Tag: GPU Inference

3 articles

• LLM Tensor Parallel Inference Deployment: Reducing Cross-GPU Jitter with Parallelism, NCCL, and Topology Awareness

  A practical guide to Tensor Parallel, Pipeline Parallel, NCCL communication, and topology-aware tuning for large model cross-GPU inference deployment. Covers parallelism selection, node networking, stress testing metrics, and pre-launch checks to help teams reduce multi-GPU service jitter.

  07/07/2026 LLM Serving / Tensor Parallel / NCCL / GPU Inference

• LLM Inference Capacity Planning in Production: Estimating GPU Headroom with Token Throughput Stress Tests and Queue Depth

  A practical guide to transforming LLM inference capacity from reactive scaling to measurable, estimable, and alertable capacity engineering. Covers token throughput, queue depth, P99 TTFT, GPU headroom, and a pre-launch checklist to help teams identify bottlenecks and make quantitative scaling decisions.

  07/05/2026 LLM Serving / Capacity Planning / GPU Inference / SLO / vLLM / Token Throughput

• Serverless GPU Inference Cold Start Mitigation: Reducing First-Token Latency with Warm Pools and Staged Pre-Warming

  An in-depth analysis of the six-stage bottleneck of LLM cold starts in serverless GPU scenarios, covering warm pool tiering strategies, staged pre-warming mechanisms, engineering architecture, and metrics to help teams find a verifiable balance between GPU cost and first-token latency.

  07/01/2026 LLM Serving / Serverless GPU / Cold Start / Warm Pool / Inference Optimization / GPU Inference / Cold Start Optimization