Gpus

Modal GPU Reference

Detailed reference for GPU acceleration on Modal.

Available GPUs

GPU	VRAM	Max Count	Best For
T4	16 GB	8	Budget inference, light training
L4	24 GB	8	Inference
A10	24 GB	4	Inference, light training
A100-40GB	40 GB	8	Training, large model inference
A100-80GB	80 GB	8	Large models, distributed training
L40S	48 GB	8	Best cost/performance ratio
H100	80 GB	8	High-performance training
H200	141 GB	8	Very large models
B200	192 GB	8	Largest models, cutting-edge

GPU Selection

Single GPU

@app.function(gpu="A100")
def train():
    import torch
    assert torch.cuda.is_available()

Multiple GPUs

# 8 H100s for large model training
@app.function(gpu="H100:8")
def train_large_model():
    import torch
    device_count = torch.cuda.device_count()  # 8

GPU Fallbacks

Request multiple GPU types in priority order:

@app.function(gpu=["H100", "A100-80GB", "A100-40GB:2"])
def flexible_function():
    # Will try H100 first, then A100-80GB, then 2x A100-40GB
    ...

Specific GPU Variants

# Specific A100 variant
@app.function(gpu="A100-40GB")
def smaller_a100():
    ...

@app.function(gpu="A100-80GB")
def larger_a100():
    ...

# Prevent H100 → H200 auto-upgrade
@app.function(gpu="H100!")
def strict_h100():
    ...

GPU Selection Guidelines

For Inference

Model Size	Recommended GPU
< 7B params	L4, T4
7B-13B params	L40S, A100-40GB
13B-70B params	A100-80GB, H100
> 70B params	H100:2+, H200, B200

For Training

Training Type	Recommended GPU
Fine-tuning (LoRA)	A100-40GB, L40S
Full fine-tuning	A100-80GB, H100
Pre-training	H100:8, H200:8

Multi-GPU Training

PyTorch DDP

@app.function(gpu="A100:4")
def train_ddp():
    import subprocess
    import sys
    subprocess.run(
        ["torchrun", "--nproc_per_node=4", "train.py"],
        check=True
    )

Multi-Node Clusters (Beta)

import modal.experimental

@app.function(gpu="H100:8", timeout=86400)
@modal.experimental.clustered(size=4, rdma=True)  # 4 nodes × 8 GPUs = 32 GPUs
def train_distributed():
    cluster_info = modal.experimental.get_cluster_info()
    
    from torch.distributed.run import run, parse_args
    run(parse_args([
        f"--nnodes={4}",
        f"--node-rank={cluster_info.rank}",
        f"--master-addr={cluster_info.container_ips[0]}",
        "--nproc-per-node=8",
        "--master-port=1234",
        "train.py",
    ]))

CUDA Setup

Using Pre-installed Drivers

Modal provides CUDA drivers by default. Many libraries work with just pip:

image = modal.Image.debian_slim().pip_install("torch")

@app.function(gpu="A100", image=image)
def cuda_function():
    import torch
    print(torch.cuda.is_available())  # True

Full CUDA Toolkit

For libraries requiring nvcc or CUDA headers:

image = (
    modal.Image.from_registry(
        "nvidia/cuda:12.8.1-devel-ubuntu24.04",
        add_python="3.12"
    )
    .entrypoint([])
    .pip_install("flash-attn", "triton")
)

@app.function(gpu="H100", image=image)
def advanced_cuda():
    ...

GPU Metrics

Access GPU metrics from within your function:

@app.function(gpu="A100")
def check_gpu():
    import subprocess
    result = subprocess.run(
        ["nvidia-smi", "--query-gpu=utilization.gpu,memory.used,memory.total", "--format=csv"],
        capture_output=True, text=True
    )
    print(result.stdout)

Cost Optimization

1. Right-size your GPU: Start with smaller GPUs and scale up
2. Use container idle timeout: Keep containers warm for repeated requests
3. Batch requests: Use @modal.batched for throughput
4. Consider GPU fallbacks: Accept any available GPU type

@app.cls(
    gpu="L40S",
    container_idle_timeout=300,  # Keep warm for 5 min
)
class InferenceService:
    @modal.enter()
    def load_model(self):
        self.model = load_model()
    
    @modal.batched(max_batch_size=32, wait_ms=100)
    async def predict(self, inputs: list[str]) -> list[str]:
        return self.model.batch_predict(inputs)