ثورة في تكاليف الذكاء الاصطناعي للشركات: نموذج Nemotron 3 Ultra مفتوح المصدر من إنفيديا يخفض النفقات 10 أضعاف

On Tuesday, July 8, 2026, Nvidia and LangChain jointly announced the NemoClaw for LangChain Deep Agents blueprint, a reference architecture for building enterprise AI agents powered by Nvidia's open-source Nemotron 3 Ultra model. The announcement highlighted massive inference cost reductions and enterprise-grade performance against closed rivals .

What Was Announced: NemoClaw for LangChain Deep Agents

The NemoClaw for LangChain Deep Agents blueprint provides enterprises with a reference architecture for building open agent systems using Nemotron 3 Ultra and Nvidia's OpenShell for runtime policy controls. It is designed for large customers needing customizable, production-ready AI agents with lower inference costs . The NemoClaw v0.0.76 release (July 8) made Nemotron 3 Ultra the managed Nvidia Endpoints default for Deep Agents Code .

Inference Cost Advantage: 10x Lower Than Closed Rivals

LangChain's Deep Agents harness, optimized for Nemotron 3 Ultra, delivers inference costs up to 10x lower per run compared to certain leading closed-source models . On Artificial Analysis, Nemotron 3 Ultra costs $0.58 per 1M tokens compared to Claude Sonnet 4.6 at $2.31 — a 75% cost reduction . Chinese financial media reported a claim that inference costs are ~90% lower than closed models .

Performance Against Closed Rivals on Enterprise Benchmarks

Nvidia states that on LangChain's Deep Agents benchmarks, Nemotron 3 Ultra matched the highest-scoring model on business tasks (parity in quality) . Key benchmark scores include:

• Artificial Analysis Intelligence Index: Scores 47.7–48 — highest among US open-weight models, ahead of Gemma 4 31B (39) and Nemotron 3 Super (36), but behind Chinese open-weight leader Kimi K2.6 (54) and proprietary frontier models like GPT-5.5 (60.2) and Claude Opus 4.7 (57) .
• GPQA Diamond: Scores 86.7%, competitive with top models .
• Agentic benchmarks: On HuggingFace's published data, Nemotron 3 Ultra scores 56.4 on Terminal Bench 2.1, 46.7 on GDPVal, and 70.7 on SWE-Bench Verified, competitive with MiniMax-2.7, GLM-5.1, and Kimi-K2.6 in agentic tasks .
• PinchBench Agent Productivity: Scores 90–91%, matching Kimi K2.6 and ahead of Qwen 3.5 .

Architectural Specifications

Specification	Detail
Total Parameters	550 billion (550B)
Active Parameters	55 billion (55B) per token
Architecture Type	Mixture-of-Experts (MoE) Hybrid Mamba-Attention (Mamba-Transformer)
Context Length	1 million tokens (1M)
Pre-training	20 trillion text tokens
Post-training	SFT + RL + Multi-tier On-Policy Distillation (MOPD)
License	OpenMDW 1.1 / Linux Foundation permissive license

Source of Performance Gains

Performance gains come from three core innovations :

• Hybrid Mamba-Attention MoE architecture: Combines Mamba 2 state-space layers with attention layers in an MoE routing framework, activating only 55B of 550B parameters per token for massive efficiency.
• Multi-tier On-Policy Distillation (MOPD): A post-training technique using reinforcement learning across interactive environments to distill reasoning capabilities.
• Sparse activation: The 10:1 total-to-active parameter ratio delivers frontier-level reasoning at a fraction of the compute cost of dense models.

Nemotron 3 Ultra achieves up to 5.9x higher inference throughput compared to GLM-5.1-754B-A40B on the 8K token input / 64K token output setting . On a pre-release DeepInfra endpoint, it served over 300 tokens per second .

LangChain Partnership & Quotes

LangChain provided Day 0 support for Nemotron 3 Ultra in its Deep Agents harness and is a member of the Nemotron Coalition . LangChain CEO Harrison Chase said enterprises can achieve strong performance at a fraction of the cost of closed models . Nvidia stated that LangChain's Deep Agents harness completed "more tasks at higher throughput using Nemotron 3 Ultra while delivering inference costs up to 10x lower than some leading closed models" .

Enterprise Adoption

Global partners EY and others such as Abridge, Amdocs, and Box are already engaged with the NemoClaw + Nemotron 3 Ultra stack for building enterprise AI agents . Cadence, Siemens, Synopsys, and Dassault Systèmes are also deploying autonomous AI engineers using Nvidia's NemoClaw blueprints .

Developer Access Options

Developers can access Nemotron 3 Ultra through :

• Hugging Face: Model weights at nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
• NVIDIA Endpoints / Nvidia NIM: Managed inference via Nvidia's cloud endpoints (default in NemoClaw)
• Ollama: Available at ollama.com/library/nemotron-3-ultra
• On-premise / GPU Cloud: Weights can be deployed on any GPU cloud instance
• Nvidia NemoClaw: Open-source blueprints deployable with a single command

Stock Market Reaction

Nvidia (NVDA) shares gained 4% on Wednesday, July 9, as the new benchmark results and NemoClaw announcement suggested Nemotron 3 Ultra could challenge closed models on both cost and enterprise performance . Bank of America reiterated its Buy rating on Nvidia stock following the announcement, citing the model's cost-performance advantage and enterprise momentum .

Timeline of Key Announcements

• June 1, 2026: Jensen Huang announced Nemotron 3 Ultra at Computex 2026 in Taipei
• June 4, 2026: Nvidia officially released Nemotron 3 Ultra as an open-source model with weights, training data, and recipes
• July 8, 2026 (Tuesday): LangChain and Nvidia announced the NemoClaw for LangChain Deep Agents blueprint, highlighting 10x lower inference costs and enterprise adoption by EY, Abridge, Amdocs, and Box
• July 9, 2026 (Wednesday): NVDA stock rose 4% on the news; Bank of America reiterated Buy