Hardware-Aware Inference Optimization Built for Peak GPU Density, Sub-500ms TTFT, and Extreme Cloud Cost Efficiency—engineered to bridge high-level model graphs with bare-metal silicon acceleration.
We compile PyTorch and ONNX models into bare-metal machine code using TVM, TensorRT, and OpenVINO.
Dead-code elimination, operator fusion, and constant folding targeting specific GPU architectures.
Post-training quantization and QAT shrinking multi-gigabyte models into lightweight mobile weights.
Dynamic workload balancing routing matrix ops to NPUs, GPUs, and TPUs with zero overhead.
Why uncompiled models burn engineering budgets and stall high-concurrency enterprise applications.
Python overhead and separate kernel launches for attention and normalization cause unacceptable delays for real-time applications.
FIX: Operator Fusion & FlashAttentionKV-cache fragmentation exhausts GPU memory, capping concurrent requests and forcing expensive vertical scaling.
FIX: PagedAttention & Chunked PrefillRunning FP16 models across multiple H100 clusters results in hundreds of thousands of dollars in wasted monthly cloud charges.
FIX: AWQ / FP8 Quantization (-60% cost)How WebConvoy lowers high-level model definitions into hand-optimized machine code for silicon accelerators.
PyTorch Dynamo, ONNX or SafeTensors graph extraction.
Graph rewrite into Target-Independent Intermediate Rep (IR).
Dead code removal, constant folding & operator fusion.
Generate custom OpenAI Triton and CUDA assembly.
Execute via TensorRT-LLM and vLLM continuous batching.
Peak FLOPS utilization on NVIDIA, Apple & Edge NPUs.
Deep hardware-aware optimizations designed to extract maximum compute density from your silicon.
Fusing attention blocks, LayerNorm, and activation functions into single CUDA kernel dispatches, eradicating memory roundtrips.
AWQ, GPTQ, and SmoothQuant post-training quantization reducing memory footprints by 50-75% with zero degradation in benchmark accuracy.
Hand-tuned OpenAI Triton kernels written specifically for non-standard attention masks, custom embeddings, and sparse matrix multiplications.
Virtual memory paging for key-value caches that eliminates memory waste and allows 4x larger concurrent batch sizes.
Deploying small draft models to predict draft tokens validated in parallel by the target model, yielding 2.5x token generation speedups.
Target compilation passes for diverse chip architectures: NVIDIA CUDA, AMD ROCm, Apple Metal Performance Shaders, and Qualcomm NPUs.
Run large models on iPhones, Android devices, embedded robotics, and edge servers with zero cloud dependency.
AWQ and GPTQ 4-bit weights run complex models within limited mobile and IoT RAM budgets.
All biometric, image, and text processing executes locally with zero network packets emitted.
Compile once, deploy everywhere. How WebConvoy's compiler layer targets diverse cloud and edge hardware.
Quantization (FP8/INT4) · Operator Fusion · Memory Paging · Kernel Tuning
Standard PyTorch Eager Mode versus WebConvoy Hardware-Compiled Runtime on NVIDIA H100.
Low-level libraries and frameworks leveraged by WebConvoy's inference engineers.
Enterprise teams witness radical turnaround acceleration and operational cost reductions within the first 14 days of production deployment.
Average turnaround cycle time and human hours spent on repetitive operational workflows.
Schedule an inference optimization audit with WebConvoy's systems engineers. We will analyze your model graphs, kernels, and compute footprints.