---
title: "NVIDIA"
description: "Technology company that designs powerful computer chips used for video games, data centers, and artificial intelligence."
---

# NVIDIA
> Technology company that designs powerful computer chips used for video games, data centers, and artificial intelligence.

## Articles

### [Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72](https://yomu.fyi/post/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reaso.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 12, 2026
- Type: Explainer

Alibaba’s Qwen3.8-2.4T-A95B (Qwen3.8-Max) is an open-weight 2.4T-parameter model with 95B activated per token, designed for reasoning and agentic workloads. Its fine-grained mixture-of-experts architecture combines full and linear attention, supporting context of up to one million tokens and output lengths up to 128K while using bounded recurrent state in linear-attention layers. NVIDIA describes multinode serving on the 72-GPU GB300 NVL72, where a 130 TB/s NVLink domain supports expert communication; in FP8, the model reportedly exceeds 4K tokens per second per GPU and 350 tokens per second per user without additional tuning. Developers can control reasoning depth with low, high, or xhigh modes, choose SGLang, vLLM, NVIDIA Dynamo, or NIM for serving, and use NeMo AutoModel for full SFT or LoRA post-training.


### [How to Choose Full-Stack Observability for NVIDIA AI Factories](https://yomu.fyi/post/how-to-choose-full-stack-observability-for-nvidia-ai-factories.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Jorge Cardoso
- Published: Aug 12, 2026
- Type: Explainer

AI infrastructure spans compute, networking, storage, orchestration, and applications, so a symptom at one layer may originate elsewhere. The post proposes selecting observability tools by failure domain, mapping components to telemetry sources, and reducing signals to a top-k alert set in a unified Prometheus/Grafana triage dashboard. For a DGX cluster using InfiniBand, BCM, and Slurm, it recommends Redfish/IPMI, DCGM, NVSM, UFM, and BCM, while deferring NetQ, NMX, Run:ai, and NIM until their corresponding capabilities are needed. The framework emphasizes DCGM for GPU metrics, NVSM for DGX system health, UFM for InfiniBand, and BCM for cluster and job aggregation, with specialized tools retaining low-level telemetry. An elevated InfiniBand bit error rate illustrates how retransmissions can slow one rank during NCCL all-reduce, causing synchronized training throughput to follow the slowest rank; alerts should connect concise signals to owners and remediation actions.


### [NVIDIA JetPack 7.2.1 Adds Agentic Video Skills and T3000 Emulation](https://yomu.fyi/post/nvidia-jetpack-7-2-1-adds-agentic-video-skills-and-t3000-emulation.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 11, 2026
- Type: Announcement

JetPack 7.2.1 adds support for PyNvVideoCodec 2.2, NVIDIA's Python library for hardware-accelerated video encoding and decoding on GPUs, alongside foundational agentic video skills for Jetson workflows. These skills inspect the live target, query codec capabilities, generate Video Codec SDK or PyNvVideoCodec recipes, execute runs, and return measurements, warnings, and reproducible evidence. PyNvVideoCodec keeps frames in GPU-resident device memory through DLPack and CUDA device buffers, with multi-mode frame sampling and a ThreadedDecoder that pre-decodes frames in a background thread. The release also enables emulation of recently announced Jetson T3000 performance on a Jetson T5000 module in the Jetson Thor AGX Developer Kit, while the video skills are limited to Video Codec SDK and PyNvVideoCodec stages.


### [NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents](https://yomu.fyi/post/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Aug 11, 2026
- Type: Announcement

NVIDIA Nemotron 3.5 Lightning is an open 30B mixture-of-experts model with 3B active parameters, designed for the high-volume execution layer of long-running autonomous agents. The post positions it alongside frontier reasoning models: larger systems handle planning and orchestration, while Lightning handles tool calls, result validation, subagent delegation, and other routine work. Its design combines harness-optimized training, multi-token prediction for speculative decoding, DFlash and DSpark draft models, and NVFP4 quantization alongside BF16. The model is customizable through LoRA or full SFT, reinforcement learning, and environment-based evaluations, with weights, training data, and recipes released under OpenMDW-1.1. The post reports up to 4x the output speed of similar-sized models, 86% accuracy on PinchBench, and completion of 10,000 tasks 30% faster than Qwen3.6 35B at similar accuracy, while NeMo Switchyard routes work among models.


### [Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard](https://yomu.fyi/post/route-ai-agent-workloads-across-models-with-nvidia-nemo-switchyard.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 11, 2026
- Type: Explainer

NVIDIA NeMo Switchyard addresses the problem of routing AI-agent work across models with different capabilities, costs, latency, and verbosity profiles rather than sending every request to one model. At runtime, its router evaluates a request, context, policies, and system signals, then directs work to a configured model pool, potentially routing individual agent steps and carrying session state when needed. The provider-agnostic switchyard-libsy SDK separates semantic model targets from provider endpoints, while a reference server accepts OpenAI, Anthropic, and Responses API requests and records routing decisions and outcomes. Available approaches include tuning-free LLM classifier, stage, and escalation routers, alongside tunable and custom algorithms. In a LangChain evaluation, escalation routing cut cost 74% versus a frontier-only baseline with a measured six-point accuracy trade-off, while Cognition’s staged routing reported near-frontier coding performance at approximately 28% lower mean cost.


### [Run Local Agentic AI Workflows with Meta’s Muse Glimmer on NVIDIA](https://yomu.fyi/post/run-local-agentic-ai-workflows-with-meta-s-muse-glimmer-on-nvidia.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 10, 2026
- Type: Announcement

Meta has released Muse Glimmer, a 30B open-weight dense model with a 120K+ context window aimed at local agentic AI workflows. The model uses a dense architecture that activates every parameter per token, avoiding routing overhead and targeting reliable instruction following, long-context coherence, and predictable latency for multi-step agent tasks. On NVIDIA Blackwell Ultra hardware, Muse Glimmer reports over 20K tokens/sec/GPU at BF16/NVF4 precision, with the full model fitting in a single GPU's VRAM. Deployment options span NVIDIA GeForce RTX 5090, DGX Spark, DGX Station, and Jetson edge devices, supported by vLLM, SGLang, and NVIDIA NIM containers. Fine-tuning is available through NVIDIA NeMo AutoModel for SFT and LoRA, with NeMo RL for reinforcement learning, and the NemoClaw harness enables long-running assistants in a sandboxed environment.


### [Beyond VLAs: How World Action Models Reshape Robot Manipulation](https://yomu.fyi/post/beyond-vlas-how-world-action-models-reshape-robot-manipulation.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 4, 2026
- Type: Explainer

Robot policies built on vision-language-action models often struggle with physical generalization because vision-language backbones describe scenes instead of predicting physical dynamics. World action models address this limitation by building policies on video world models that jointly predict actions and future world states. NVIDIA Cosmos 3 provides an open foundation using a Mixture-of-Transformers architecture where an autoregressive transformer guides a diffusion transformer to synthesize continuous modalities including video and robot actions. Specializing Cosmos 3 on the DROID platform produced policies capable of simultaneously outputting action trajectories and predicted video observations while boosting RoboLab success from 28.1% to 36.8%. These models support flexible deployment tiers, from workstation serving on RTX PRO 6000 GPUs to real-time 15 Hz on-device execution on NVIDIA Jetson Thor embedded hardware.


### [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super](https://yomu.fyi/post/generate-trajectories-reasoning-traces-and-auto-labels-with-nvidia-alp.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 4, 2026
- Type: Announcement

Autonomous vehicle development typically relies on disjoint models for trajectory generation, intent prediction, scene understanding, and data labeling. NVIDIA Alpamayo 2 Super addresses this fragmentation with an open 34-billion-parameter vision-language-action model that couples a 32-billion-parameter Cosmos 3 Super Reasoner with a 2-billion-parameter diffusion-based Action Expert. Processing surround-view video across up to seven cameras, the model simultaneously produces future vehicle trajectories, Chain-of-Causation reasoning traces, high-level meta-actions, and grounded scene question answers. In benchmark evaluations, the model achieved a 6.4-second minADE\_6 of 0.911 meters on the Physical AI AV Dataset and ranked first on LingoQA with a score of 79.2. Released under the OpenMDW-1.1 permissive license, the architecture serves as an offline policy teacher, evaluation critic, or auto-labeling engine that can be distilled for NVIDIA DRIVE AGX Thor deployment.


### [How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure](https://yomu.fyi/post/how-to-run-isolated-tenant-kubernetes-clusters-on-shared-gpu-infrastru.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Aug 3, 2026
- Type: Tutorial

Organizations sharing a single Kubernetes cluster across multiple teams frequently encounter conflicting custom resource definitions, overlapping role-based access control, and difficulties budgeting GPU capacity. To address these operational conflicts without provisioning separate hardware, administrators can deploy vCluster alongside KAI Scheduler on a shared host cluster. vCluster supplies each team with a dedicated, virtualized control plane featuring its own API server and permissions, while KAI Scheduler dynamically time-slices GPU resources through hierarchical queues. In a demonstration using an NVIDIA L40S GPU on MicroK8s, three separate tenant clusters successfully scheduled workloads with fractional allocations of 0.33 GPU each. The combination achieves tenant autonomy and visibility isolation while consolidating AI workloads on shared physical infrastructure.


### [NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage](https://yomu.fyi/post/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 3, 2026
- Type: Benchmark

Agentic AI architectures require storage systems to secure, compress, and validate massive streams of persistent data and key-value cache entries across concurrent reasoning loops. These tasks run primarily on CPUs and frequently create processing bottlenecks that prevent modern networks and flash storage from operating at full capacity. NVIDIA evaluated the Vera CPU within the BlueField-4 STX Storage Processor using a purpose-built in-memory microbenchmark suite running standard libraries like OpenSSL, Zstandard, and LZ4. The results demonstrated that Vera outperformed x86 processors across core storage primitives including encryption, decompression, Reed-Solomon recovery, and CRC32C integrity checking. For a sequential multi-stage pipeline executing compression followed by encryption, Vera achieved up to 3.21x higher throughput than the comparison x86 CPU.


### [Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference](https://yomu.fyi/post/co-designing-ai-model-attention-for-fast-interactive-long-context-infe.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jul 31, 2026
- Type: Explainer

As context lengths increase in long-context and agentic workloads, attention increasingly dominates GPU inference runtime. Dense attention performance depends on architectural parameters like group size, head dimension, and sequence length, alongside GPU parallelization strategies. Prefill remains compute-bound and scales quadratically with input sequence length, showing minimal runtime sensitivity to group size variations. Conversely, decode generation is memory-bound and scales linearly with KV cache length, where doubling group size roughly halves decode execution time. To maximize throughput and interactivity on NVIDIA hardware, model architects should push group sizes higher, select head dimensions of 128 or 256, reduce effective KV state, and keep tensor parallelism less than or equal to the number of KV heads.


### [NVIDIA Video Codec SDK 13.1: Zero-Copy Transcode, AV1 B-Frames, and Frame-Accurate Seek](https://yomu.fyi/post/nvidia-video-codec-sdk-13-1-zero-copy-transcode-av1-b-frames-and-frame.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Jul 31, 2026
- Type: Announcement

Modern video pipelines require higher efficiency and support for complex workloads across streaming, collaboration, and artificial intelligence media tools. NVIDIA released Video Codec SDK 13.1 to expand GPU-accelerated encoding, decoding, and transcoding capabilities. The release introduces Hierarchical Reference Mode for AV1 with up to 31 B-frames, combines UHQ tuning with iterative encoding, and extracts per-macroblock decode statistics for H.264 and HEVC without CPU overhead. It also implements frame-accurate, GOP-aware seeking through the NvVideoDecoder class and adds stereoscopic MV-HEVC decode metadata. To simplify setup across different environments, the update delivers an official multi-stage Docker container pinned to Ubuntu 22.04 LTS, CUDA 12.3.2, and Vulkan SDK.


### [Run High-Performance Core Math at Scale with NVIDIA nvmath-python](https://yomu.fyi/post/run-high-performance-core-math-at-scale-with-nvidia-nvmath-python.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jul 30, 2026
- Type: Announcement

NVIDIA released nvmath-python v1.0 to give Python users direct access to CUDA-X math libraries without requiring low-level C or C++ interfaces. The library acts as an abstraction layer over GPU engines like cuFFT and cuBLASLt, CPU backends like NVPL and Intel MKL, and distributed libraries such as cuBLASMp. It interfaces directly with NumPy, CuPy, and PyTorch tensors, structuring routines into generic interfaces for broad interoperability and specialized advanced modules for hardware-specific optimizations. Users can define custom sparse formats with Universal Sparse Tensors or invoke compiled device APIs directly inside custom numba-cuda kernels. Built-in logging captures execution space decisions and flags potentially expensive memory transfers across host and device environments.


### [Four Ways to Deploy More Secure AI Agents](https://yomu.fyi/post/four-ways-to-deploy-more-secure-ai-agents.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jul 30, 2026
- Type: Problem & solution

Connecting large language models to live tools and enterprise systems introduces severe security vulnerabilities across multiple deployment harnesses. Over six months of evaluations, the NVIDIA AI Red Team identified recurring failures in AI agents, including missing access controls, arbitrary code execution via shell or file-write tools, unrestricted network egress, and exposed plaintext credentials. Mitigations operating in the same control plane as the model, such as system prompts or LLM-as-a-judge review agents, consistently fail against adversarial manipulation. Securing agentic deployments instead requires deterministic, architectural controls enforced outside the model control plane. Effective defenses include authenticating users, isolating command execution inside hardened sandboxes, blocking network egress by default, and delivering ephemeral secrets via external managers.


### [NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure](https://yomu.fyi/post/nvidia-exemplar-cloud-lessons-for-unlocking-full-performance-on-ai-inf.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Jul 30, 2026
- Type: Problem & solution

Identical AI computing clusters can exhibit 8% to 12% training throughput deficits compared to NVIDIA reference architectures due to compounded configuration choices across kernels, hypervisors, BIOS, and NCCL settings. Profiling with Linux perf, turbostat, and NCCL tests uncovers performance degradation across multiple layers of the compute stack. In virtualized Grace deployments, enabling Arm SMMU Virtual Command Queue support eliminates invalidation serialization bottlenecks on memory-heavy workloads. On x86 systems, adjusting BIOS C-states to allow idle cores into C6 frees thermal headroom for active cores to achieve rated turbo frequencies, while cpuset isolation resolves NUMA contention. Further gains stem from tuning NCCL queue pairs per connection and properly bind-mounting topology files inside workload containers.


### [How to Self-Host a Validated AI Coding Assistant with NVIDIA NeMo Guardrails](https://yomu.fyi/post/how-to-self-host-a-validated-ai-coding-assistant-with-nvidia-nemo-guar.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jul 29, 2026
- Type: Tutorial

Deploying AI coding assistants in regulated or sovereign environments presents risks around data privacy, hallucinated dependencies, and auditability. This tutorial outlines a self-hosted architecture deploying StarCoder2-7B as an NVIDIA NIM endpoint served entirely from local GPUs. NVIDIA NeMo Guardrails sits as an OpenAI-compatible proxy between developer IDEs and the model to intercept and reject requests for restricted or human-only code paths. Downstream CI gates catch package hallucinations and verify changes, while commit trailers and a Prometheus and Grafana metrics loop monitor defect escape rates. Because the model remains distinct from the external policy and validation layers, teams can incrementally adopt components or upgrade models without rearchitecting the pipeline.


### [Developing Healthcare Robotics with GPU-Native Medical Physics Simulation](https://yomu.fyi/post/developing-healthcare-robotics-with-gpu-native-medical-physics-simulat.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jul 28, 2026
- Type: Announcement

Healthcare robotics faces data scarcity, long-tail clinical scenarios, and multi-year development cycles due to reliance on physical phantoms and animal studies. To resolve this, NVIDIA Medical Physics Simulation introduces GPU-native simulation within NVIDIA Isaac for Healthcare, integrating classical physics solvers and generative world models on a single GPU. The Endoluminal Simulation Module models flexible instruments as Cosserat rods solved via globally coupled extended position-based dynamics, maintaining zero-copy PyTorch interoperability via NVIDIA Warp. Concurrently, Cosmos-H-Dreams leverages distilled video diffusion models via the FlashDreams runtime to stream action-conditioned surgical video at over 30 frames per second. This dual-track architecture enables synchronized physics, synthetic fluoroscopic rendering, and scaled reinforcement learning policies across hundreds of parallel GPU environments.


### [NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning](https://yomu.fyi/post/nvidia-ising-enables-fully-automated-quantum-computer-calibration-with.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jul 27, 2026
- Type: Announcement

NVIDIA Ising Calibration is an open-source vision language model designed to interpret quantum processor diagnostic outputs and recommend tuning steps. The newly released Ising Calibration 1.5 features a 31-billion-parameter architecture that is 11.4% smaller at BF16 precision than its predecessor. Trained on multi-modality partner data including superconducting qubits, quantum dots, ions, neutral atoms, and electrons on Helium, the model supports both zero-shot reasoning and in-context learning. Evaluated on the QCalEval benchmark, it scores 10% higher on average than comparable open models in zero-shot tasks and improves in-context learning performance by over 86% relative to the prior release. In addition to BF16 weights, the model offers an NVFP4 quantization option suitable for deployment on single consumer GPUs or NVIDIA DGX Spark hardware.


### [Six Agent Harness Capabilities for Higher Model Performance](https://yomu.fyi/post/six-agent-harness-capabilities-for-higher-model-performance.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jul 27, 2026
- Type: Announcement

NVIDIA Labs released NVIDIA Labs Object-Oriented Agents (NOOA), an open-source research preview treating AI agents as single Python classes. The harness implements six model-facing capabilities: typed inputs and outputs, passing live objects by reference, code-based actions, programmable loops, explicit object state, and model-callable harness APIs. Instead of context compaction, passing live references keeps transcripts append-only, preserving prompt cache validity across long sessions. NOOA also includes a curated SQLite-backed long-term memory system forming a typed knowledge graph. Across evaluations, NOOA achieved 82.2 percent on SWE-bench Verified with GPT-5.5 using roughly 1.1M tokens per task, 86.8 percent on CyberGym L1 without network access, and 85.1 percent mean RHAE on ARC-AGI-3 using GPT-5.6-sol at under twenty dollars per game.


### [NVIDIA Nemotron 3 Ultra Leads Open Models on Accuracy and Efficiency in Agentic RTL Coding](https://yomu.fyi/post/nvidia-nemotron-3-ultra-leads-open-models-on-accuracy-and-efficiency-i.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Nirmal Kumar Juluru
- Published: Jul 27, 2026
- Type: Benchmark

Register transfer level (RTL) development relies heavily on iterative verification and tool feedback, which poses challenges for standard code generation models. To address realistic hardware design tasks, the ACE-RTL agent pairs an iterative generate-test-reflect workflow with NVIDIA Nemotron 3 Ultra. The model utilizes a hybrid Mamba-Attention Mixture-of-Experts architecture trained on a synthetic dataset encompassing code generation, editing, and error debugging. Across the comprehensive Verilog design problems benchmark, ACE-RTL with Nemotron 3 Ultra attained a 97.1% average pass rate, outperforming GLM 5.2 and Kimi K2.6. Additionally, Nemotron 3 Ultra achieved its accuracy while consuming an average of 6,629 tokens per iteration, which is substantially lower than competing models.


[Newer posts](https://yomu.fyi/company/nvidia-developer-blog/page/4.md) · [Older posts](https://yomu.fyi/company/nvidia-developer-blog/page/6.md)