42%
Throughput Improvement
37%
Power Reduction
3×
Lower Operating Cost
12
Distributed Nodes Deployed
The Challenge
The client was operating a 13B parameter language model as the backbone of a customer-facing conversational AI product. The infrastructure was mid-range GPU hardware - capable in theory, but heavily under-utilized due to inefficiencies in the inference stack. Two constraints made the project technically demanding:
- Power budget: the deployment environment had strict per-rack power limits that the existing inference stack regularly exceeded under load
- Cost pressure: the client needed to scale from prototype to multi-node production at a cost that the existing per-query GPU spend made impossible
Standard optimization approaches - reducing batch size, switching frameworks - had already been attempted. The client needed kernel-level engineering to go further.
Technical Approach
Phase 1: Profiling and Bottleneck Identification
Jashom conducted a full profiling pass using NVIDIA Nsight to map the inference execution graph. Key findings included excessive memory bandwidth consumption from unoptimized attention operations, high kernel launch overhead from non-fused operators, and underutilized tensor cores due to misaligned precision modes.
Phase 2: Custom CUDA Kernel Development
We implemented custom CUDA kernels targeting the identified bottlenecks:
- Fused multi-head attention kernels reducing memory round-trips in the attention computation
- Operator fusion eliminating redundant kernel launches across transformer layers
- Optimized memory access patterns aligned to L2 cache boundaries for the client's specific GPU architecture
Phase 3: Dynamic Quantization - INT8 / FP16
We implemented dynamic quantization across the model's linear layers using INT8 precision for weight storage with FP16 activations. This reduced the effective VRAM footprint of the model by approximately 40% while preserving the numerical range needed for accurate token prediction. Calibration was performed against a representative sample of the client's actual query distribution, not a generic benchmark.
Phase 4: TensorRT Inference Re-Engineering
The inference path was re-implemented using TensorRT with layer fusion enabled across the full transformer stack. TensorRT's profiling-guided optimization selected the most efficient kernel implementations for each layer given the client's hardware and precision requirements.
Phase 5: Adaptive Batching Scheduler
We designed an adaptive batching scheduler that dynamically adjusts batch size based on current GPU utilization and queue depth. Under light load, the scheduler runs smaller batches for lower latency. Under heavy load, it consolidates requests into larger batches to maximize throughput. This produced measurably higher GPU utilization across the variable load patterns of a production service.
Phase 6: Distributed Deployment
The optimized inference stack was containerized and deployed across 12 distributed nodes with load balancing. Each node runs an independent inference replica behind a shared request router. The RAG (Retrieval-Augmented Generation) pipeline was integrated at the routing layer, allowing context retrieval to happen in parallel with inference scheduling.
Results
| Metric | Before Optimization | After Jashom |
|---|---|---|
| Inference Throughput | Baseline (100%) | +42% (142%) |
| GPU Power Consumption | Baseline (100%) | −37% (63%) |
| VRAM Utilization per Model | ~22GB (full FP16) | ~13GB (INT8/FP16 mixed) |
| Cost per 1M Tokens | Reference | ~3× reduction |
| Model Accuracy (BLEU vs. reference) | Reference | No measurable degradation |
| Deployment Nodes | Prototype: 1 node | Production: 12 nodes |
| RAG Query Latency (p50) | Baseline | Within latency SLA maintained |
Technologies Used
Client Outcome – Business Impact
Optimized model performance allowed the client to scale LLM inference at one-third of the original cost, enabling deployment of a full production conversational AI system on mid-range GPU clusters. The power efficiency gains resolved the per-rack budget constraint entirely, clearing the path for further capacity expansion.