…it tackles data bottlenecks natively on the device. Developers can easily integrate high-throughput compression directly into their Python workflows (such as PyTorch or TensorFlow). Measured checkpoint compression ratios We fine-tuned…
…segmented reduction variant, new segmented scan and binary search primitives, and integrates with updated CUDA Python and CuPy for streamlined workflow and interoperability across frameworks. AI-generated content may summarize information incompletely…
…generate a complete production-grade microservice with REST APIs, health monitoring, deployment automation, and Kafka integration — all in one development session. How to generate a VLM-powered vision AI application: Step 1…
…The architecture is built from the bottom up, starting with low-level communication protocols and moving up to high-level model-specific workflows. This post uses Boltz as the example codebase. To…
…kubectl apply -f results/disagg/top1/k8s_deploy.yaml This workflow generalizes across models and hardware. The same interface applies whether deploying Qwen3-32B on eight NVIDIA H200 GPUs or DeepSeek-V3…
…Megatron Core framework modifications for supporting Dynamic-CP This section introduces the pipeline of Dynamic CP integration into Megatron Core. Building multiple context parallel groups per rank With standard context parallelism, each…
…KV cache growth and attention computation often dominate the end-to-end rollout time in RL workflows with long output sequence lengths (OSL) while also saturating memory bandwidth and slowing down token…
…Behind every one of these workflows is an inference stack under significant KV cache pressure. Lets take Claude Code as an example. After the first API call that writes the conversation prefix…
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