AI & Machine Learning Infrastructure

Purpose-Built Hardware for Model Training at Scale

AI & Machine Learning Infrastructure

At Bitworks, we don’t just understand GPUs—we understand your workload. From LLM training to real-time inference, our AI infrastructure solutions are engineered to deliver maximum performance, minimal latency, and future-proof scalability.

Since 2015, we’ve helped researchers, engineers, and enterprise teams build the backbone of their machine learning workflows—one rack at a time.

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Quality First

What’s Included in Our AI Infrastructure Solutions?

Purpose-Built AI Architectures

We don’t sell generic servers — we build intelligent infrastructure that mirrors the complexity and scale of your AI workloads.

Whether you're fine-tuning transformer models, running diffusion-based generative pipelines, or deploying large-scale vision systems, our architecture planning considers every bottleneck: compute density, memory access, bandwidth, and thermal load. We tailor GPU selections, memory configs, and internal I/O layout based on your frameworks and expected model sizes.

We also account for workload concurrency, staging datasets, and acceleration needs — giving you a system that's not only powerful but sustainable under real-world strain.

Scalable Compute Ecosystems

Modern AI demands elastic, modular infrastructure that adapts as your needs grow.

We design ecosystems — not just boxes — using interconnected GPU nodes with optimized latency, shared storage, and optional hybrid cloud integration. From small labs to AI-first enterprises, we enable setups that scale horizontally and vertically: whether that’s expanding your local rack, adding inference nodes, or offloading training into burstable compute zones.

Every system is orchestrated with modern training tools like DeepSpeed, Ray, and JAX, making your compute setup as dynamic as your models.

Fast-Track Build & Launch

You shouldn’t waste weeks configuring drivers or setting up environments — we handle it all before your first epoch.

Your system arrives pre-loaded with performance-tuned frameworks, libraries, and environment containers. We optimize BIOS settings, handle GPU firmware versions, and validate training performance against expected benchmarks. Whether you're doing LLM training, hyperparameter sweeps, or edge deployment for real-time inference, your system is ready on day one.

Our documentation and onboarding support ensure your engineering team hits the ground running — no wasted cycles.

Long-Term Support

AI infrastructure is never “set it and forget it.” It evolves with your models — and so do we.

We provide lifecycle services that keep your systems optimized as workloads shift from training to inference, from research to production. That includes thermal performance monitoring, model runtime diagnostics, GPU health checks, and guidance on optimizing for throughput, cost, or energy draw.

As your models grow in size and sophistication, we help you plan your next step — whether it’s adding memory, upgrading interconnects, or expanding your cluster intelligently.

Quality First

Why Choose Bitworks?

01Proven Expertise

Since 2015, Bitworks has been engineering GPU-powered infrastructure—from boutique ML clusters to full-scale data centers. We were there when CUDA was just getting started, and we’ve been ahead of the curve ever since.

02Enterprise-Grade Reliability

Trusted by research institutions, AI startups, VFX studios, and national labs, Bitworks delivers systems that perform under pressure—so you don’t have to worry about downtime or underperformance.

03Custom-Tailored Solutions

No one-size-fits-all. Whether you're training LLMs or rendering high-res simulations, we build to fit your workload—not the other way around.

05Fast & Flexible Deployment

From consultation to production in days, not months. We move at the speed of innovation, helping you scale when you need it most.