LLM Training & Deployment

Why Fine-Tune?

Foundation models are general-purpose. Your business isn't. Fine-tuning bridges that gap — taking a pre-trained LLM and specializing it on your domain data, your terminology, your edge cases.

The result is a model that speaks your language: higher accuracy on domain tasks, lower latency from smaller specialized models, reduced per-token costs, and behavior that's consistent and controllable.

Our Training Pipeline

Data Engineering

Every fine-tune is only as good as its training data. We start with a rigorous data pipeline:

• Audit — assess your existing data assets for coverage, quality, and bias
• Curation — extract, clean, and structure training examples from your documents, logs, and domain artifacts
• Augmentation — synthetically expand your dataset where gaps exist using carefully designed generation pipelines
• Validation — holdout sets, cross-validation splits, and contamination checks to ensure training integrity

Training Infrastructure

We configure and manage the full training stack:

• Distributed training across multi-GPU and multi-node clusters using DeepSpeed ZeRO or PyTorch FSDP
• Parameter-efficient methods (LoRA, QLoRA) when full fine-tuning isn't necessary — same accuracy lift at a fraction of the compute cost
• RLHF / DPO alignment when your model needs to follow specific behavioral constraints or preference patterns
• Hyperparameter optimization with automated sweeps tracked in Weights & Biases

Evaluation & Benchmarking

We don't ship a model without proving it works:

• Task-specific benchmarks against your baseline (prompting, RAG, or existing model)
• Human evaluation protocols for subjective quality metrics
• Regression testing across edge cases and failure modes
• Latency, throughput, and cost-per-request profiling

Deployment Architecture

Training a model is half the problem. Deploying it reliably at scale is the other half.

Inference Optimization

Before deployment, we optimize your model for production:

• Quantization (GPTQ, AWQ, GGUF) to reduce memory footprint and increase throughput without meaningful accuracy loss
• Speculative decoding and continuous batching for maximum tokens-per-second
• KV cache optimization to handle long-context workloads efficiently

Production Infrastructure

We deploy behind battle-tested inference servers:

• vLLM for high-throughput serving with PagedAttention
• NVIDIA Triton for multi-model serving and ensemble pipelines
• Text Generation Inference (TGI) for Hugging Face model compatibility

Every deployment includes:

• Autoscaling based on request queue depth and GPU utilization
• Health checks with automatic restart and failover
• Model versioning with A/B testing and instant rollback
• Request logging and token-level observability

Monitoring & Continuous Improvement

Models degrade over time as data distributions shift. We build monitoring that catches drift before it impacts your users:

• Output quality monitoring — automated evaluation on a rotating sample of production requests
• Data drift detection — statistical tests on input distributions compared to training data
• Performance dashboards — latency percentiles, throughput, error rates, and cost tracking
• Retraining triggers — automated alerts when model performance drops below threshold, with documented retraining procedures