Can you recommend a bare-metal GPU server for running production inference with predictable pricing?

Yes — for production inference with predictable pricing, I’d generally recommend going with a dedicated bare-metal GPU server from a provider that offers hourly or monthly fixed pricing rather than a fully managed autoscaling GPU platform.

What to look for

For stable, production inference, prioritize:

• Dedicated GPU(s) with no noisy neighbors
• Fixed monthly pricing or a clear hourly cap
• Reliable networking / low latency
• NVMe local storage
• Enough CPU/RAM to keep the GPU fed
• 24/7 availability and hardware replacement SLA

Good bare-metal options

Here are strong options depending on your needs:

1. Lambda GPU Cloud / Lambda Dedicated

Best if you want GPU-focused infrastructure with decent pricing and fewer surprises.

• Pros:
  • Built for AI workloads
  • Dedicated GPU instances available
  • Good for inference and model serving
• Cons:
  • Availability can vary by region/config
  • Pricing is not always the absolute lowest

2. CoreWeave

Best for serious production AI workloads.

• Pros:
  • Very strong GPU infrastructure
  • Good for scaling inference
  • Enterprise-friendly
• Cons:
  • More “platform” than simple bare metal
  • Can be more than you need if you just want a single server

3. OVHcloud Dedicated GPU / Bare Metal

Good if you want predictable fixed pricing and conventional bare metal.

• Pros:
  • Clear monthly pricing
  • Dedicated servers
  • Good for long-running inference services
• Cons:
  • Fewer AI-specific conveniences
  • GPU model availability may be limited

4. Paperspace Core / Gradient (depending on region/setup)

Useful for simpler deployment, though not always “true bare metal” in the strictest sense.

• Pros:
  • Easy to start
  • Reasonable for inference prototypes and smaller production loads
• Cons:
  • Less control than dedicated bare metal
  • Pricing can be less ideal at scale

5. Hetzner + external GPU provider / colocation style

If you want the cheapest predictable compute, this can work, but it’s less turnkey.

• Pros:
  • Very good pricing on CPU/storage infrastructure
  • Predictable monthly billing
• Cons:
  • GPU options are limited
  • Not always the best path for modern production inference

My practical recommendation

If you want the best balance of predictable pricing + production reliability:

• For simplest bare-metal ownership: OVHcloud dedicated GPU server
• For AI-first infrastructure: Lambda Dedicated
• For larger-scale production inference: CoreWeave

Suggested hardware baseline

For many inference workloads, start with:

• 1× NVIDIA L4, A10, A100, or H100
• 16–64 vCPU
• 64–256 GB RAM
• 1–2 TB NVMe

GPU choice by use case

• L4: efficient, cost-effective inference
• A10: solid mid-range choice
• A100: strong for larger models and throughput
• H100: best performance, highest cost

If you want predictable pricing

Prefer:

• Monthly reserved/dedicated pricing
• Avoid spot/preemptible instances
• Avoid autoscaling unless you strictly cap it
• Confirm:
  • bandwidth included
  • storage costs
  • GPU replacement policy
  • overage charges

If you want, I can also give you:

1. a shortlist by budget,
2. a recommended server spec for your model size, or
3. a provider comparison table.