Private LLM

Model Optimization and Deployment

Tune, deploy, and infer large models inside your own server room—data never leaves your domain.

For enterprises that want a dedicated LLM without sending data off the intranet, Model Optimization and Deployment delivers a full loop from training to launch to serving—covering tuning, deployment, and inference.

Talk about private models

Three-stage pipeline from tuning to deployment to inference — Tuning → Deployment → Inference — one private lifecycle.

Private on-prem model serving inside the enterprise boundary — Weights, data, and traffic stay inside your network edge.

Model tuning — train an LLM that belongs to your company

Goal: at controlled cost and timeline, train a dedicated model that fits your business—not a generic “standard answer.”

1. Data structuring

Enterprise data is often scattered across document libraries, tickets, chats, and knowledge bases—messy formats, uneven quality. We provide:

Unstructured cleaning, denoising, and deduplication
Auto-normalization into training formats (instruction–answer pairs, multi-turn dialogue, domain Q&A, etc.)
Quality filtering and semi-automatic labeling so training data is usable and trustworthy

2. Base LLM selection and fine-tuning

Based on task type (Q&A, summarization, code, domain reasoning), parameter scale, and hardware, we help evaluate mainstream open bases (Llama, Qwen, Mistral, DeepSeek, and more) and support multiple fine-tuning paths:

Parameter-efficient fine-tuning (LoRA / QLoRA)
Full-parameter fine-tuning
Preference alignment (RLHF / DPO)

3. Minimize training cost and time

Engineering practices that keep training spend in a reasonable range:

Mixed-precision training and distributed parallelism
Smart checkpointing and resume-from-failure
Hyperparameter search to cut wasted trials

Model deployment — securely on your own premises

Goal: run the model inside your physical boundary—data, weights, and inference never leave the domain.

Core capabilities:

Capability	Description
Private deployment	Deploy in your server room / intranet GPU cluster—no dependency on public cloud APIs
Data stays in-domain	From training data to inference requests, all traffic stays inside the enterprise network boundary
Containers & orchestration	Standard container images compatible with Kubernetes / bare metal and other private stacks
Access control & audit	Built-in permissions and operation audit logs for security and compliance reviews

Fits finance, healthcare, government, and other industries with strict data compliance needs.

Model inference — open-model catalog + high-performance serving

Goal: a callable open-model list with high throughput, low latency, and long-term stability in production.

Open model list

Open models (2026 releases)

Sources: official model cards and technical reports (Hugging Face / official releases). For selection reference only—verify the latest official info before production deploy.

Model	Released	Params (total / active)	Fit	License
DeepSeek-V4-Flash	2026-04	284B / 13B (MoE)	Lightweight / low-cost deploy	MIT
Qwen3.5-397B-A17B	2026-02	397B / 17B (MoE)	General chat / multimodal / agent	Apache 2.0
DeepSeek-V3.2	2026-01	671B / 37B (MoE)	Reasoning / tool use	MIT
DeepSeek-V4-Pro	2026-04	1.6T / 49B (MoE)	Million-token context / code generation	MIT
Kimi K3	2026-07	2.8T / 104B (MoE)	Complex reasoning / long context / native vision	Kimi K3 License

Sources: official model cards / technical reports. For reference only.

Core traits

Higher throughput

Continuous batching, quantized inference (INT8 / INT4 / FP8), tensor parallelism

Lower latency

KV-cache optimization, speculative decoding, request scheduling

Stable operations

24/7 monitoring and alerts, canary releases and rollback, SLA, regular security patches

Overview

Three stages at a glance

Stage	Core question	Key capabilities
Tuning	How to train a dedicated model at low cost	Data structuring · base selection & fine-tuning · cost/time optimization
Deployment	How to keep the model in your own room	Private deploy · data in-domain · access & audit
Inference	How to serve stably and efficiently	Open-model catalog · throughput & latency · long-term ops

Model Optimization and Deployment — so enterprises own a real LLM of their own, from training to serving, fully controllable and fully on the intranet.

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