Developer & ML Workstations That Keep Up
Developers lose hours to two things: compiles that crawl and training runs that wait on a cloud queue. A developer workstation kills both. We build for the way you actually work — many cores for parallel builds, a GPU sized for deep learning, and enough RAM to hold the dataset instead of paging it. It's the best PC for your machine-learning work because it's built around your machine-learning work.
An office PC stalls on real work
A standard office PC stalls on big builds, can't hold a real dataset in memory, and has no GPU for training — so you rent one, wait in line, and pay the meter.
Building a deep-learning PC yourself means sourcing parts, fighting driver stacks, and no one to call when it won't post. We take both problems off your desk.
Cores for parallel builds
High-core CPU so compiles, tests, and containers run in parallel without the wait.
GPU sized for deep learning
NVIDIA card matched to your model sizes for local training instead of cloud queues.
RAM to hold the dataset
Enough memory to keep data resident, so I/O stops being the bottleneck.
A dev environment that boots ready
Drivers, CUDA, and your toolchain set up; no weekend lost to a driver stack.
Build it yourself vs. a TIS dev rig
| TIS Developer Workstation | DIY deep-learning PC | |
|---|---|---|
| Part selection | Specced to your stack | Hours of research |
| Driver / CUDA setup | Done, boots ready | Your problem |
| Bench-tested under load | Yes | Hope it posts |
| Support when it breaks | Call a Texas builder | Forum threads |
| Time to first training run | Day one | A weekend, maybe |
Built for the desk you actually use
From Missouri City to Rosenberg, we build dev and ML rigs for engineers who'd rather own their compute than wait on a cloud GPU. Bench-tested before it ships, set up at the desk where it'll run. See our Texas service areas.
Developer workstation questions
What's the best PC for machine learning work?+
The one specced to your models: a GPU with enough VRAM, a CPU with enough cores to feed it, and RAM to hold your dataset. There's no single "best" — there's the best for your workload, which is what we build.
Should I build a deep-learning PC myself or have it built?+
DIY saves a little money and costs a lot of time — part research, driver stacks, and no one to call when it won't post. We hand you a bench-tested machine that boots ready and comes with a phone number.
Will it handle both software development and ML training?+
Yes. We balance cores for fast compiles with a GPU for training so the same rig handles your day job and your model runs.
Can I bring my own GPU or parts?+
Often yes. If you've already got a card worth keeping, we'll build around it and tell you straight if it's worth reusing.
Do you preinstall my dev tools?+
We set up drivers, CUDA, and your core toolchain so the first thing you do is work, not configure.
Can I fine-tune a 70B model on a desktop?+
With QLoRA, yes — quantized QLoRA fine-tuning of a 70B model is feasible on a single high-VRAM card such as the 96GB RTX PRO 6000, though it's at the upper edge of what a desk machine handles. A full-precision fine-tune of 70B needs roughly 140GB-plus of VRAM and is a multi-GPU server conversation, not a workstation. We'll tell you honestly which side of that line your job falls on, and route you to a local LLM server build when it crosses over.
Up to AI workstations overview · size the GPU on an NVIDIA AI workstation or have us build any custom PC · a full 70B fine-tune? That's a local LLM server · trading desk? See trading AI systems.
Fine-tune VRAM: LoRA vs. QLoRA vs. full
Fine-tuning needs far more VRAM than inference, and the method you choose changes that by a lot. QLoRA combines LoRA with quantization to roughly halve the memory, which is what makes fine-tuning possible on a single desk-side card. Approximate figures below are from public 2025–2026 fine-tuning guides — re-verify against your framework and sequence length.
| Model | QLoRA ~VRAM | LoRA ~VRAM | Full fine-tune ~VRAM | Fits a desk? |
|---|---|---|---|---|
| 7B | ~12GB | ~28GB | ~100–120GB | QLoRA on 24GB; full is a server |
| 13B | ~16GB | ~40GB+ | ~200GB+ | QLoRA on 24–32GB; full is a server |
Approximate figures from public fine-tuning guides; a 24GB card handles QLoRA on 7B and 13B, while full fine-tunes need several times the inference memory and quickly become multi-GPU server territory. To size the card itself from these numbers, see the GPU & VRAM guide.
Compiles and containers: don't starve the CPU
The GPU does the training, but the CPU runs everything around it — and on a dev rig that's a real workload. Parallel compiles, a stack of containers, test suites, and data loading all scale with core count. A GPU that's waiting on a single-threaded data pipeline sits idle, so the CPU isn't an afterthought; it's what keeps the expensive card fed.
For one GPU and typical dev work, a high-core mainstream chip (Core i9 or Ryzen 9) handles parallel builds and containers comfortably. When you move to multiple GPUs, you want a HEDT platform (Threadripper or Xeon W) for the extra PCIe lanes and ECC — not for raw compile speed, but to feed two cards at full bandwidth. Pair the cores with enough RAM to hold your dataset and your container set resident; 64GB is a sensible floor for dev work and 128GB is comfortable when builds and training share the machine.
A dev rig that keeps up with you
Tell us your stack and your model sizes and we'll build a developer workstation that compiles fast and trains local — owned outright, no cloud queue.