Host offloading is most useful when GPU memory limits model size, sequence length, or batch size, and when selected tensors are large enough to reduce HBM pressure. It is especially useful when offloading can replace expensive activation rematerialization or make a larger batch configuration feasible. Performance depends on overlap. Host offloading works best when the workload has enough compute, communication, or other independent work to hide transfer latency. On NVIDIA GPUs, XLA helps create this overlap by using dedicated copy streams, scheduling transfers with LHS, and enabling pipelined
Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading | NVIDIA Technical BlogSpeculative decoding has two phases: drafting and verification. A smaller draft model proposes future tokens. The target model verifies those tokens in parallel and accepts the longest valid prefix. If the draft is correct, the system generates multiple tokens with one target-model verification pass. Traditional speculative decoding methods often use autoregressive draft models. These drafters still generate tokens sequentially, so drafting cost increases as the number of speculative tokens increases. This limits how far the method can push throughput. DFlash replaces the autoregressive drafte
Boost Inference Performance up to 15x on NVIDIA Blackwell Using DFlash Speculative Decoding | NVIDIA Technical Blog…Initialize the agent The agent uses MiniMax M2.5 as its reasoning model. Use the publicly hosted endpoints or, for best performance, deploy the NVIDIA NIM locally. The rest of the deployment…
…Supporting LLMs, multimodal models, RAG, and agentic workflows, NeMo delivers scalable, production-ready AI applications. Integrated with NVIDIA accelerated computing and deployable via optimized microservices, it provides high performance, security, and portability…
…Quantum simulation becomes exponentially costly for classical processors requiring state-of-the-art accelerated computing to push the limits of what simulations can be performed. Enabling quantum accelerated workflows Mature quantum accelerated…
…Developing robust strategies to manage GPU memory efficiently, including seamless spilling of data beyond physical GPU limits to maintain performance and scale. GPU file readers and intelligent I/O prefetching: Plugging in…
…model language and perception rather than world dynamics. What a WAM changes A WAM overcomes that dynamics-modeling limit by building the policy on a video world model. Because the backbone models…
…His work focuses on enabling large-scale training and inference, optimizing performance across distributed systems, and building cloud-native architectures for high-performance AI workloads. Anurag’s interests include large-scale AI…
…energy, chips, infrastructure, models, and applications. NVIDIA DSX platform provides the complete playbook for designing, simulating, building, and operating AI factories, aligning every layer of the stack across compute, software, facilities, and…
…still depend on manual effort, keeping MTTR high and limiting how far operators can move toward truly autonomous operations. A telco reasoning model becomes the engine for an AI agent that can…
…all-to-all communication that would typically limit scale in distributed all-neighbors kNN workloads. This method can produce major end-to-end performance gains on very large datasets. How to configure…
…local model context protocol (MCP) configurations that often run outside of a sandbox context. These recommended controls further reduce the attack surface, making host enumeration and exploration more difficult, limiting risks posed…
