Model Optimization for Consumer GPU Inference

Running state-of-the-art models on consumer-grade GPU hardware requires aggressive optimization without sacrificing output quality. This article presents three complementary techniques evaluated on Homegrown Intelligence workflows: quantization, structured pruning, and knowledge distillation. Each technique is characterized by its compression ratio, throughput impact, and accuracy preservation on standard evaluation benchmarks.

Post-Training Quantization

Weight quantization reduces model memory footprint by representing parameters with fewer bits. Our evaluation focused on four-bit and eight-bit quantization applied to diffusion transformer and large language model architectures. The Q4_K_M quantization scheme from the llama.cpp project achieved a 3.8× reduction in model size for Llama 3.1 8B with a perplexity increase of only 0.34 points on the WikiText-2 validation set. For diffusion models, FP8 quantization via the TensorRT model optimizer preserved structural similarity index above 0.98 compared to FP16 baselines while reducing VRAM consumption by 42 percent. Activation quantization proved more sensitive than weight quantization; models quantized to INT8 activations exhibited outlier channels that degraded output quality unless per-channel scaling factors were calibrated on a representative prompt set of at least 256 samples.

Structured Pruning

Removing redundant parameters through structured pruning targets attention heads and feed-forward network dimensions that contribute minimally to output quality. We applied magnitude-based pruning to the Flux2 UNet, removing 25 percent of channels in the middle transformer blocks while fine-tuning for 500 steps on a distilled subset of the LAION aesthetic dataset. The pruned model achieved a 1.3× throughput improvement at 1024×1024 resolution with a Frechet Inception Distance increase of 0.7 points relative to the unpruned model. Layer-wise pruning ratios were determined by measuring the average activation norm across 100 calibration prompts; layers with norms below the 15th percentile received a 40 percent pruning ratio while high-norm layers remained untouched.

Knowledge Distillation

Knowledge distillation trains a smaller student model to replicate the behavior of a larger teacher model. We trained a distilled variant of the WhisperX encoder using a 4-layer transformer student with hidden dimension 384, reducing encoder parameters by 72 percent. The student was trained on 2000 hours of pseudo-labeled audio where the teacher generated frame-level soft targets. Word error rate on LibriSpeech clean increased from 3.1 percent to 4.8 percent, while inference throughput improved by 3.4× on an RTX 4090. The distilled encoder is packaged as a drop-in replacement within the Homegrown Intelligence WhisperX workflow and selected automatically when the installation script detects less than 8 GB of available VRAM.

Practical Optimization Recipes

For users seeking immediate throughput gains without retraining, the recommended first step is enabling FP16 inference through the workflow configuration file. This single change typically doubles throughput with no measurable quality loss. The second step applies four-bit quantization to the largest weight tensors while keeping critical layers such as embedding tables and output projections in FP16. The third step enables Flash Attention v2 if the GPU supports it, which reduces attention computation memory from quadratic to linear in sequence length. These three steps together yield a 4.5× to 6× throughput improvement over naive FP32 execution on RTX 40-series hardware.