GPU instances for AI model Fine-tuning
Fine-tune large language models, vision models, and generative AI models with LoRA, QLoRA, supervised fine-tuning, or full-parameter training. Choose GPU containers, VMs, or bare metal with transparent hourly pricing and zero egress fees.
Host OpenClaw on always-on Virtual Servers for up to 85% less cost and connect it to external LLM APIs or routing layers. When you need self-hosted inference, you can also pair OpenClaw with Fluence GPU Cloud.
GPU
GPU instance type: h200
H200
Fluence
Fluence
$2.56/hr
$2.56/hr
CoreWeave
Core Weave
$6.30/hr
$6.30/hr
AWS
AWS
$7.90/hr
$7.90/hr
Google Cloud
Google Cloud
$10.84/hr
$10.84$
Available GPUs for AI inference
Choose cloud GPU instances by model, VRAM, deployment type, provider, region, and hourly price. Find the configuration that fits your fine-tuning method, model size, and expected runtime.
Certified for compliance with GDPR, ISO 27001,
SOC2 standards, top-tier facilities and
high-performance servers
All
Container
VM
Bare Metal
H100
80 GB
Info
80 GB RAM
Deployment
Container
Best fit
Large inference
Price
$1.24 /per hr
H200
141 GB
Info
141 GB RAM
Deployment
Container
Best fit
Memory-heavy fine-tuning
Price
$2.56 /per hr
A100
80 GB
Info
80 GB RAM
Deployment
Container
Best fit
LoRA and QLoRA
Price
$1.22 /per hr
RTX 4090
24 GB
Info
24 GB RAM
Deployment
Container
Best fit
Small-model PEFT
Price
$0.48 /per hr
H100
80 GB
Info
80 GB RAM
Deployment
VM
Best fit
Supervised fine-tuning
Price
$2.29 /per hr
H200
141 GB
Info
141 GB RAM
Deployment
VM
Best fit
Long-context fine-tuning
Price
$3.10 /per hr
A100
80 GB
Info
80 GB RAM
Deployment
VM
Best fit
Persistent fine-tuning
Price
$1.83 /per hr
RTX 4090
24 GB
Info
24 GB RAM
Deployment
VM
Best fit
Budget LoRA jobs
Price
$0.64 /per hr
H100
640 GB
Info
80 GB RAM
Deployment
Bare Metal
Best fit
Multi-GPU fine-tuning
Price
$19.35 /per hr
All
Container
VM
Bare Metal
H100
80 GB
Info
80 GB RAM
Deployment
Container
Best fit
Large inference
Price
$1.24 /per hr
H200
141 GB
Info
141 GB RAM
Deployment
Container
Best fit
Memory-heavy fine-tuning
Price
$2.56 /per hr
A100
80 GB
Info
80 GB RAM
Deployment
Container
Best fit
LoRA and QLoRA
Price
$1.22 /per hr
RTX 4090
24 GB
Info
24 GB RAM
Deployment
Container
Best fit
Small-model PEFT
Price
$0.48 /per hr
H100
80 GB
Info
80 GB RAM
Deployment
VM
Best fit
Supervised fine-tuning
Price
$2.29 /per hr
H200
141 GB
Info
141 GB RAM
Deployment
VM
Best fit
Long-context fine-tuning
Price
$3.10 /per hr
A100
80 GB
Info
80 GB RAM
Deployment
VM
Best fit
Persistent fine-tuning
Price
$1.83 /per hr
RTX 4090
24 GB
Info
24 GB RAM
Deployment
VM
Best fit
Budget LoRA jobs
Price
$0.64 /per hr
H100
640 GB
Info
80 GB RAM
Deployment
Bare Metal
Best fit
Cluster inference
Price
$19.35 /per hr
Choose how you want
to fine-tune AI models
GPU Containers
Launch packaged LoRA, QLoRA, and parameter-efficient fine-tuning jobs in a repeatable container environment.
Launch packaged LoRA, QLoRA, and parameter-efficient fine-tuning jobs in a repeatable container environment.
• LoRA and QLoRA
• Short experiments
• Reproducible environments
• LoRA and QLoRA
• Short experiments
• Reproducible environments
• LoRA and QLoRA
• Short experiments
• Reproducible environments
GPU VMs
Build a persistent environment with control over the operating system, drivers, libraries, storage, datasets, and model checkpoints.
Build a persistent environment with control over the operating system, drivers, libraries, storage, datasets, and model checkpoints.
• Supervised fine-tuning
• Custom training stacks
• Persistent checkpoints
• Supervised fine-tuning
• Custom training stacks
• Persistent checkpoints
• Supervised fine-tuning
• Custom training stacks
• Persistent checkpoints
Bare Metal
Use dedicated infrastructure for full-parameter fine-tuning, multi-GPU jobs, and sustained training runs.
Use dedicated infrastructure for full-parameter fine-tuning, multi-GPU jobs, and sustained training runs.
Dedicated hardware for maximum control, direct hardware access, stronger isolation, and high-utilization inference workloads.
• Dedicated GPU capacity
• Full hardware control
• Large model fine-tuning
• Dedicated GPU capacity
• Full hardware control
• Large model fine-tuning
• Dedicated GPU capacity
• Full hardware control
• Large model fine-tuning
What you can run on Fluence AI compute
LoRA and QLoRA fine-tuning
LoRA and QLoRA fine-tuning
Run parameter-efficient fine-tuning for open-source LLMs with your own model, adapter configuration, dataset, and training code.
Best fit: Containers / VMs
Supervised and instruction fine-tuning
Supervised and instruction fine-tuning
Train on curated input-output examples to adapt model behavior, instruction following, response format, or domain-specific performance.
Best fit: GPU VMs
Full-parameter fine-tuning
Full-parameter fine-tuning
Update the full model on GPU configurations sized for model weights, optimizer states, activations, checkpoints, and distributed training.
Best fit: VMs / Bare Metal
Vision and multimodal fine-tuning
Vision and multimodal fine-tuning
Customize image, video, audio, diffusion, vision-language, and other multimodal models with your own data and training framework.
Best fit: VMs / Bare Metal
Why choose Fluence for LLM fine-tuning
Why choose Fluence for LLM fine-tuning
Control fine-tuning costs
Control fine-tuning costs
Estimate by GPU rate, count, runtime; move datasets, checkpoints, models free.
Estimate by GPU rate, count, runtime; move datasets, checkpoints, models free.
Control fine-tuning costs
Estimate by GPU rate, count, runtime; move datasets, checkpoints, models free.
Choose your training environment
Choose your training environment
Choose containers, VMs, or bare metal by framework, storage, isolation, method.
Choose containers, VMs, or bare metal by framework, storage, isolation, method.
Choose your training environment
Choose containers, VMs, or bare metal by framework, storage, isolation, method.
Choose your training environment
Choose containers, VMs, or bare metal by framework, storage, isolation, method.
Access flexible GPU infrastructure
Access flexible GPU infrastructure
Access flexible GPU infrastructure
Choose GPU capacity by model, memory, provider, region, deployment, and availability.
Choose GPU capacity by model, memory, provider, region, deployment, and availability.
Access flexible GPU infrastructure
Choose GPU capacity by model, memory, provider, region, deployment, and availability.
Avoid managed-platform lock-in
Avoid managed-platform lock-in
Avoid managed-platform lock-in
Avoid managed-platform lock-in
Control models, data, code, checkpoints, and evaluations without proprietary APIs.
Control models, data, code, checkpoints, and evaluations without proprietary APIs.
Request a custom GPU cluster
Request a custom GPU cluster
Need a different GPU setup or configuration?
Send us your requirements and our team will
get back to you.
Need a different GPU setup or configuration?
Send us your requirements and our team will get back to you.
Top-tier GPU infrastructure for AI model fine-tuning
Run LLM and generative AI fine-tuning on high-performance GPU hardware at best-in-class locations. Review the provider, region, hardware configuration, and available compliance information before deployment.
Certified for compliance with GDPR, ISO 27001,
SOC2 standards, top-tier facilities and
high-performance servers
FAQ
What is LLM fine-tuning?
What is the difference between LoRA, QLoRA, and full fine-tuning?
Which GPU is best for LLM fine-tuning?
How much VRAM do I need for LLM fine-tuning?
How much does LLM fine-tuning cost?
Show more
What is LLM fine-tuning?
What is the difference between LoRA, QLoRA, and full fine-tuning?
Which GPU is best for LLM fine-tuning?
How much VRAM do I need for LLM fine-tuning?
How much does LLM fine-tuning cost?
Show more
Start LLM fine-tuning on Fluence GPUs
Bring your model, dataset, and training stack. Choose GPU containers, VMs, or bare metal with transparent pricing, zero egress fees, and no managed-platform lock-in.
