NVIDIA Nemotron 3 Ultra Now Available on Simplismart: Advancing Infrastructure for Agentic AI
Author : Simplismart Ai | Published On : 06 Jun 2026
The rise of agentic AI is transforming how organizations build and deploy intelligent systems. Unlike traditional AI applications that focus on answering queries or generating content, agentic AI systems can reason, plan, make decisions, and interact with tools to complete complex tasks autonomously. As these workloads become more sophisticated, the infrastructure required to support them must evolve as well.
Introducing NVIDIA Nemotron 3 Ultra
NVIDIA Nemotron 3 Ultra is NVIDIA's next-generation model built for advanced reasoning and long-context applications. The model is designed to support a wide range of agentic workloads, including:
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Complex enterprise workflows
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Coding agents
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Deep research systems
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Multi-step planning applications
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AI software agents
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Long-running autonomous tasks
Built on a sophisticated Mixture-of-Experts architecture, Nemotron 3 Ultra activates only a subset of its parameters during inference, allowing it to deliver strong performance while maintaining efficiency.
Some of its key architectural highlights include:
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253 billion total parameters
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49 billion active parameters during inference
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128 expert architecture
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Support for context windows up to 1 million tokens
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Advanced reasoning capabilities
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High-quality performance for coding and agentic workflows
These features make the model particularly attractive for organizations building production-ready AI agents that require extensive reasoning and long-term memory.
Why Agentic AI Workloads Are Different
Although large language models have become increasingly efficient, agentic AI introduces unique infrastructure challenges that traditional AI systems rarely encounter.
1. KV Cache Growth Creates Memory Challenges
One of the biggest differences in agentic AI systems is the continuous growth of context over time. Long-running agents accumulate information from:
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User interactions
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Tool calls
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Retrieved documents
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Intermediate reasoning steps
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External observations
Since Nemotron 3 Ultra supports context windows of up to one million tokens, memory consumption can increase rapidly if not managed effectively.
Without optimization, growing KV cache requirements can reduce system concurrency and increase latency, ultimately limiting the number of agents that can run simultaneously.
2. Reasoning Tokens Increase Latency
Modern reasoning models often generate substantial chains of reasoning before producing a final answer. Even when the final response is relatively short, thousands of intermediate reasoning tokens may be generated.
For real-time applications, this additional computation can negatively impact user experience by increasing response times. Managing and optimizing these reasoning processes is critical for maintaining interactive performance.
3. Agent Traffic Is Highly Variable
Traditional chatbot applications typically exhibit predictable traffic patterns. Agentic workloads behave differently.
An AI agent's workload may fluctuate dramatically depending on the task it is performing. Some requests may involve simple actions, while others require extensive reasoning, multiple tool interactions, or long planning sequences.
This variability creates infrastructure challenges that demand intelligent scheduling and resource management.
4. Performance Requires More Than Raw Compute
Simply adding more GPUs is not enough to solve the challenges associated with large-scale agent deployments. Production environments require infrastructure that can dynamically allocate resources, manage memory efficiently, and optimize request execution.
This is where Simplismart's inference platform provides a significant advantage.
Optimizing NVIDIA Nemotron 3 Ultra on Simplismart
Simplismart has developed a range of infrastructure-level optimizations specifically designed for agentic AI inference.
These enhancements focus on maximizing throughput while maintaining low latency and high resource efficiency.
According to benchmark results highlighted in the document, Simplismart achieved up to 50% higher throughput compared to TensorRT-LLM combined with MTP and NVFP4 configurations on NVIDIA B200 GPUs.
These gains are particularly valuable for organizations deploying large numbers of AI agents in production environments.
Advanced KV Cache Management
KV cache management plays a critical role in sustaining long-running agentic workloads.
Simplismart extends effective cache capacity through several optimization techniques, including:
KV Cache Offloading
The platform intelligently utilizes CPU memory to extend available cache resources, helping organizations support longer context windows without excessive GPU memory consumption.
Prefix Caching
Many agent requests share common context elements such as:
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System prompts
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Organizational instructions
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Shared knowledge bases
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Workflow definitions
By reusing these shared prefixes, Simplismart reduces redundant computation and improves overall efficiency.
Together, these optimizations enable better utilization of hardware resources while supporting more concurrent AI agents.
Accelerating Reasoning Workloads
Reasoning performance is another major focus area.
Rather than processing every request with identical computational intensity, Simplismart dynamically adapts execution strategies based on workload requirements.
For reasoning-intensive tasks, the platform can selectively adjust resource allocation to improve response times while maintaining output quality.
The result is a significant reduction in latency for interactive applications.
The document highlights that these optimizations can reduce latency by approximately 33% while maintaining strong reasoning performance.
This improvement is especially important for applications where responsiveness directly impacts user satisfaction.
Deployment Profiles for Different Agent Types
Not all AI agents have the same performance requirements.
Recognizing this reality, Simplismart supports different deployment configurations tailored to specific workload patterns.
Interactive Agents
Interactive agents prioritize:
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Faster response generation
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Lower latency
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Smooth user experiences
These applications benefit from configurations optimized for responsiveness and real-time interaction.
Background Agents
Background agents often focus on:
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Higher throughput
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Better GPU utilization
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Improved cost efficiency
These agents may process large volumes of tasks without strict latency requirements.
By matching infrastructure configurations to workload characteristics, organizations can achieve better performance and lower operational costs.
Fine-Grained Continuous Batching
One of the most impactful infrastructure innovations described in the document is fine-grained continuous batching.
As agent sessions evolve, requests frequently vary in size and complexity. Simplismart continuously adapts batch composition to maximize hardware utilization without compromising response quality.
This approach enables higher GPU efficiency across diverse workloads and helps organizations scale agent deployments more effectively.
The benchmark results demonstrate notable improvements in throughput while maintaining operational stability.
Benchmark Results
To validate these optimizations, Simplismart conducted benchmarking using NVIDIA B200 GPUs and NVIDIA Nemotron 3 Ultra.
The results demonstrated substantial gains in throughput compared with conventional deployment approaches.
The combination of:
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KV cache optimization
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Prefix caching
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Continuous batching
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Advanced scheduling
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Accelerated reasoning techniques
enabled significantly better hardware utilization and performance.
These findings reinforce the importance of infrastructure-level optimization when deploying advanced reasoning models at scale.
Deploying the Next Generation of AI Agents
As AI systems become more autonomous and capable, organizations need infrastructure that can support increasingly demanding workloads.
NVIDIA Nemotron 3 Ultra provides powerful reasoning capabilities and long-context support, making it well suited for production-scale agentic AI applications.
However, achieving maximum performance requires more than simply deploying the model. Efficient scheduling, intelligent memory management, and workload-aware optimization are essential for delivering real-world results.
Simplismart addresses these challenges with a purpose-built inference platform optimized specifically for agentic AI. By combining advanced infrastructure innovations with NVIDIA's latest reasoning model, organizations can deploy intelligent agents more efficiently, reduce latency, increase throughput, and maximize hardware utilization.
As agentic AI continues to reshape industries, platforms that optimize inference performance will play a crucial role in enabling scalable, cost-effective, and reliable AI deployments
