Large Language Model (LLM) Development
Architect, customize, and deploy domain-specific Large Language Models engineered for high precision, proprietary knowledge, and minimal latency.
Engineering Capabilities & Core Specializations
MV Tech Labs builds custom LLM solutions that understand your industry jargon, proprietary workflows, and strict compliance rules. From architectural parameter sizing to custom tokenizer training, we engineer LLMs from foundation to production.
Domain-Specific Tokenizer & Vocabulary Optimization
Production-hardened implementation conforming to enterprise security, compliance, and sub-second latency targets.
Parameter-Efficient Fine-Tuning (PEFT / LoRA / QLoRA)
Production-hardened implementation conforming to enterprise security, compliance, and sub-second latency targets.
Instruction Tuning on Proprietary Corporate Datasets
Production-hardened implementation conforming to enterprise security, compliance, and sub-second latency targets.
Quantization & Fast Inference (vLLM, Ollama, TensorRT-LLM, GGUF)
Production-hardened implementation conforming to enterprise security, compliance, and sub-second latency targets.
Evaluation Frameworks (MMLU, BLEU, ROUGE, Human Evaluation Rubrics)
Production-hardened implementation conforming to enterprise security, compliance, and sub-second latency targets.
Tangible Enterprise Deliverables
When partnering with MV Tech Labs, your organization receives complete production artifacts ready for internal auditing and continuous deployment:
- Production-Optimized Custom LLM Weights with Quantized Variants
- High-Throughput Inference Server Setup (vLLM on GPU Cloud)
- Comprehensive Benchmark Evaluation Report against Standard Baselines
- API Client Libraries in Python, TypeScript, and PHP 8.4
Architectural Technology Stack
Our 4-Stage AI Engineering Methodology
We minimize risk and eliminate technical debt through structured, iterative deployment cycles that move from proof-of-concept to production hardening.
Common Questions About Large Language Model (LLM) Development
Clear answers on timelines, pricing models, intellectual property, and infrastructure requirements.
Should my company train an LLM from scratch or fine-tune an existing model?
For 95% of enterprise applications, fine-tuning an open-weights foundation model (LLaMA 3.3, Mistral, Qwen) on proprietary domain datasets delivers 10x lower costs and equal or superior accuracy compared to pre-training from scratch.
What hardware is required to host a custom LLM?
With 4-bit and 8-bit quantization (AWQ/GPTQ), high-performance 7B to 14B parameter models run efficiently on a single NVIDIA A10G/L4 GPU ($1–$1.50/hr), handling tens of thousands of daily requests.
Enterprise AI Production SLA & Governance Matrix
Every enterprise AI engagement at MV Tech Labs is backed by strict production benchmarks, continuous latency monitoring, and zero-data-retention security guarantees.
Zero Data Retention
Your proprietary datasets and runtime inference logs are strictly isolated within your private cloud VPC boundary.
99.9% Uptime Guarantee
Multi-region failover and semantic cache clustering ensuring uninterrupted production availability.
Token FinOps Control
Automated prompt compression and embedding deduplication that cuts monthly inference bills by up to 50%.
Schedule an Architecture Review
Connect directly with a Senior AI Solutions Architect from our Pune HQ within 2 business hours.