GPU Hosting for Workloads
That Never Stop
USA-based GPU dedicated servers and GPU VPS built for AI inference, LLM hosting, image generation, and 3D rendering — with guaranteed resources, no shared hardware, and transparent flat-rate pricing.
GPU Hosting Plans — Up to 80% Lower Cost
No shared resources, no hidden fees, no bandwidth limits — single-card and multi-GPU server options available.
Save 2–5× vs. Other GPU Cloud Providers
Same dedicated GPU hardware. Same performance. A fraction of the cost — no cloud markup, because we own the servers.
All GPU Mart plans include dedicated GPU, CPU, RAM, NVMe storage & unmetered bandwidth. No setup fees. No egress costs. No hidden charges.
Lower Cost. Proven Stability. Real Support.
We own the hardware, operate the data centers, and answer the tickets — no cloud middleman.
Up to 80% Lower Cost — No Hidden Markup
We own our hardware and skip the cloud middleman entirely — so you pay for raw GPU compute, not a platform premium.
Built for Long-Running Workloads That Never Stop
Every plan, including GPU VPS, is a dedicated physical GPU — no virtualization. Performance is exactly what the spec sheet says, every hour.
Real Engineers — Responding in Minutes
Our GPU infrastructure team is online 24/7. From provisioning to CUDA configuration, help arrives fast — every time.
The Right GPU for Every AI & Creative Workload
The same dedicated GPU server, configured for your workload — at a fraction of what public cloud charges.
The most cost-efficient GPU for AI inference — deploy LLaMA, DeepSeek, Gemma and other open-source LLMs with predictable throughput.
Run SDXL, Flux, ComfyUI, and video models with full VRAM access and flat monthly pricing for cost-efficient large-scale generation.
Render with Blender, Redshift, or V-Ray on dedicated GPUs — without render farm pricing or shared queues. Simple hourly or monthly pricing, no per-job markup.
Full Windows GPU environments with RDP access — rare among providers. Ideal for interactive workloads. Linux also supported.
Not Sure Where to Start?
Start with a proven setup for your workload — backed by real benchmarks and deployment guides.
Enterprise Hardware. Zero Compromises.
Latest NVIDIA GPUs with ECC, NVMe, and enterprise networking — fully owned and operated by us.
Trusted by AI Engineers, Studios & Researchers
What teams running production workloads say after switching from public cloud GPU services.
We moved our LLM hosting from a major cloud provider to GPU Mart six months ago. The dedicated AI GPU server gives us consistent throughput for our inference API — no throttling, no surprise bills. The VRAM headroom on the A100 lets us serve a 70B model comfortably in production.
Our studio runs Blender Cycles and Redshift renders continuously. These dedicated GPU servers handle multi-day rendering jobs without a single dropout. The fixed monthly price beats any render farm service we've tried. It genuinely feels like owning the hardware.
We run Stable Diffusion SDXL and custom LoRA pipelines 24/7 for a client content platform. Having a dedicated server with that much VRAM means we can keep multiple checkpoint variants loaded at once. Root access lets us control the full environment. Support responded to a driver question in under 20 minutes.
FAQ — Everything You Need to Decide
The questions we hear most before a purchase decision — answered directly.
Why is GPU Mart cheaper than major cloud providers?
Will performance be consistent during long-running workloads?
Is GPU Mart suitable for production or only testing?
Can I try a GPU server before committing?
Which GPU should I choose for my workload?
- 16–24GB VRAM (RTX A4000, Pro 2000, Pro 4000, 4090, A5000) — small to mid LLMs, basic AI workloads
- 40–48GB+ VRAM (A6000, A100, Pro 5000, Pro 6000) — larger models, higher throughput
- Multi-GPU setups — large-scale training or high-concurrency inference
Are there any hidden fees or setup charges?
Do you offer hourly billing or long-term discounts?
Can I run open-source LLMs like Llama or DeepSeek?
Is this suitable for Stable Diffusion or SDXL pipelines?
Do I need multiple GPUs for rendering or AI workloads?
Are AI frameworks like PyTorch or CUDA pre-installed?
How do I access the GPU server?
Which operating systems are available?
- Ubuntu (18/20/22/24 LTS), CentOS 7.x/8.x, Debian 10–12, AlmaLinux, Fedora
- Windows Server OS with full administrator access
Do you support Docker and custom container images?
What kind of support do you provide?
Get Started with GPU Hosting
Stop fighting shared cloud GPU queues. Rent a GPU dedicated server or GPU VPS with full VRAM, root access, unmetered bandwidth, and 24/7 expert support included.