Dedicated GPUs for AI, rendering & compute
Full single-tenant NVIDIA RTX 4090 and 3090 cards attached to fast Ryzen hosts. The entire GPU and all 24 GB of VRAM are yours — no vGPU slicing, no shared schedulers, no oversold compute.
Built for the workloads that need a GPU
Anything that lives or dies on parallel throughput — model training, inference, rendering, and media — runs faster on a card that's entirely yours.
AI & deep-learning training
Train and fine-tune transformers, diffusion, and vision models on the whole card — no vGPU slicing, no shared VRAM, full tensor-core throughput.
LLM inference & serving
Self-host Llama, Mistral, and Qwen with low, predictable token latency. Blackwell's FP8/FP4 paths and dedicated VRAM keep your context window fast and entirely yours.
3D rendering & VFX
CUDA/OptiX acceleration for Blender, Octane, and Redshift. Render farms scale linearly across dedicated cards with direct routes between locations.
Video transcoding
Hardware NVENC/NVDEC pipelines push real-time 4K transcoding at many multiples of CPU-only speed — ideal for streaming and media platforms.
Scientific & HPC compute
Molecular dynamics, CFD, genomics, and Monte-Carlo simulation run on raw CUDA with full PCIe bandwidth to the host's NVMe and memory.
Cloud gaming & VDI
Stream GPU-accelerated desktops and game sessions from a single-tenant box with consistent frame pacing and full out-of-band IPMI control.
Real workloads, real commands
You get a clean base OS and full root — then build your own stack. Pick your NVIDIA driver, CUDA version, and frameworks: Ollama, PyTorch, Blender, ffmpeg, whatever you run. No proprietary wrappers, no abstraction tax.
- Clean base OS — install any driver, CUDA, and framework versions you need
- Full root, custom kernels, any container runtime
- 100% of the card's VRAM and tensor cores
- Direct routes between locations for multi-GPU render farms
The cards we run
Every card is single-tenant with full root access — from latest-gen Blackwell to proven 24 GB workhorses. Pick raw speed or value, or scale out across several with direct routes between locations.
NVIDIA RTX 4090
- VRAM
- 24 GB GDDR6X
- FP16
- ~330 TFLOPS
- CUDA cores
- 16,384
- Bandwidth
- 1008 GB/s
Our fastest card — best for training, LLM serving, and Cycles/OptiX rendering.
NVIDIA RTX 5080
- VRAM
- 16 GB GDDR7
- FP16
- ~225 TFLOPS
- CUDA cores
- 10,752
- Bandwidth
- 960 GB/s
Latest-gen Blackwell flagship — GDDR7 bandwidth and 5th-gen tensor cores for fast training and inference at 16 GB.
NVIDIA RTX 5070 Ti
- VRAM
- 16 GB GDDR7
- FP16
- ~176 TFLOPS
- CUDA cores
- 8,960
- Bandwidth
- 896 GB/s
The sweet spot — 16 GB and near-5080 throughput for inference, rendering, and mid-size training.
NVIDIA RTX 3090
- VRAM
- 24 GB GDDR6X
- FP16
- ~143 TFLOPS
- CUDA cores
- 10,496
- Bandwidth
- 936 GB/s
Excellent value for inference, transcoding, and rendering at a full 24 GB of VRAM.
NVIDIA RTX 5060
- VRAM
- 8 GB GDDR7
- FP16
- ~77 TFLOPS
- CUDA cores
- 3,840
- Bandwidth
- 448 GB/s
Efficient 8 GB Blackwell for lighter inference, transcoding, dev boxes, and CUDA workloads on a budget.
Compare the lineup
Switch metrics to see how each card trades off raw tensor throughput, VRAM, core count, and clocks. Bigger isn't always better — the right pick depends on your workload.
FP16 tensor throughput (dense) — raw matrix-math speed for training and inference.
FP16 figures are dense tensor throughput derived from CUDA-core counts and boost clocks; real-world results vary by workload and precision — FP8/FP4 run faster on Ada and Blackwell cards. All values are per single dedicated GPU.
Bare metal,
in your hands.
No setup fees. No contracts. Full root and IPMI on every server, and engineers who answer the ticket.