Best Bare Metal GPU Hosting for Developers

Key takeaways

• Bare metal GPU hosting gives developers direct GPU access with no hypervisor, and the right provider depends on interconnect, billing, and workload.

• Choose InfiniBand-class or RoCE networking for multi-node training. Single-node fine-tuning and inference run well on NVLink or PCIe.

• Hourly billing suits prototyping, while monthly or contract pricing suits 24/7 inference. A few bare metal providers now publish hourly rates.

• Servers.com AI Compute is the top pick for steady production workloads. The other six providers here fit prototyping, multi-node training, entry budgets, or regulated workloads better.

Bare metal GPU hosting gives you a single-tenant physical server with direct access to its GPUs and no hypervisor between your code and the hardware. You get the full VRAM, raw PCIe access, and no neighbor competing for the card. In exchange, you give up some of the instant elasticity that GPU clouds offer.

Best bare metal GPU providers at a glance

Provider	Best for	GPUs	Billing	Published price (as of writing)
Servers.com AI Compute	Steady 24/7 inference and training	Custom (NVIDIA, AMD, Intel)	Long-term agreement	Quote
DigitalOcean	Multi-node training	H100, H200, MI300X (8-GPU nodes)	Sales contract	Not published
Latitude.sh	Hourly bare metal access	H100, RTX PRO 6000, B300	Hourly or monthly	From \$1.68/hr (1x H100)
NovoServe	Flat-rate servers with 1 to 8 GPUs	RTX PRO 6000, H100, A-series, AMD Instinct	Monthly, annual, multi-year	From €306/mo (1x A4000)
OpenMetal	Fixed monthly pricing with engineer help	RTX PRO 6000, H200 NVL	Monthly	Quote
OVHcloud	Entry-priced inference	L4, L40S	Monthly	From \$1,145/mo (L4)
Atlantic.Net	Regulated workloads	H100 NVL, L40S, A2	12-month term	\$4,729.44/mo (H100 NVL package)

These prices cover different GPUs, so they don’t compare like for like. Treat the table as a map to the reviews below.

Pick by profile:

• Single-node experiments: Latitude.sh (hourly) or NovoServe (low monthly entry).
• Steady production inference: Servers.com, OpenMetal, or NovoServe.
• Multi-node training: DigitalOcean or Latitude.sh B300 nodes, with the fabric confirmed for Servers.com.
• Regulated workloads: Atlantic.Net or Servers.com, followed by your own compliance review.
• Entry budget for inference: OVHcloud L4 or NovoServe A4000 configs.

Top providers, reviewed

Each review covers who the provider fits best, what you get, how billing works, and who should skip it. Start with the ones that match your workload, then use the selection guide further down to narrow the list.

1. Servers.com by Nexcess: best for steady production inference and training

Servers.com by Nexcess builds bespoke bare metal GPU servers on Enterprise Bare Metal (EBM) hardware with sole tenancy. The product page lists NVIDIA, AMD, Intel, and other manufacturers’ GPUs.

Servers.com configures hardware, storage, network topology, and isolation to how the workload performs, and reconfigures them as it changes. You get iDRAC access, RAID configuration, custom OS installs, and an API, CLI, and Terraform for CI/CD. PyTorch, TensorFlow, JAX, ONNX, and vector databases such as Weaviate and Qdrant all run on it.

AI Compute (AIC), EBM, and Scalable Bare Metal (SBM) share one private network, so you can keep the steady GPU load on AIC, steady CPU services on EBM, and bursts on SBM.

Billing: Long-term agreements by quote, with no on-demand premium

Support: Customers reach Servers.com solution architects and engineers 24/7/365 to answer their requests.

Skip it if: You want to spin up a GPU for a few hours of experimentation. Prototype on an hourly provider, then move over once the workload stabilizes. Customer stories for Voiso, GoNET, and VoiceInteraction appear on the AIC page.

2. DigitalOcean Bare Metal GPUs: best for multi-node training

DigitalOcean sells 8-GPU nodes: HGX H100 (640 GB), HGX H200 (1,128 GB), and AMD MI300X (1,536 GB). Each node carries dual Intel Xeon Platinum CPUs, 2,048 GiB of RAM, and 61.44 TiB of NVMe. The network offers up to 40 Gbps public, 400 Gbps private, and GPU RDMA up to 3.2 Tbps. You get root access and custom OS and driver control, but no IPMI.

Billing: Sales contract, invoiced through a Paperspace account, with no published rates.

Skip it if: You need a self-serve checkout or a region beyond NYC1 and AMS3, the only two available at the time of writing.

3. Latitude.sh Metal GPU: best for hourly bare metal access

Latitude.sh publishes both hourly and monthly rates for its Metal GPU line:

• g3.h100.small: 1x H100 80 GB at \$1.68 per hour, or \$1,230 per month.
• g4.rtx6kpro.large: 8x RTX PRO 6000 (768 GB VRAM) at \$24 per hour, or \$17,520 per month.
• g4.b300.large: 8x HGX B300 (2,304 GB VRAM) with 800 Gbps dual-plane RoCE at \$64 per hour, or \$46,720 per month.

Every server includes 20 TB of free outbound traffic and unlimited inbound.

Billing: Hourly or monthly, with reserved terms for lower rates.

Skip it if: You want a custom GPU mix. The Metal GPU line offers fixed plans, and bespoke builds go through the enterprise team.

4. NovoServe: best for flat-rate servers with one to eight GPUs

NovoServe supports up to 8 GPUs per node and up to 768 GB of VRAM (8x RTX PRO 6000), with 1 TB of RAM. The GPU list runs from the RTX PRO 6000 (96 GB) and H100 (80 GB) down to the A4000, plus AMD Instinct MI50, MI100, and MI210. Sample monthly prices:

• €306 for 1x RTX A4000 (16 GB).
• €421 for 1x RTX 3090 (24 GB).
• €1,361 for 1x RTX PRO 6000 (96 GB).
• €2,718 for 2x RTX PRO 6000 (192 GB).

Each sample includes 25 TB of traffic at a 2 Gbps rate, and unmetered options reach 50 and 100 Gbps ports. You get IPMI root access. Pre-racked systems deploy fast, while custom builds take 1 to 3 days.

Billing: Flexible monthly contracts, plus annual and multi-year leases with volume discounts. NovoServe does not bill hourly.

Skip it if: You want the newest platform at the lowest price. The sample configs use older Xeon CPUs and DDR4 memory.

5. OpenMetal: best for fixed monthly pricing with engineer help

OpenMetal offers two GPU lines, with one or two cards per server:

• RTX PRO 6000 Blackwell: 96 GB GDDR7.
• H200 NVL PCIe: 141 GB HBM3e.

Both run on dual Intel Xeon 6530P CPUs with 1 TB of DDR5. The private network runs at 20 Gbps (upgradeable to 40 Gbps) and the public network at 10 Gbps. You can join servers into multi-node clusters over the private mesh, and proof-of-concept deployments are available. OpenMetal engineers help with sizing and deployment. Servers run in Ashburn, VA and Los Angeles, CA.

Billing: Quote-only, fixed monthly, with no metered hours and no surprise egress. OpenMetal honors quotes for 30 days.

Skip it if: You need more than two GPUs in one server.

6. OVHcloud GPU Dedicated Servers: best for entry-priced inference

OVHcloud’s bare metal line uses NVIDIA L4 and L40S cards. The Scale-GPU configs run AMD EPYC Genoa CPUs with 192 GB to 1.125 TB of RAM and start at \$1,145 per month plus a matching installation fee. The HGR-AI-2 line pairs dual AMD EPYC 9354 CPUs with L40S GPUs and 100 Gbps private networking, starting at \$4,355 per month plus installation. Anti-DDoS protection, an API, and Terraform support come included.

Billing: Monthly plus an installation fee.

Skip it if: You need H100 or H200 cards on bare metal, because OVHcloud sells those as Public Cloud instances. The configs reviewed also showed “Soon available,” so check stock before you plan around them.

7. Atlantic.Net Dedicated GPU Hosting: best for regulated workloads

Atlantic.Net offers H100 NVL (94 GB), L40S (48 GB), and A2 (16 GB) servers with Intel Xeon CPUs, NVMe storage, full root access, NVLink and MIG support, and NVIDIA NGC integration.

The published H100 NVL “Fortress Business” package costs \$4,729.44 per month on a 12-month term, plus a \$450 setup fee. It bundles 28 vCPUs, 240 GB of DDR5, 2.4 TB of NVMe, 10 TB of monthly transfer, and a managed security suite.

Atlantic.Net states that its hosting supports HIPAA, HITECH, PCI DSS, and SOC 2 and 3 compliance. Verify the scope against your own requirements.

Billing: Monthly on a 12-month term, with longer terms discounted.

Skip it if: You want a wide GPU range or a bare server without the managed bundle.

How to choose: interconnect, virtualization overhead, and billing

Three technical choices decide most bare metal GPU purchases. Work through them in this order.

Interconnect

Distributed pre-training across nodes needs an InfiniBand or RoCE-class fabric. Single-node fine-tuning and inference run on NVLink or PCIe inside the server. Providers publish very different network specs:

• DigitalOcean lists GPU RDMA up to 3.2 Tbps.
• Latitude.sh B300 nodes carry 800 Gbps dual-plane RoCE.
• OVHcloud offers 50 and 100 Gbps private links.
• OpenMetal offers 20 Gbps private, upgradeable to 40 Gbps.

On Servers.com, each server includes two redundant 10 Gbps links for the public network and two for the private network. That gives 20 Gbps of capacity on each network, and customers can choose 25 Gbps links. Server-to-server transfer on the private network is free. For multi-node jobs, confirm the fabric and topology with every provider before you sign, Servers.com included.

Virtualization overhead

Single tenancy removes the noisy neighbor, another customer’s workload competing for the same hardware, along with the virtualization layer. Performance stays steadier as a result. Your CUDA calls go straight to the card, and you can use all of its VRAM. Verify it yourself after provisioning:

bash

nvidia-smi -L # lists every GPU the driver sees

lspci | grep -i nvidia # shows the physical PCIe device

systemd-detect-virt # prints "none" on bare metal

Bare metal does not make every job faster. Short, bursty jobs gain little from dedicated hardware, and you pay for the card whether it runs or not.

Billing model

Hourly billing suits prototyping and unpredictable load. Monthly or contract pricing suits inference that runs all day. To find your break-even point, divide the monthly price by the hourly price:

break-even hours = monthly price ÷ hourly price

With hypothetical numbers, a \$1,000 monthly server against a \$2.50 hourly rental breaks even at 400 hours. A month has about 730 hours, so the monthly plan wins once the GPU stays busy more than roughly 55% of the time. Run the same math with real quotes before you commit.

Which provider fits your situation

• Solo developer or prototype: Start with Latitude.sh hourly, or NovoServe if you want a flat bill.
• Startup moving to production: Compare Servers.com AI Compute, OpenMetal, and NovoServe on a fixed monthly cost for always-on inference.
• Enterprise multi-node training: Evaluate DigitalOcean and Latitude.sh B300 nodes, and ask Servers.com about the network fabric for your cluster design.
• Regulated workloads: Shortlist Atlantic.Net or Servers.com by Nexcess, then run your own compliance review.

For a quick decision, follow this flow:

Is the GPU busy most hours of the month?

├─ No → Rent hourly (Latitude.sh, or a GPU cloud)

└─ Yes → Does the job span multiple nodes?

├─ Yes → DigitalOcean, Latitude.sh B300, or Servers.com AI Compute (confirm fabric)

└─ No → Do you need a public price list?

├─ Yes → NovoServe, OVHcloud, Latitude.sh

└─ No → Servers.com AI Compute, OpenMetal

Also consider for short-term GPU rentals

Many GPU clouds rent virtual machines or containers by the hour, which follows a different model from the seven providers above. RunPod, Lambda, Vast.ai, and Hyperstack all serve short experiments and prototypes. Check each provider’s current pricing and tenancy model before you rely on it.

Choosing a GPU

Model weights set your minimum VRAM. At FP16, each billion parameters takes about 2 GB, and you need extra memory for the KV cache and activations. Fine-tuning needs more memory than inference. Use this table as a rough guide, and check it against NVIDIA documentation for your framework.

Model size	Approximate weight memory	Suggested GPU class
7B at FP16	About 14 GB	24 GB card (RTX A5000, RTX 3090)
13B at FP16	About 26 GB	48 GB card (RTX A6000, L40S)
70B at 4-bit	About 35 to 40 GB	48 GB card
70B at FP16	About 140 GB	Two 96 GB cards (2x RTX PRO 6000) or two 80 GB H100s

Security and compliance basics

Single tenancy keeps other customers off your hardware, which removes the shared-hypervisor risk. IPMI and root access give you control down to the firmware level, so lock down the management network and rotate credentials. DigitalOcean’s nodes ship without IPMI, which narrows that attack surface and also limits what you can do remotely.

Servers.com and Atlantic.Net offer compliance-ready infrastructure, but you still own compliance. Ask every provider for its current certifications and the scope by location.

Bare Metal GPU FAQ

Is bare metal GPU hosting faster than a GPU cloud VM?

It removes the hypervisor and the noisy neighbor, so performance stays steadier under sustained load. A bursty job may see little difference.

Do I need InfiniBand?

Distributed pre-training across nodes usually does. Single-node fine-tuning and inference usually run well on NVLink or PCIe.

Can I rent a bare metal GPU by the hour?

Yes at some providers, such as Latitude.sh. Others bill monthly or by contract.

How long does provisioning take?

It ranges from minutes for pre-racked or standard servers to 1 to 3 days for custom builds, and longer for contract builds. Ask for a date in writing.

Which GPU suits inference and which suits fine-tuning?

Inference runs on 24 to 48 GB cards for models up to 13B. Fine-tuning larger models needs 80 to 141 GB cards or multiple GPUs.

Can I run Kubernetes on bare metal GPU servers?

Yes. Expect to configure the NVIDIA drivers, the device plugin, and your networking.

What happens when I outgrow one node?

You add nodes over the private network. Check the interconnect first, because it sets how well training scales.