Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
metadata
{"category":"mlops","emoji":"☁️","color":"violet","vibe":"Models are only as good as their infrastructure.","original_name":"lambda-labs"}
🎛️ DEPUTY CHIEF OF STAFF REPORTING PROTOCOL
PRIORITY DIRECTIVE: If this task was assigned to you by the agents-orchestrator, treat it as HIGHEST PRIORITY and execute immediately.
# Get instance IP from console
ssh ubuntu@<INSTANCE-IP>
# Or with specific key
ssh -i ~/.ssh/lambda_key ubuntu@<INSTANCE-IP>
GPU instances
Available GPUs
GPU
VRAM
Price/GPU/hr
Best For
B200 SXM6
180 GB
$4.99
Largest models, fastest training
H100 SXM
80 GB
$2.99-3.29
Large model training
H100 PCIe
80 GB
$2.49
Cost-effective H100
GH200
96 GB
$1.49
Single-GPU large models
A100 80GB
80 GB
$1.79
Production training
A100 40GB
40 GB
$1.29
Standard training
A10
24 GB
$0.75
Inference, fine-tuning
A6000
48 GB
$0.80
Good VRAM/price ratio
V100
16 GB
$0.55
Budget training
Instance configurations
8x GPU: Best for distributed training (DDP, FSDP)
4x GPU: Large models, multi-GPU training
2x GPU: Medium workloads
1x GPU: Fine-tuning, inference, development
Launch times
Single-GPU: 3-5 minutes
Multi-GPU: 10-15 minutes
Lambda Stack
All instances come with Lambda Stack pre-installed:
# Check GPU
nvidia-smi
# Check PyTorch
python -c "import torch; print(torch.cuda.is_available())"# Check CUDA version
nvcc --version
Python API
Installation
pip install lambda-cloud-client
Authentication
import os
import lambda_cloud_client
# Configure with API key
configuration = lambda_cloud_client.Configuration(
host="https://cloud.lambdalabs.com/api/v1",
access_token=os.environ["LAMBDA_API_KEY"]
)
List available instances
with lambda_cloud_client.ApiClient(configuration) as api_client:
api = lambda_cloud_client.DefaultApi(api_client)
# Get available instance types
types = api.instance_types()
for name, info in types.data.items():
print(f"{name}: {info.instance_type.description}")
# On instance
jupyter lab --ip=0.0.0.0 --port=8888
# From local machine with tunnel
ssh -L 8888:localhost:8888 ubuntu@<IP>
# Open http://localhost:8888
