GPU-Powered Intelligence

A cloud GPU with an AI agent that writes code, trains models, and runs experiments. Describe the goal. Watch it happen.

session: llama-3-finetune

runningRTX 4090 24GB

Fine-tune Llama-3 8B on my dataset with LoRA. Use batch size 4, evaluate every 500 steps.

Setting up the training environment. Installing dependencies and configuring LoRA parameters.

execute_code // running...pip install transformers peft accelerate
Successfully installed 12 packages

GPU Metrics

Utilization87%

Memory18.2 / 24 GB

Temperature72 C

Power285 W

Session cost$0.37

Uptime00:51:23

The Agent Operates the Machine

Describe your research. The agent provisions the GPU, writes code, executes it, and streams results back.

Describe

Natural language in

> Fine-tune Llama-3 8B on my
  dataset with LoRA, batch
  size 4, eval every 500 steps

Execute

Agent writes and runs code

import torch
from transformers import AutoModelForCausalLM
from peft import get_peft_model, LoraConfig

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-8B",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

Stream

Live results flow back

[Step 500]  loss: 1.342  lr: 2e-4
[Step 1000] loss: 0.871  lr: 1.8e-4
[Step 1500] loss: 0.654  lr: 1.5e-4
[Eval]      accuracy: 89.2%
-- Training complete. Model saved.

Precision Tooling

Every component built for serious GPU workloads.

Real-Time Streaming

Token-by-token output, live tool calls, and GPU metrics as your agent thinks and acts.

Secure Sandbox

Every session runs in an isolated container with dedicated GPU. Your data never leaves your pod.

Persistent Storage

50GB NVMe storage survives restarts. Datasets, checkpoints, and results stay where you left them.

Multi-Model Interplay

Consult GPT-4o, Gemini, and other models for code audits, validation, and second opinions.

Terminal Access

Full interactive shell in your browser. Install packages, run scripts, debug in real time.

File Management

Upload, download, and browse your workspace. Full filesystem access, no restrictions.

Choose Your Compute

Pay only for what you use. No subscriptions. No commitments.

RTX 4090

24GB VRAM

Best for fine-tuning 7B models, inference, small experiments

$0.44 / hr

RTX A6000

48GB VRAM

Great for 13B models, larger batch sizes, multi-task

$0.79 / hr

A100 80GB

80GB VRAM

Premium for 70B models, large-scale training, research

$1.64 / hr

H100 80GB

80GB VRAM

Flagship for cutting-edge research, fastest training

$3.29 / hr

All GPUs include 50GB persistent NVMe storage and a full Linux environment.

Enter GPU Hub

Provision a GPU, describe your research, and let the agent handle the rest.

Launch Your First Session

GPU-Powered Intelligence

A cloud GPU with an AI agent that writes code, trains models, and runs experiments. Describe the goal. Watch it happen.

Start Building

session: llama-3-finetune

runningRTX 4090 24GB

Fine-tune Llama-3 8B on my dataset with LoRA. Use batch size 4, evaluate every 500 steps.

Setting up the training environment. Installing dependencies and configuring LoRA parameters.

execute_code // running...pip install transformers peft accelerate
Successfully installed 12 packages

GPU Metrics

Utilization87%

Memory18.2 / 24 GB

Temperature72 C

Power285 W

Session cost$0.37

Uptime00:51:23

The Agent Operates the Machine

Describe your research. The agent provisions the GPU, writes code, executes it, and streams results back.

Describe

Natural language in

> Fine-tune Llama-3 8B on my
  dataset with LoRA, batch
  size 4, eval every 500 steps

Execute

Agent writes and runs code

import torch
from transformers import AutoModelForCausalLM
from peft import get_peft_model, LoraConfig

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-8B",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

Stream

Live results flow back

[Step 500]  loss: 1.342  lr: 2e-4
[Step 1000] loss: 0.871  lr: 1.8e-4
[Step 1500] loss: 0.654  lr: 1.5e-4
[Eval]      accuracy: 89.2%
-- Training complete. Model saved.

Precision Tooling

Every component built for serious GPU workloads.

Real-Time Streaming

Token-by-token output, live tool calls, and GPU metrics as your agent thinks and acts.

Secure Sandbox

Every session runs in an isolated container with dedicated GPU. Your data never leaves your pod.

Persistent Storage

50GB NVMe storage survives restarts. Datasets, checkpoints, and results stay where you left them.

Multi-Model Interplay

Consult GPT-4o, Gemini, and other models for code audits, validation, and second opinions.

Terminal Access

Full interactive shell in your browser. Install packages, run scripts, debug in real time.

File Management

Upload, download, and browse your workspace. Full filesystem access, no restrictions.

Choose Your Compute

Pay only for what you use. No subscriptions. No commitments.

RTX 4090

24GB VRAM

Best for fine-tuning 7B models, inference, small experiments

$0.44 / hr

RTX A6000

48GB VRAM

Great for 13B models, larger batch sizes, multi-task

$0.79 / hr

A100 80GB

80GB VRAM

Premium for 70B models, large-scale training, research

$1.64 / hr

H100 80GB

80GB VRAM

Flagship for cutting-edge research, fastest training

$3.29 / hr

All GPUs include 50GB persistent NVMe storage and a full Linux environment.

Enter GPU Hub

Provision a GPU, describe your research, and let the agent handle the rest.

Launch Your First Session