coreweave-performance-tuning

name	coreweave-performance-tuning
description	Optimize CoreWeave GPU inference latency and throughput. Use when reducing inference latency, maximizing GPU utilization, or tuning batch sizes and concurrency. Trigger with phrases like "coreweave performance", "coreweave latency", "coreweave throughput", "optimize coreweave inference".
allowed-tools	Read, Write, Edit, Bash(kubectl:*)
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","gpu-cloud","kubernetes","inference","coreweave"]
compatibility	Designed for Claude Code

CoreWeave Performance Tuning

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

GPU Selection by Workload

Workload	Recommended GPU	Why
LLM inference (7-13B)	A100 80GB	Good balance of memory and cost
LLM inference (70B+)	8xH100	NVLink for tensor parallelism
Image generation	L40	Good for diffusion models
Training (large models)	8xH100 SXM5	Fastest interconnect
Batch processing	A100 40GB	Cost-effective

Inference Optimization

# Continuous batching with vLLM
containers:
  - name: vllm
    args:
      - "--model=meta-llama/Llama-3.1-8B-Instruct"
      - "--max-num-batched-tokens=8192"
      - "--max-num-seqs=256"
      - "--gpu-memory-utilization=0.90"
      - "--enable-prefix-caching"
      - "--dtype=float16"

Autoscaling Tuning

# HPA based on GPU utilization
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: inference-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1