Train, fine-tune, and run inference on open-source AI models through optimized GPU infrastructure. Built by researchers behind breakthrough AI efficiency techniques, delivering unmatched cost and performance advantages for enterprises building on open-source AI.
Platform Capabilities
Train foundation models and custom architectures on distributed GPU clusters with automatic checkpointing, mixed-precision training, and seamless scaling from single-GPU to multi-node configurations.
Customize pre-trained models for your specific use cases using LoRA, QLoRA, and full fine-tuning techniques. Support for instruction tuning, RLHF, and domain-specific adaptation workflows.
Deploy models to production with auto-scaling inference endpoints. Optimized for low latency with continuous batching, speculative decoding, and quantized model serving for cost efficiency.
Access thousands of pre-trained open-source models including Llama, Mistral, Falcon, and more. One-click deployment with optimized inference configurations and version management.
Access massive GPU clusters co-built with leading hardware partners. NVIDIA H100, A100, and next-generation accelerators with high-bandwidth networking optimized for distributed training.
End-to-end data processing infrastructure for preparing training datasets. Distributed data loading, preprocessing, tokenization, and caching with support for petabyte-scale datasets.
Comprehensive MLOps tooling for tracking experiments, comparing runs, and reproducing results. Integrated logging, metrics visualization, and model registry for complete lifecycle management.
Intelligent resource allocation and spot instance management to minimize costs. Real-time pricing, usage analytics, and automated scaling policies to optimize your GPU spend.
Enterprise-grade security with SOC 2 Type II, HIPAA, and GDPR compliance. Private VPC deployment, data encryption at rest and in transit, and comprehensive audit logging.
Seamlessly scale training across thousands of GPUs with automatic parallelization strategies including data parallel, model parallel, pipeline parallel, and ZeRO optimization.
Live dashboards for GPU utilization, training progress, and system health. Automated alerting for anomalies, job failures, and resource constraints with Slack and webhook integrations.
Comprehensive REST APIs and native SDKs for Python, JavaScript, and Go. Seamless integration with popular ML frameworks including PyTorch, TensorFlow, JAX, and Hugging Face Transformers.
High-throughput batch inference for processing large datasets offline. Queue management, priority scheduling, and automatic retry logic for reliable large-scale inference workloads.
Train and deploy models that understand text, images, audio, and video. Support for vision-language models, speech recognition, and generative multi-modal architectures.
Pay only for what you use with serverless model endpoints. Automatic cold start optimization, request-based billing, and zero-to-scale capabilities for unpredictable workloads.
Dedicated solutions architects, 24/7 technical support, and custom SLAs for enterprise customers. Migration assistance, architecture reviews, and priority access to new features.
The Future of AI Infrastructure
Our platform abstracts away the complexity of distributed computing, allowing your teams to focus on what matters: building exceptional AI models that transform your business and delight your users.
Proven at Scale
Train billion-parameter language models from scratch with distributed training across thousands of GPUs using FSDP and DeepSpeed ZeRO-3 optimization.
Deploy models to production with auto-scaling inference that handles millions of requests per day with sub-100ms latency and 99.99% availability.
Customize open-source models for domain-specific applications using LoRA adapters, RLHF, and instruction tuning with your proprietary datasets.
Accelerate AI research with flexible compute allocation, comprehensive experiment tracking, and seamless collaboration tools for distributed teams.
World-Class Infrastructure
Access to the latest NVIDIA H100 Tensor Core GPUs with 80GB HBM3 memory and third-generation Tensor Cores. Purpose-built for large-scale AI training with up to 9x faster training performance compared to previous generations.
Reliable A100 GPU pools for production inference and cost-effective training workloads. Available in 40GB and 80GB configurations with Multi-Instance GPU (MIG) support for flexible resource allocation.
InfiniBand HDR interconnects delivering 400Gb/s bandwidth between nodes. Optimized network topology ensures minimal latency for distributed training with NCCL and efficient all-reduce operations across the cluster.
High-performance parallel file systems with petabyte-scale capacity. Optimized for AI workloads with fast checkpoint writing, efficient data loading, and seamless integration with popular ML frameworks.
Kubernetes-native infrastructure with custom operators for ML workloads. Automatic GPU scheduling, resource quotas, and priority queues ensure fair and efficient allocation across teams and projects.
Data centers across North America, Europe, and Asia-Pacific regions. Choose deployment locations based on data residency requirements, latency needs, and GPU availability for your specific workloads.
Use Cases
Build sophisticated conversational agents that understand context, maintain multi-turn dialogues, and provide accurate, helpful responses across customer support, sales assistance, and internal knowledge management applications.
Accelerate software development with AI-powered code completion, generation, and review tools. Train models on proprietary codebases to understand internal conventions, APIs, and best practices unique to your organization.
Extract insights from unstructured documents at scale. Process contracts, reports, research papers, and regulatory filings with AI that understands domain-specific terminology and context for accurate information extraction and summarization.
Generate high-quality marketing copy, product descriptions, and creative content at scale. Fine-tune models to match your brand voice and style guidelines while maintaining consistency across all customer touchpoints.
Build semantic search engines and personalized recommendation systems that understand user intent and context. Deploy embedding models and retrieval systems that scale to billions of items while maintaining millisecond latency.
Accelerate AI research with flexible compute allocation and comprehensive experiment tracking. Run thousands of experiments in parallel, compare results, and iterate quickly on new architectures and training techniques.
Model Hub
Deploy thousands of pre-trained models with one click. Our optimized serving infrastructure delivers maximum performance from every model architecture.
Don't see your model? We support any Hugging Face compatible model architecture.
Request a ModelPerformance Benchmarks
Sustained throughput for Llama 3.1 70B inference on a single H100 node with continuous batching and speculative decoding enabled. Measured with real production traffic patterns.
Technical Specifications
Developer Experience
# GCSYSTEMS Inference API - Deploy models in minutes from gcsystems import Client # Initialize the client with your API key client = Client(api_key="gc_your_api_key_here") # Run inference on Llama 3.1 70B with streaming response = client.inference.create( model="meta-llama/Llama-3.1-70B-Instruct", messages=[ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": "Explain quantum computing in simple terms."} ], max_tokens=1024, temperature=0.7, stream=True ) # Process streaming response for chunk in response: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="") # Batch inference for high throughput batch_results = client.inference.create_batch( model="meta-llama/Llama-3.1-70B-Instruct", requests=[ {"messages": [{"role": "user", "content": prompt}]} for prompt in prompts ], max_concurrent=100 ) print(f"Processed {len(batch_results)} requests")
Developer Tools
First-class SDKs for Python, JavaScript, Go, and Rust with full type safety, auto-completion, and comprehensive documentation.
Drop-in replacement for OpenAI APIs. Migrate existing applications with a single line change to your base URL configuration.
Live dashboards for request latency, throughput, error rates, and GPU utilization with Prometheus and Grafana integration.
Auto-scaling that responds to traffic in seconds, not minutes. Handle traffic spikes without provisioning or capacity planning.
Git-like version control for models with branching, tagging, and rollback capabilities for safe production deployments.
Intuitive web interface for managing models, monitoring jobs, viewing logs, and configuring deployments without writing code.
Get in Touch
Whether you're training your first model or scaling to millions of requests, our team is here to help you succeed. Get in touch to discuss your requirements and learn how GCSYSTEMS can power your AI infrastructure.
(513) 379-8858
contact@gcsystems.co
708 Alhambra Blvd
Sacramento, CA 95816
FAQ
We offer NVIDIA H100 (80GB HBM3), A100 (40GB and 80GB variants), and L40S GPUs. H100 clusters are optimized for large-scale training with InfiniBand HDR networking and NVLink 4.0. A100 pools are ideal for inference and cost-effective training workloads. All GPU types support Multi-Instance GPU (MIG) for flexible resource allocation.
Training is billed per GPU-hour with options for on-demand, reserved, and spot instances. Inference is billed per million tokens or per request depending on your plan. We offer significant discounts for committed usage and enterprise contracts. Contact our sales team for detailed pricing tailored to your workload patterns and scale requirements.
Yes, absolutely. GCSYSTEMS supports training models from scratch on up to 10,000 GPUs with distributed training strategies including FSDP, DeepSpeed ZeRO, and pipeline parallelism. Our infrastructure is designed for training billion-parameter models with automatic checkpointing, fault tolerance, and optimized data loading pipelines.
GCSYSTEMS is SOC 2 Type II certified and HIPAA compliant. We support GDPR requirements with data residency options in multiple regions. All data is encrypted at rest (AES-256) and in transit (TLS 1.3). Enterprise customers can deploy in private VPCs with dedicated infrastructure, and we provide comprehensive audit logging for compliance requirements.
You can deploy pre-configured models from our Model Hub in under 5 minutes. Simply select a model, configure your endpoint settings, and start making API calls. For custom models, upload your weights and we'll optimize serving automatically. Our APIs are OpenAI-compatible, so existing integrations work with minimal code changes.
Yes, we support all major fine-tuning techniques including LoRA, QLoRA, and full fine-tuning. Upload your datasets in JSONL, Parquet, or Arrow format. We support instruction tuning, RLHF with human feedback, DPO, and custom training loops. Enterprise customers get dedicated data processing pipelines and custom data connectors.
All customers have access to documentation, community forums, and email support. Business plans include priority support with guaranteed response times. Enterprise customers get dedicated solutions architects, 24/7 phone support, custom SLAs, and proactive monitoring. We also offer professional services for architecture reviews, migration assistance, and custom integrations.