LLM Fine-Tuning & LLMOps
We adapt large and small language models to your domain, data, and voice — delivering private models that are faster, cheaper, and more accurate for your use case than general-purpose APIs. 2026 is the year of fine-tuned small models, and we help you own that advantage.
What we deliver
- Domain adaptation and instruction tuning on your proprietary data
- Parameter-efficient fine-tuning — LoRA, QLoRA, PEFT — and preference tuning (RLHF/DPO)
- Small Language Models (SLMs) for edge and on-device, low-cost inference
- Quantization, distillation, and optimization for latency and cost
- Private, on-premise, and sovereign AI deployments for data residency & compliance
- Prompt & context engineering, evaluation, and continuous LLMOps monitoring
Technical stack
- Tuning
- LoRA, QLoRA, PEFT, full fine-tuning, RLHF, DPO, instruction tuning
- Optimization
- Quantization (GGUF, GPTQ, AWQ), distillation, pruning
- Serving
- vLLM, TGI, Ollama, TensorRT-LLM — on-prem or private cloud
- Base models
- Llama, Mistral, Qwen, Gemma, Phi and other open-weight families
LLM Fine-Tuning & LLMOps — questions
What does LLM Fine-Tuning & LLMOps include at Kenzed Tech Lab?
We adapt large and small language models to your domain, data, and voice — delivering private models that are faster, cheaper, and more accurate for your use case than general-purpose APIs. 2026 is the year of fine-tuned small models, and we help you own that advantage.
What does Kenzed Tech Lab deliver for LLM Fine-Tuning & LLMOps?
Engagements typically deliver domain adaptation and instruction tuning on your proprietary data; parameter-efficient fine-tuning — LoRA, QLoRA, PEFT — and preference tuning (RLHF/DPO); small Language Models (SLMs) for edge and on-device, low-cost inference; quantization, distillation, and optimization for latency and cost; private, on-premise, and sovereign AI deployments for data residency & compliance; prompt & context engineering, evaluation, and continuous LLMOps monitoring.
Which tools does Kenzed Tech Lab use for LLM Fine-Tuning & LLMOps?
Base models: Llama, Mistral, Qwen, Gemma, Phi and other open-weight families. Tool choice is an architecture decision taken per project rather than a fixed template.
How much does LLM Fine-Tuning & LLMOps cost?
Kenzed Tech Lab does not publish a price list for this work, because scope, integrations, data volume and hosting move the number too far for a printed tier to be honest. Scope is agreed first, then quoted.
How do I start a LLM Fine-Tuning & LLMOps project?
Email [email protected] or call +91 76990 01138. The first step is a scoping conversation.