Train & Fine-Tune Custom AI Models on Your Private Data
Llama 3.3, DeepSeek-R1 & Mistral Customization with FP8/INT4 vLLM Serving (Save 75% on GPU Costs)
Customize open-weights foundation models on your proprietary datasets and compress them via FP8/INT4 quantization for ultra-low latency on private hardware.
The Optimization & Fine-Tuning Pipeline
From curated instruction dataset preparation to post-training quantization, we engineer specialized models that outperform generic commercial APIs on domain-specific benchmarks.
Curated Dataset Preparation
High-quality synthetic generation, deduplication, and instruction tuning data verified by human domain specialists (99.8% precision SLA).
Parameter-Efficient Tuning (LoRA / QLoRA)
Fine-tune low-rank adaptation adapters on open-weights foundation models, preserving base reasoning while embedding domain vocabulary.
Direct Preference Optimization (DPO)
Align model outputs to strict enterprise tone, compliance guardrails, and deterministic formatting without reinforcement learning instability.
FP8 & INT4 Quantization & vLLM Serving
Compress model weights into FP8, INT8, or AWQ INT4 format for deployment on cost-effective NVIDIA L40S, A10G, or H100 clusters.
Why Custom Models Outperform Generic APIs
Gain complete independence from per-token pricing, rate limits, and third-party downtime.
Sub-50ms Streaming Latency
Deploy models with vLLM and TensorRT-LLM kernels for rapid token streaming in real-time conversational and edge applications.
- PagedAttention eliminates memory fragmentation on GPU VRAM
- Continuous batching maximizes concurrent request throughput
- Sub-50ms Time to First Token (TTFT) even at peak traffic
Zero Vendor Lock-In & Total Privacy
Never worry about OpenAI or Anthropic changing pricing, deprecating models, or logging confidential prompts.
- 100% legal ownership of fine-tuned adapter weights and checkpoints
- Deploy on your own AWS/Azure/GCP VPC or private on-premise hardware
- Predictable flat-rate compute costs rather than scaling token fees
Rigorous Benchmark & Evals Suite
Every training checkpoint is benchmarked against domain-specific evaluation sets to mathematically prove superiority over base models.
- Automated perplexity, task accuracy, and latency benchmarks
- A/B testing against commercial APIs on real enterprise prompts
- Zero catastrophic forgetting of foundational reasoning capabilities
Optimization & Deployment Stack
vLLM
Inference EngineHigh-throughput serving with PagedAttention and FP8 support.
TensorRT-LLM
Kernel AccelerationNVIDIA-optimized C++ kernels for ultra-low latency.
Hugging Face / Unsloth
Training FrameworkDistributed multi-GPU LoRA, QLoRA, and full fine-tuning.
AWQ / GPTQ
Quantization4-bit and 8-bit weight compression preserving 99%+ accuracy.
Triton Server
Model ServingEnterprise model management with dynamic scaling.
NVIDIA H100 / L40S
Hardware SupportOptimized deployments across modern GPU architectures.
Technical & Engagement FAQs
Ready to Build Production AI That Actually Delivers ROI?
Schedule a 30-minute architecture session with our senior AI engineers. We'll assess your dataset, evaluate model feasibility, and draft an end-to-end implementation roadmap.