Technologies That Help
The World Grow.
Technology should make running a business easier. We help organizations design, engineer, and deploy full-stack web platforms, mobile applications, custom AI/ML models, and low-level GPU optimizers with equal dedication.
Technology Solutions for Every Stage of Growth
We provide full-lifecycle technical execution across four core disciplines, delivered with equal dedication and technical rigor.
Full-Stack Web Development
Architecting high-performance web applications using React, Next.js (App Router, SSR, Server Components), Node.js, Express, TypeScript, and relational databases.
- Next.js 15 SSR, React 19, and modular frontend architectures
- Node.js, Express, RESTful APIs & GraphQL
- PostgreSQL, Prisma ORM, MongoDB, and Redis caching
Mobile Apps (iOS & Android)
Building fluid, responsive mobile products using React Native alongside native iOS (Swift) and Android (Kotlin) modules. Complete lifecycles from UI design to store releases.
- React Native cross-platform apps with 60fps animations
- Native iOS (Swift) & Android (Kotlin) hardware bridges
- Offline-first SQLite sync and automated store release pipelines
AI & Machine Learning Engineering
Designing, training, and deploying custom AI/ML models — including TTS and STT speech systems, NLP pipelines, predictive models, computer vision, and fine-tuned generative architectures.
- Custom & predictive models for your data and domain
- TTS / STT speech models and NLP pipelines
- LLM fine-tuning, RAG, and computer vision systems
GPU Optimizers & Systems
Low-level performance engineering in C++, CUDA, and SIMD. We resolve compute bottlenecks and offload model parameters to host RAM, proven by our PyPI libraries TrainStream and AdamBridge.
- Zero-dependency C++/CUDA PyTorch runtime extensions
- Host-offloaded states with fused AVX2 SIMD CPU kernels
- Quantization (FP8, NVFP4, NF4, int8) with outlier preservation
Flagship GPU Tooling: TrainStream & AdamBridge
We validate our systems engineering capabilities through open-source software published publicly on PyPI. Both libraries solve critical memory constraints in deep learning training.
TrainStream
Zero-dependency PyTorch training offload engine. Automatically migrates optimizer state, gradients, activations, and model weights to system host RAM, streaming them back over PCIe dynamically when required by the GPU.
torch.optim.AdamW within 10⁻⁶ after 20 steps.# One-line PyTorch integration
import trainstream
model, optimizer = trainstream.wrap(model, optimizer)
# With NVFP4 quantization and bf16 outlier protection
model, optimizer = trainstream.wrap(
model,
optimizer,
quantize="nvfp4",
outliers=0.01
)AdamBridge
AdamW for PyTorch featuring host-offloaded state, multi-threaded fused SIMD CPU kernels, and layer-by-layer asynchronous overlap with the GPU backward pass (OptiCompute-Bridge architecture).
# Asynchronous layer-wise optimizer offload
import adambridge
opt = adambridge.AdamBridge(
model.parameters(),
lr=1e-4,
weight_decay=0.01,
precision="bf16_moments" # 'fp32' | 'bf16' | 'fp8'
)
opt.attach_hooks(model) # overlaps CPU updates with backward pass
for x, y in loader:
loss = loss_fn(model(x), y)
loss.backward() # offloaded & updated per layer
opt.step_sync() # sync boundary for forward passStandardized Training & Optimizer Benchmarks
Data sourced verbatim from reproducible benchmark suites on PyPI packages. Hardware: NVIDIA RTX 5090 Laptop GPU (24 GiB VRAM), Intel Core Ultra 9 290HX Plus, 64 GiB RAM.
| Framework / Configuration | Step Time | Peak VRAM | Relative Delta |
|---|---|---|---|
| TrainStream (NVFP4 base, FP4 tensor cores) | 0.56 s | 19.1 GB | Reference (Fastest) |
| bitsandbytes QLoRA (NF4) + PEFT | 0.63 s | 17.4 GB | +12.5% time |
| TrainStream (NF4 base) | 0.67 s | 19.1 GB | +19.6% time |
| Unsloth (4-bit) | 0.75 s | 11.5 GB | +33.9% time |
| TrainStream (auto mode) | 0.84 s | 20.6 GB | +50.0% time |
| DeepSpeed ZeRO-2 (Optimizer Offload) | 42.38 s | 35.9 GB | Spills past physical VRAM |
| Stock PyTorch + PEFT | 50.63 s | 37.6 GB | Spills past physical VRAM |
| Optimizer Configuration | Final Val Loss | Step Latency | Peak GPU VRAM | VRAM Saving |
|---|---|---|---|---|
| PyTorch AdamW (FP32 master on GPU) | 2.6402 | 292 ms | 19.6 GiB | Reference |
| AdamBridge (FP8 moments, host offload) | 2.6409 (delta < 0.001) | 305 ms | 13.2 GiB | -32.7% VRAM |
| AdamBridge (BF16 moments, host offload) | 2.6405 (delta < 0.001) | 317 ms | 13.2 GiB | -32.7% VRAM |
| torchao CPUOffloadOptimizer (FP32 weights) | 2.6401 | 525 ms | 15.9 GiB | -18.8% VRAM |
| bitsandbytes AdamW8bit (FP32 weights) | 2.6433 | 312 ms | 16.8 GiB | -14.2% VRAM |
About Working With InnvoSols
What does InnvoSols do?
InnvoSols is an independent IT services, software consulting and technology engineering company. It builds full-stack web applications (React, Next.js, Node.js, TypeScript), iOS and Android mobile apps, custom AI/ML models, and low-level GPU optimizers.
Does InnvoSols work with clients outside its home country?
Yes. InnvoSols is a remote-first company that serves clients globally. Engagements run as agile digital sprints across client time zones, with code delivered through Git.
What are TrainStream and AdamBridge?
They are open-source PyTorch libraries published on PyPI by InnvoSols. TrainStream trains and fine-tunes models larger than GPU VRAM by streaming state from host RAM; AdamBridge keeps FP32 master weights and Adam moments in host RAM and updates them with fused SIMD CPU kernels, cutting peak VRAM by about a third.
Who owns the code InnvoSols delivers?
The client does. Every engagement ends with full intellectual property transfer, documented standard code repositories and automated test suites, with no vendor lock-in.
How do I start a project with InnvoSols?
Email contact@innvosols.com, call +91 6363717949, or send the enquiry form on the contact page. Engineering leadership reviews every enquiry and responds within 24 business hours.
Discuss Your Technical Architecture
Whether you need full-stack web applications, mobile apps (iOS & Android), custom AI/ML model engineering, or low-level GPU acceleration, we are available for technical consultations.
InnvoSols operates under an agile, remote-first model serving clients globally. We maintain no physical commercial storefronts.
www.innvosols.com
Initiate Engineering Consultation
All inquiries are reviewed directly by technical leadership. For expedited communications, reach us at contact@innvosols.com or +91 6363717949.