8× H2001.13 TB HBM3e · Hopper
1.1 TB of HBM3e on the deepest CUDA ecosystem.
$21,592 /mo34 available
Configure
Solutions · Fine-tuning
Fine-tune 70B+ models without fighting for memory.
LoRA, QLoRA or full fine-tunes need room for weights, optimizer states and long sequences. Rent the memory you need for the month, from 141 GB to 288 GB per GPU.
- Servers from $8,996/mo
- At least 25% below market
- Root access in < 60 s
The problem
What slows teams down today
Out-of-memory loops
Shrinking batches and sequence lengths to fit 80 GB wastes days of engineering.
Paying for idle hours
Fine-tuning is iterative: on hourly billing, every pause between experiments costs money.
Unclear GPU choice
Memory, bandwidth and price per GB matter more than peak FLOPS for most fine-tunes.
How CoreGPU fits
Dedicated GPUs, a monthly price, no surprises
Every server is racked, tested and imaged before it goes on sale, so you get root access in under a minute and a bill you know in advance.
- 141 GB (H200) to 288 GB (MI350X, B300) per GPU
- Ready images with PyTorch, DeepSpeed, Axolotl-style tooling via pip
- Keep the server between experiments: idle time is already paid for
- Resize at renewal when the next model is bigger
Recommended servers
Configurations our engineers recommend
Live stock and the monthly price of the whole server. Open a GPU page to configure it.
FAQ
Fine-tuning: common questions
Another question? Our engineers answer within one business day.
Contact usHow much GPU memory do I need to fine-tune a 70B model?
Full fine-tuning in BF16 with Adam needs roughly 16 bytes per parameter, about 1.1 TB for 70B, so an 8× H200 server. LoRA or QLoRA fit on one to two GPUs. See our memory guide.
Can I use my own containers?
Yes. You have root access: run Docker, Podman or bare-metal environments with your own drivers if you prefer.
Is my training data isolated?
Yes. GPUs and servers are dedicated to you, and local disks are securely erased between customers. See Security.
Other solutions
Tell us about your workload
Model size, context length, deadline and budget: we reply with the right GPU and server count within one business day.