GPU Optimizers & Systems Architecture
Drawing directly from our published open-source runtime tooling (TrainStream and AdamBridge), we design low-level compute extensions, host-to-device memory pipelines, and fused mathematical kernels that maximize hardware saturation.
Core Technical Capabilities
CUDA & C++ Runtimes
Custom PyTorch C++ extensions and asynchronous CUDA streams.
SIMD AVX2 Kernels
Vectorized CPU host optimizers for zero-GPU-overhead updates.
Memory Hierarchy
Pinned host arena allocation, NVLink mapping, and PCIe tuning.
Inference Quantization
FP8, INT8, and NVFP4 containerized TensorRT and ONNX pipelines.
Representative Deliverables
- Compiled PyTorch CUDA/C++ Wheel Binaries
- Empirical VRAM & Throughput Benchmark Suite
- Quantized TensorRT / Triton Inference Servers
- Compute Profiling & Bottleneck Root-Cause Audits
Our Published Tooling
Why Businesses Engage InnvoSols
Zero commercial real-estate overhead. Engagements are executed entirely through agile digital sprints across all client time zones.
Clients speak directly with technical engineers. No non-technical layers or distorted communication channels.
Full intellectual property transfer upon delivery. Clean, documented, standard code repositories and automated test suites.
Every optimization, throughput benchmark, and latency metric is documented and independently reproducible.
GPU Optimizers & Systems: FAQ
What does GPU optimization consulting involve?
InnvoSols profiles your training or inference workload to find compute, memory and PCIe bottlenecks, then fixes them with custom PyTorch C++/CUDA extensions, asynchronous CUDA streams, SIMD AVX2 host kernels, memory-offload pipelines and quantization, verified with reproducible benchmarks.
Can InnvoSols reduce how much GPU memory our models need?
Yes. Offloading optimizer state, gradients, activations or weights to host RAM lets models train on GPUs with less VRAM. InnvoSols' AdamBridge library, for example, cut peak VRAM by 32.7% on Qwen2.5-0.5B fine-tuning at matching validation loss.
Which quantization formats do you work with?
FP8, INT8 and NVFP4 for inference, delivered as containerized TensorRT, Triton or ONNX pipelines, and int8, NF4, FP8 and NVFP4 frozen weights for training through TrainStream.
What do I receive from a GPU optimization engagement?
Compiled PyTorch CUDA/C++ wheel binaries, an empirical VRAM and throughput benchmark suite, quantized TensorRT or Triton inference servers, and a compute profiling and bottleneck root-cause audit.
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Start a GPU Optimizers & Systems Engagement
All inquiries are reviewed directly by technical leadership. Tell us about your workload, stack and goals.