Dedicated NVIDIA GPU servers with L40 capacity
Dedicated NVIDIA GPU servers for AI inference, image generation, and persistent ComfyUI workloads—with single- and multi-GPU configurations available. Private, single-tenant capacity matched to your workload during a short intake, not sold from a public marketplace.
What the NVIDIA L40 is good for
The NVIDIA L40 is a data-center GPU built on the Ada Lovelace architecture with 48 GB of GDDR6 memory. That combination — modern Ada cores plus generous VRAM on a single card — makes it a strong fit for practical AI workloads that stall on smaller consumer GPUs:
- High-resolution and batched image generation (Stable Diffusion, SDXL, Flux, ComfyUI pipelines).
- Private LLM inference for open-weight models that fit within a single 48 GB device or run quantized.
- Fine-tuning and LoRA workflows where iteration speed and VRAM headroom matter more than peak FLOPs.
- Long-running internal tools and agents that need a stable, dedicated environment.
Why rent dedicated NVIDIA L40 capacity instead of a public marketplace slot
- Single tenant. The node is yours for the term — no shared pool, no preemption, no noisy neighbors.
- Direct onboarding. A real operator helps get drivers, CUDA, and your stack running instead of a ticket queue.
- Predictable pricing. Fixed weekly/monthly rate for the term rather than usage-based spot pricing.
- Persistent environment. Models, checkpoints, and workflows stay put between sessions.
Single- or multi-GPU configurations
Single- or multi-GPU configurations are available subject to availability. One L40 with 48 GB of VRAM is enough for a large share of production image-gen and inference work. When a workload actually benefits from more than one GPU — independent parallel jobs, replicated inference workers, or frameworks with explicit tensor/pipeline parallelism — we can arrange up to 2× NVIDIA L40 GPUs in one dedicated node, subject to availability. Two 48 GB devices are not automatically one 96 GB pool; software has to be written for multi-GPU use. If you're unsure which side of that line your workload falls on, that's exactly what we scope during qualification.
How allocation works
Instead of publishing internal capacity, we confirm the exact configuration privately after a short intake. You tell us what you're running, what frameworks and model sizes are involved, and when you need to start; we come back with a recommended setup, term, and price — or say honestly if we're not the right fit.
Prefer to read more first? See Dedicated ComfyUI GPU server for image-gen specifics, or the guide on choosing dedicated L40 capacity.
Ready to talk about your workload?
Tell us what you're running and when you need it. We confirm the exact configuration during a short intake conversation.
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