01 / Motivation & Objectives
Research Motivation & Problem Statement
While commercial model APIs provide strong general capabilities, enterprise workloads face severe constraints around data privacy, latency guarantees, deterministic evaluation, and unit cost. Running proprietary open-weights models (Llama, Mistral, Qwen) is critical, but naive deployments are prohibitively expensive and slow.
The Core Question
Can specialized 7B–14B models fine-tuned with LoRA match or exceed frontier 70B+ models on domain-scoped enterprise tasks while slashing serving costs by 80%?
Empirical Methodology
Rigorous benchmarking across reproducible datasets, deterministic test suites, and hardware-accelerated inference runtimes (vLLM, TensorRT-LLM).
02 / Evaluation Infrastructure
Experimental Setup & Evaluation Harness
To eliminate benchmark noise, I architected a modular evaluation harness capable of testing models across reasoning accuracy, instruction following, and latency under concurrent batch loads.
Fine-Tuning Methodology
Comparing full parameter tuning vs. LoRA (r=16, r=64) and 4-bit QLoRA to evaluate catastrophic forgetting and domain adaptation efficiency.
Inference Profiling
Benchmarking continuous batching and PagedAttention in vLLM against baseline Hugging Face TGI runtimes across A100 and H100 GPU instances.
Quantization Degradation
Measuring accuracy drop-offs between FP16, AWQ 4-bit, and GPTQ formats across complex mathematical and multi-hop extraction tasks.
03 / Empirical Discoveries
Key Findings & Architecture Insights
Our research produced actionable insights that directly influence how enterprise engineering teams structure their production AI infrastructure.
LoRA Rank Saturation
For domain-specific schema generation and classification, increasing rank beyond r=32 yields negligible accuracy gains while unnecessarily ballooning adapter storage and swap latency.
PagedAttention Concurrency
Under heavy multi-tenant concurrency (32+ parallel requests), vLLM's memory management achieved 3.8x higher throughput compared to standard attention implementations.
04 / Production Translation
Translation to Production AI Leadership
As an Engineering Manager, empirical research isn't an academic exercise—it is the foundational knowledge required to make pragmatic architectural trade-offs under real budget and latency constraints.
Pragmatic Model Selection
Knowing when a fine-tuned 8B model will outperform a multi-billion parameter proprietary model saves thousands of dollars in recurring token costs.
Zero-Downtime Adapter Swapping
Architecting multi-LoRA serving where a single base model dynamically serves diverse task adapters on the fly without reloading weights.
Deterministic Guardrails
Implementing strict grammar sampling and logit bias filters to guarantee 100% schema compliance in mission-critical backend microservices.
Open Research Lab
Explore the AI Research Laboratory
View repository implementations, benchmarks, observations, and experimental notebooks.