Fine-tuning
Domain adaptation and performance improvement using your data and task requirements.
Global Open-Model AI Solutions
AI infrastructure,
built for real work.
Deployment, optimization, and global compute services.
Access leading open models through OpenAI-compatible APIs, adapt models to your domain, and lease dedicated compute for training, fine-tuning, and inference—all through one enterprise-oriented delivery path.
GlobalCoverage options
Lower costAPI and compute
Enterprise-readyPrivate deployment
ScalableFrom GPU to cluster
Illustrative facility imagery. Site-specific hardware, capacity, and availability are confirmed for each engagement.
Global deploymentFlexible regional options
Model choiceOpen models, one API path
Private by designIsolation and access controls
Capacity that scalesDedicated GPU resources
01 / Low-Cost Open-Model API
One compatible endpoint.
More room to build.
OpenAI-compatible API endpoints for leading open models, prepared for global deployment with predictable performance and a lower cost profile.
01
Indicative target discounts
Compared with public API pricing for selected models
ModelDiscount rangeTarget
Kimi K2.625%–30% off
Qwen3.5-397B30%–40% off
gpt-oss-120B30%–40% off
MiniMax M2.725%–30% off
DeepSeek R125%–39% off
Kimi K340%–50% off
DeepSeek V4.1 FlashUp to 50% off
MiniMax M330%–40% off
GLM-5.330%–40% off
0%10%20%30%40%50%
OpenAI-compatible integrationThese are current target commercial discount ranges—not guaranteed quotes. Final pricing depends on model availability, workload, context length, throughput, and deployment mode.
02 / Custom Model Optimization
Adapt the model to the work.
We tailor open models to domain requirements, latency targets, cost efficiency, and quality objectives—then evaluate them against real tasks.
Post-training
SFT, RLHF, DPO, and continued pre-training to align model behavior with intended use.
Pruning
Model compression to reduce inference cost and support higher throughput.
Quantization
INT8/INT4 and advanced quantization techniques for efficient deployment.
Distillation
Smaller, faster models designed to retain the capabilities that matter for the task.
Inference optimization
Improve latency, throughput, and cost for production workloads.
Evaluation & benchmarking
Comprehensive evaluation and testing on representative real-world tasks.
Private deployment
On-premises and VPC deployment options for sensitive workloads and data.
From adaptation to operation.Optimization can be scoped as a focused engagement or paired with API access and dedicated compute, depending on data boundaries, quality targets, and operating requirements.
03 / Dedicated Compute Leasing
Capacity for every stage
of the model lifecycle.
Dedicated, global, self-built compute capacity for training, fine-tuning, and inference—from single-GPU workloads to multi-node clusters.
▦ A wide hardware portfolio
NVIDIA
A100A800H100H800H20H200B200B300L40SRTX 4090RTX 5090
Huawei
910B910C950
▤
Single GPU to multi-node
Dedicated clusters and flexible compute resources for workloads of different scales.
⑂
Network, storage & scheduling
High-speed networking, storage, and workload scheduling for training and large-scale inference.
◎
Flexible global deployment
Regional deployment options aligned to workload, latency, and data requirements.
☁
Private or shared delivery
Dedicated clusters or multi-tenant options with enterprise-grade isolation.
▦
Self-built capacity
The poster states 50,000P+ of global self-built compute capacity across its infrastructure footprint.
◇
More stable supply and pricing
Self-built data centers are designed to reduce exposure to compute price volatility and support long-term delivery.
50,000P+
Global self-built compute capacity
Capacity, hardware availability, region, and lease terms are confirmed against the requested configuration and delivery window.
04 / Why Work With Us
One partner, from model to infrastructure.
Research and engineering experience, compute partnerships, and end-to-end delivery help teams move from model selection to production infrastructure.
Research-led engineering
Team backgrounds spanning MIT, Stanford, and Tsinghua, combined with practical model and infrastructure work.
Global compute-center partnerships
Strategic partnerships intended to broaden compute coverage and available capacity.
End-to-end open-model delivery
Support from model adaptation and optimization through production deployment and infrastructure.
Infrastructure experience
Experience across AI infrastructure, distributed systems, model deployment, and optimization.
Enterprise delivery and efficiency
Lower total cost through a balance of reliability, workload fit, and measurable business value.
Self-built data centers
Infrastructure ownership and operations designed to support more stable supply and pricing.
05 / Data Security & Confidentiality
Security controls that follow your workload.
Security and deployment controls can be selected to match enterprise workloads, private deployments, and sensitive data handling requirements. Exact controls are agreed during solution design.
▦
Physical isolationDedicated physical resources for sensitive workloads.
◇
Tenant isolationLogical and workload isolation across shared environments.
◉
Encryption at restEncryption options for training data, checkpoints, and artifacts.
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Encryption in transitTLS-secured data transfer and service communication.
▤
Dual-layer encryptionOptional application-layer and storage-layer encryption for sensitive datasets.
⑂
Private networkingVPC, private links, and restricted network paths.
♙
Access control & auditingRole-based access, approval workflows, and audit logs.
◇
Data residency & retentionRetention policies and controlled deletion workflows.
Start with your workload
Tell us what you need to run. We’ll help shape the right path.
For enterprise proposals, customized pricing, private deployment, or benchmark-based optimization plans, contact us for a tailored solution.