Docker Execution
Any container.
Any GPU.
Full environment control. Per-second billing. Your containers, running on the exact hardware you need.
$ lyceum docker run myregistry/train:v2 \
--machine gpu.h100.8x \
--volume ./data:/data
▸ Pulling myregistry/train:v2...
▸ Image size: 4.2 GB
▸ Provisioning 8x H100 cluster...
▸ Mounting volumes...
Container running on 8x H100
[rank 0] Initializing distributed training...
[rank 0] Model loaded: 70B parameters
[rank 0] Starting epoch 1/100...
Full control. Here's why.
Any Docker image
If it runs in Docker, it runs on Lyceum. No modifications needed.
Per-second billing
Pay only for what you use. A 4-minute job costs 4 minutes.
Instant availability
GPUs provision in seconds. No waiting in queues.
train:v2
4.2 GB image
Your container active
CUDA 12.1
8× H100 640 GB
Available GPUs
L40S to B200. Multi-GPU and multi-node available.
| GPU | VRAM | Price/hr |
|---|---|---|
| NVIDIA B300 | 288 GB | $8.49 |
| NVIDIA B200 | 192 GB | $6.29 |
| NVIDIA H200 | 141 GB | $3.99 |
| NVIDIA H100 Popular | 80 GB | $3.59 |
| NVIDIA A100 | 80 GB | $2.50 |
Want something simpler?
For rapid prototyping with just Python scripts, try Python execution. Zero config, automatic dependency detection, instant iteration.
Explore Python executionRun your first GPU job.
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