coreweave-performance-tuning
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". '
Profile →npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill coreweave-performance-tuning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```yaml # 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 ```yaml # HPA based on GPU utilization apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: inference-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: inference-server minReplicas: 2 maxReplicas: 10 metrics: - type: Pods pods: metric: name: DCGM_FI_DEV_GPU_UTIL
- GPU Selection by Workload
- Inference Optimization
- Autoscaling Tuning
- Performance Benchmarks
- Resources
- Next Steps
What does the coreweave-performance-tuning skill do?
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". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill coreweave-performance-tuning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.