deploybase

How to Deploy vLLM on CoreWeave

Deploybase · July 5, 2025 · Tutorials

Contents

How to Deploy VLLM Coreweave: What is vLLM

How to Deploy Vllm Coreweave is the focus of this guide. vLLM is a high-throughput inference engine for large language models. The system accelerates LLM serving through parallel computation and memory management innovations. vLLM reduces inference latency by 10-15x compared to standard serving implementations.

Why CoreWeave for vLLM

CoreWeave offers GPU infrastructure specifically designed for machine learning workloads. The platform provides dedicated H100, H200, A100, and L40S GPUs without the noisy neighbor problem common in shared cloud environments.

CoreWeave's pricing structure makes vLLM deployment cost-effective. For batch inference workloads, CoreWeave's 8xH100 configuration costs $49.24/hour, while A100 clusters run at $21.60/hour. Teams requiring high-performance inference find these options competitive compared to AWS GPU pricing or Azure alternatives.

Prerequisites

Before deploying vLLM on CoreWeave, confirm the following prerequisites are met:

  • CoreWeave account with active billing
  • Docker installed locally for testing
  • Python 3.9 or higher
  • At least 50GB storage for model weights
  • Familiarity with container deployments
  • SSH access configured for CoreWeave instances

Deployment Steps

Step 1: Create a CoreWeave Account

Visit the CoreWeave dashboard and provision a GPU instance. Select the appropriate GPU type based on model size. H100 GPUs accommodate models up to 70B parameters with acceptable latency.

Step 2: Prepare the Docker Image

Create a Dockerfile with vLLM dependencies:

FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
RUN pip install vllm
RUN pip install torch torchvision torchaudio
COPY . /app
WORKDIR /app
CMD ["python", "-m", "vllm.entrypoints.api_server"]

Step 3: Deploy on CoreWeave

Push the image to CoreWeave's container registry:

docker build -t vllm-coreweave .
corectl push vllm-coreweave:latest

Launch the deployment through the CoreWeave dashboard or API. Specify resource requests matching the selected GPU type.

Step 4: Configure Load Balancing

CoreWeave integrates with Kubernetes for orchestration. Configure replicas to distribute requests across multiple GPU instances:

replicas: 2
resources:
  gpu: 1
  memory: 80Gi

Configuration Optimization

vLLM supports several optimization flags that reduce memory overhead and improve throughput:

Quantization: Enable 4-bit or 8-bit quantization to reduce model size by 75%. CoreWeave's H100 GPUs handle quantized models efficiently.

Batch Size: Increase batch_size to 32 or 64 for throughput-optimized workloads. Single-request latency increases slightly but overall system throughput improves significantly.

Tensor Parallelism: Distribute model weights across multiple GPUs for large models. A 70B parameter model requires at least two A100 or H100 GPUs.

Token Optimization: vLLM's paged attention mechanism reduces token memory consumption by 75% compared to standard approaches. This enables larger batch sizes on the same hardware.

Compare the compute costs using CoreWeave GPU pricing to ensure optimal resource allocation. For development environments, L40S GPUs at $0.79/hour on RunPod provide excellent cost-per-performance ratios for testing before production deployment.

FAQ

How much does vLLM deployment on CoreWeave cost monthly? Monthly costs depend on GPU selection and utilization. An H100 instance at $49.24/hour runs approximately $35,650/month assuming full utilization. Most teams use reserved capacity discounts reducing effective costs by 30-40%.

Can I run open-source models like Llama 3 on vLLM? Yes. vLLM supports all major open-source models including Llama 3, Mistral, and Mixtral. Model compatibility depends on framework support, not vLLM itself.

What's the typical latency for a 70B model on CoreWeave H100s? Typical latency ranges from 100-300ms for first-token response with batch size of 1. Throughput increases substantially with batching, reaching 500+ tokens/second across 32 concurrent requests.

Does CoreWeave charge for data transfer? CoreWeave charges $0.05/GB for inbound traffic and $0.10/GB for outbound data transfer. Model downloading counts as inbound transfer.

How do I handle model updates without downtime? Use Kubernetes rolling deployments. CoreWeave's orchestration automatically routes traffic to updated instances while previous versions remain active.

Learn more about deploying LLMs across different platforms. Review how to deploy Llama 3 on RunPod for comparison. Explore deploying Stable Diffusion on Vast AI for similar workflows. Check the Mistral deployment guide on Lambda Labs for alternative infrastructure.

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