and SaaS AI features.
SCENARIO
MATCHING
Pain points
01Funding and engineering resources are limited
02Model experimentation means constant switching
03No capacity to run platform operations
Token Factory response
01Out-of-the-box cloud service
02Free model switching on one protocol
03Transparent usage-based token billing
Lower cost from prototype through testing to commercial launch.
SCENARIO
MATCHING
Pain points
01Strict data security and compliance demands
02Critical workloads cannot tolerate interruption
03Operations must be fully auditable
Token Factory response
01Private deployment with isolated compute in Thailand
02Multi-node failover and high availability
03Tiered permissions with full-chain audit
Meets classified knowledge bases, intelligent approval, and financial risk control.
SCENARIO
MATCHING
Pain points
01Multi-model experiments are cumbersome to switch
02Batch inference hits rate limits
03Experiment records are hard to trace
Token Factory response
01Unified multi-model calling and comparison
02Elastic concurrency that scales on demand
03Complete call logs and billing archives
Faster evaluation and research, with less idle capacity spend.
SCENARIO
MATCHING
Pain points
01No in-house compute or R&D resources
02Hard to build an independent brand
03Billing and customer systems incomplete
Token Factory response
01Your own brand, domain, and tenant
02Separate keys and revenue-split billing
03Ongoing platform operations support
Launch your own AI inference service and focus on customers.
SCENARIO
MATCHING
Pain points
01Text, image, and video tools are scattered
02Batch production lacks concurrency
03Content costs are hard to control
Token Factory response
01Full multimodal model aggregation
02One interface and one bill
03High-concurrency batch generation
An integrated production pipeline for creative and media teams.
Architecture
The path a request takes
From application integration down to model and compute resources, every layer carries its own security, scheduling, governance, and failover capability.
Application scenarios
Unified access
Security & governance
Intelligent scheduling
Model & compute resources
Security matrix
Every layer is independently auditable. Failover and isolation are tested continuously against the layer above.
Underneath
Running on NVIDIA accelerated computing
Token Factory is served from the same infrastructure plan that powers SiamGPT and SiamLLM, so what you call through the API is what we operate ourselves.
- Computing infrastructure: We plan to deploy NVIDIA DGX B200 for training and serving large AI models. Our products run on NVIDIA GPUs.
- Data pipeline: For data ingestion we implement the NVIDIA NeMo Retriever, followed by a data curation process, then the synthetic data generation pipelines from NVIDIA NeMo Curator.
- Query orchestration: Pre-filters and vector search build context, then NeMo Retriever and NIM microservices produce accurate and contextually relevant responses.
50+
Mainstream models targeted for integration through one protocol.
99.99%
Service availability objective for the inference API.
24/7
Technical support and monitoring for production workloads.
Thai-first
Native Thai understanding from SiamLLM, with English on the same endpoint.
Start a conversation
Whether you are validating an AI idea or running enterprise workloads, we provide the right models, stable services, and sustained engineering support.