CoreWeave

CoreWeave for AI Agents: Scalable GPU Cloud for Training and Inference

Omaima Mazhar

11 Dec 2025 — 4 min read

Why CoreWeave Fits AI Agents

AI agents place unique demands on infrastructure: they train on large, evolving datasets, and then serve inference workloads that are both interactive (low latency) and bursty (spiky concurrency). CoreWeave is purpose-built for this pattern. It provides fast access to a spectrum of NVIDIA GPUs, Kubernetes-native orchestration, and elastic scaling so teams can move from experimentation to production without re-architecting. With CoreWeave, you can fine-tune models, precompute tools or embeddings, and serve token-streaming inference on the same GPU cloud, optimizing for cost and performance at each stage. New to agent architectures? See our ultimate guide on AI agents. As you evaluate the GPU stack, explore Nvidia for AI Agents: GPUs, CUDA, and Inference Acceleration.

Training AI Agents on CoreWeave

Choose the right GPU for the job

Foundation or large fine-tunes: Use high-memory, high-bandwidth GPUs (e.g., A100/H100-class) for efficient distributed training. These typically provide up to 80 GB of VRAM per GPU and excellent interconnect on multi-GPU nodes.
Parameter-efficient fine-tuning (LoRA/QLoRA): L40S- or A40-class GPUs can be cost-effective for smaller models (7B–13B) or PEFT workflows where memory pressure is lower.
Data preprocessing and tool synthesis: Mix CPU pools with mid-tier GPUs for tokenization, embedding generation, and tool-usage simulations that don’t require top-tier accelerators.

On CoreWeave, you can select specific GPU types per workload, enabling a “right-sized” training stack rather than overpaying for a single monolithic configuration. If you need help designing distributed training and fine-tuning pipelines, our Machine Learning team can help.

A practical distributed training recipe

For PyTorch DDP or FSDP, prioritize predictable interconnects and local storage:

Prefer multi-GPU nodes when possible to leverage NVLink on supported SKUs for faster intra-node communication.
Use local NVMe scratch for dataloader caches and checkpoints to reduce I/O bottlenecks.
Pin NCCL settings (e.g., NCCL_P2P_DISABLE=0, NCCL_SOCKET_IFNAME) for reliable collective ops under Kubernetes.
Shard datasets across workers and prefetch aggressively to keep GPUs fed.

apiVersion: batch/v1
kind: Job
metadata:
name: agent-train
spec:
template:
spec:
containers:
- name: trainer
image: your-registry/agent-train:latest
resources:
limits:
nvidia.com/gpu: 8
env:
- name: NCCL_DEBUG
value: INFO
- name: TORCH_DISTRIBUTED_DEBUG
value: DETAIL
volumeMounts:
- name: scratch
mountPath: /scratch
volumes:
- name: scratch
emptyDir:
medium: ""
sizeLimit: 500Gi
restartPolicy: Never

This example requests eight GPUs on a single node and mounts an ephemeral NVMe-backed scratch volume for high-speed I/O. For multi-node, allocate fewer GPUs per pod, set rendezvous parameters, and rely on a job controller or launcher (e.g., torchrun) to coordinate ranks.

Be preemption-tolerant to save costs

Exploit preemptible capacity for PEFT runs and hyperparameter sweeps. Make training idempotent and checkpoint frequently (e.g., every few minutes).
Write checkpoints atomically to object storage or persistent volumes and verify integrity before resuming.
Separate stateful orchestrators (e.g., schedulers, metadata stores) on on-demand nodes from stateless workers on preemptible nodes.

Low-Latency Inference for Agents

Serve models with GPU-aware RAG and tool use

Agents frequently mix generation, retrieval, and tool calls. On CoreWeave you can deploy:

vLLM or similar LLM servers for high token throughput with continuous batching and KV-cache management.
ONNX/TensorRT or Triton for smaller policy or routing models that benefit from compiled inference.
Embedding services on lighter GPUs, decoupled from the main LLM to reduce queue contention.

Keep the LLM on a GPU with sufficient memory for the target context length and concurrency, and isolate retrieval/embedding services on separate nodes so bursts in one path don’t starve the others.

For practical agent workflows and plugins, see ChatGPT as an AI Agent: Workflows, Plugins, and Real-World Use Cases. If you’re building realtime voice experiences, explore Voice AI Agents with ElevenLabs: TTS, Dubbing, and Realtime Conversation. Teams integrating with Google apps can reference Google’s AI Agents: Gemini, Workspace Integrations, and Search. And for robust RAG and embeddings pipelines, our NLP Solutions can help design and productionize the stack.

Kubernetes deployment pattern for vLLM

apiVersion: apps/v1
kind: Deployment
metadata:
name: vllm-inference
spec:
replicas: 2
selector:
matchLabels:
app: vllm
template:
metadata:
labels:
app: vllm
spec:
containers:
- name: server
image: your-registry/vllm:latest
args: ["--model", "/models/your-7b", "--max-num-batched-tokens", "8192"]
resources:
limits:
nvidia.com/gpu: 1
ports:
- containerPort: 8000

Combine this with a Horizontal Pod Autoscaler that scales on request rate or GPU utilization. For larger models, add tensor or pipeline parallelism flags and select multi-GPU nodes.

Scaling and placement

Vertical scale-first: Fit the model on a single GPU if possible for minimal latency.
Horizontal scale for concurrency: Add replicas and a lightweight gateway that load-balances by queue depth.
Warm pools: Keep a small buffer of idle pods to absorb bursts; set low startup probes to reduce cold-start penalties.
GPU selection: Use node selectors or labels to pin inference to L40S-class GPUs when memory allows; move to A100/H100-class when you need larger context windows or heavier batching.

Data and Storage Architecture

Training and inference benefit from distinct storage tiers on CoreWeave:

Object storage for datasets, models, and checkpoints. Version artifacts and enable resumable uploads.
Persistent volumes for fine-tuning outputs, tokenizer caches, and RAG indexes.
Ephemeral NVMe for high-speed scratch during training or batch preprocessing.

For RAG, prebuild vector indexes offline, store them on a persistent volume, and load into memory on startup. This shortens cold starts and keeps latency predictable.

Cost and Capacity Planning on CoreWeave

Match GPU to phase: H100/A100 for heavy training; L40S/A40 for inference and embeddings when memory fits.
Mix purchasing models: Run stateful control planes on on-demand; schedule stateless or retryable jobs on discounted capacity.
Autoscale intentionally: Use queue length for inference autoscaling and GPU utilization or step completion rate for batch jobs.
Cache smartly: Keep tokenizers, weights, and compiled engines on local disk to avoid repeated downloads and compilations.

Monitoring, Reliability, and SLOs

Track token metrics: tokens/sec, time-to-first-token, batch queue time, and context cache hit ratios.
GPU health and memory: monitor SM utilization, HBM usage, and memory fragmentation; restart on persistent leaks.
Graceful degradation: fallback to smaller models or reduced context for overflow traffic to protect latency SLOs.
Checkpoint discipline: store training checkpoints and inference engine artifacts with metadata for reproducibility and quick rollbacks.

Harden model endpoints, data pipelines, and runtime guardrails with our AI Security expertise.

Putting It Together

With CoreWeave, you can build an end-to-end AI agent platform that scales smoothly:

Train or fine-tune on high-memory GPUs with DDP/FSDP and aggressive checkpointing.
Serve inference via vLLM or Triton on right-sized GPUs with autoscaling and warm pools.
Separate embedding and retrieval from generation to prevent resource contention.
Use object storage and NVMe tiers strategically to keep pipelines fast and economical.

The result is a production-ready agent stack that delivers fast iteration during training, steady low-latency inference at peak times, and sensible cloud spend—all leveraging the specialized GPU infrastructure CoreWeave is known for. Design teams can also explore AI in Figma: Design Agents, Automation, and Prototyping Plugins. These patterns apply across sectors such as Finance and Healthcare.