Private LLM Deployment

Run open-weight language models in an environment owned or designated by your organization, with a service that can be called, monitored, and maintained. We can work with existing infrastructure or help plan equipment once procurement and implementation responsibilities are clear.

Core service one · From model selection to reliable operation · Shenzhen on-site scope agreed per project

Best fitAn organization with a model or infrastructure that needs deployment, scaling, or tuning

InputsModel license, target workload, hardware inventory, network and security requirements

OutputsDeployment configuration, API, monitoring plan, performance results, handover documents

AcceptanceMeasured under agreed model, precision, context length, and concurrency

Scope of work

Environment and capacity assessment

Check GPU memory, CPU, system memory, storage, networking, drivers, and runtime. Size a single-server or multi-node setup against model size, precision, context length, concurrency, and latency objectives. Hardware compatibility must be confirmed against the actual models and equipment.

Inference serving

Evaluate SGLang and vLLM against the target model and workload. Configure model loading, serving, concurrency, and recovery. Choose the serving stack from measured compatibility, throughput, latency, and operational complexity, rather than a framework preference alone.

Quantization and performance tuning

Where task quality permits, evaluate lower-precision model formats and compare memory use, response quality, time to first token, generation speed, and concurrency. Tune batching, caching, parallelism, and resource allocation while preserving a reproducible configuration baseline.

API and monitoring

Provide an authenticated model API and, where agreed, rate limiting, logs, and integration points for business systems. Monitor service health, GPU memory, request volume, error rates, latency, and capacity; assign alert recipients and response owners.

Deliverables and acceptance

Deliver the deployment topology, software and model version inventory, configuration, start and recovery procedures, API specification, monitoring checklist, and performance test results. Every performance result records the model version, input and output lengths, concurrency, and hardware conditions. A demonstration speed is not a universal performance commitment.

Knowledge-base development, custom business applications, and model post-training are scoped separately, with their own responsibilities and acceptance criteria.

Start the conversation

Share the target tasks, candidate model, existing hardware, and expected concurrent users. We will first determine whether the environment can carry the workload, then define implementation and test scope.

Related services: Model Post-Training · Advanced Model Engineering