Artificial intelligence is moving beyond simple chatbots toward systems that can write code, analyze documents, use software tools, and complete complex tasks with less human intervention. NVIDIA is contributing to this development through its Nemotron family of AI models, designed to help developers build efficient, customizable AI applications and autonomous agents.
In October 2026, NVIDIA expanded the family with several Nemotron 3.5 models, including Nemotron 3.5 Lightning, Nemotron 3.5 Super VL, and Nemotron 3.5 Bolt. These models target different deployment requirements, from high-volume AI agents and multimodal reasoning to running AI locally on supported NVIDIA hardware.
But what exactly is NVIDIA Nemotron 3.5? Is it open source? Can you run it on a personal computer? What hardware does it require, and how can developers use it to build AI applications?
This guide explains the technology, examines the specifications of the major models, discusses their availability and hardware requirements, and explores practical applications for developers, researchers, businesses, and AI enthusiasts.
What Is NVIDIA Nemotron 3.5?
NVIDIA Nemotron 3.5 is part of NVIDIA's family of AI models designed for reasoning, software development, long-context understanding, and agentic AI applications.
Rather than offering one model for every possible task, NVIDIA provides different model sizes and architectures to accommodate different workloads.
Some models are designed to perform specialized tasks quickly and repeatedly. Others combine language understanding with visual reasoning or provide a balance between model intelligence and the memory available on local hardware.
The main models highlighted in NVIDIA's October 2026 announcements include:
Nemotron 3.5 Lightning: A text-based model designed for efficient, high-volume agentic workloads.
Nemotron 3.5 Super VL: A multimodal model that can process text, images, and video.
Nemotron 3.5 Bolt 74B-A9B 24GB: A compressed model designed to fit within a 24GB memory budget for supported local deployments.
Nemotron 3.5 Bolt E4B 4GB: A smaller model announced for compact conversational AI applications, with release scheduled for October 23, 2026.
These are not interchangeable versions of the same model. Each addresses a different balance of reasoning capability, computational requirements, speed, and deployment flexibility.
NVIDIA's official announcement describes the broader goal as making capable AI agents more efficient and easier to deploy across data centers, workstations, and local systems.
For the latest model information, see the official NVIDIA Nemotron models and resources.
Key Features of NVIDIA Nemotron 3.5
Several technical features distinguish the Nemotron 3.5 family from conventional large language models.
1. Hybrid Mamba-Transformer Architecture
One of the most important features of Nemotron 3.5 Lightning and Super VL is their hybrid Mamba-Transformer architecture.
Traditional Transformer models use attention mechanisms to help determine how different parts of an input relate to one another. Although this approach is powerful, processing long sequences can require substantial computational resources and memory.
Mamba-style architectures use state-space mechanisms to process sequences differently, offering potential efficiency advantages for long inputs.
NVIDIA combines Mamba-2 components, Transformer attention layers, and Mixture-of-Experts components in its hybrid architecture.
The objective is to combine effective information processing with more efficient computation.
This architecture is particularly relevant when an AI application must process lengthy documents, maintain context across extended interactions, or repeatedly execute tasks within an agent workflow.
However, hybrid architectures do not eliminate hardware requirements. Actual performance depends on the model, input length, inference software, memory configuration, and workload.
2. Mixture-of-Experts Technology
Nemotron 3.5 uses a Mixture-of-Experts, or MoE, architecture in its major Lightning and Super VL models.
In a conventional dense model, most of the model's parameters participate in processing each token. An MoE model instead uses a routing mechanism to select particular expert components for a given input.
This allows the model to have a large total parameter count without activating every parameter for every token.
Consider the two principal models:
| Model | Total parameters | Active parameters |
|---|---|---|
| Nemotron 3.5 Lightning | 30 billion | 3 billion |
| Nemotron 3.5 Super VL | 120 billion | 12 billion |
The distinction between total and active parameters is important.
Lightning contains 30 billion parameters in total, but approximately 3 billion are active for a given token. Super VL contains 120 billion parameters in total, with approximately 12 billion active per token.
This can reduce computation compared with a dense model of a similar total size.
However, active parameters are not the same as the total memory required to load a model. The model's weights, cache, runtime, and other components still consume memory. Hardware planning must account for these requirements.
3. Long-Context Processing
Nemotron 3.5 Lightning and Super VL support context lengths of up to approximately one million tokens.
A context window determines how much information a model can process within a supported interaction or request.
A large context window can be useful when working with:
Large software repositories.
Extensive technical documentation.
Long research reports.
Multiple related documents.
Large collections of project notes.
Complex agent workflows involving substantial background information.
For example, a software engineer could build an application that supplies relevant code, documentation, error logs, and instructions to a model in a supported workflow.
A researcher might use a similar approach to analyze a collection of papers or organize information across lengthy technical reports.
Nevertheless, a one-million-token context window does not guarantee perfect recall or unlimited output. The practical limits depend on the serving configuration, available memory, context-management strategy, and inference software.
Long-context requests can also consume substantial computing resources.
4. Native Speculative Decoding
NVIDIA uses Multi-Token Prediction, or MTP, to help accelerate generation in Nemotron 3.5 Lightning and Super VL.
In ordinary autoregressive generation, a language model generally produces one token at a time.
Speculative decoding uses additional predictions to propose several future tokens, which can then be verified by the main model. When the proposed tokens are accepted, generation can proceed more quickly.
The Nemotron 3.5 models include native MTP components designed to support this process.
NVIDIA also provides specialized speculative-decoding techniques for the models, including DFlash and DSpark for Lightning and LiLiCorr for Super VL.
The purpose is to improve inference efficiency, particularly in workloads where generation speed and throughput matter.
For developers, this can be useful when deploying AI agents that repeatedly generate short responses, call tools, write code, or perform other execution steps.
Actual speed improvements depend on the hardware, model precision, serving configuration, and workload. Speculative decoding does not guarantee the same speedup in every application.
5. Advanced Coding and Agentic Workflows
Nemotron 3.5 is designed for applications that require more than a single generated response.
Agentic AI systems can divide a task into smaller steps, use tools, inspect results, and continue working toward an objective.
For example, a coding agent might:
Examine an existing software project.
Identify the files relevant to a requested feature.
Develop an implementation plan.
Modify the necessary files.
Run available tests.
Inspect errors and make corrections.
Report the final changes.
Nemotron 3.5 Lightning is designed to perform specialized tasks efficiently within these kinds of workflows.
Super VL extends the possibilities by combining language and visual understanding, enabling applications that need to interpret visual information and interact with supported tools.
These capabilities can help developers build AI agents for software engineering, research, document processing, and business automation.
However, the model does not automatically have permission to access every application or execute every action. Developers must provide suitable tools, permissions, and safeguards.
6. Multimodal Understanding
Nemotron 3.5 Super VL is the multimodal member of the family discussed in NVIDIA's October 15 announcement.
It supports input involving text, images, and video, with text as its documented output type.
This allows developers to create applications that combine written instructions with visual information.
Possible examples include:
Understanding screenshots.
Analyzing images alongside written descriptions.
Extracting information from visual documents.
Summarizing video content.
Building agents that interpret visual interfaces.
Supporting visual inspection and document-analysis workflows.
For example, a developer could build a system that receives a screenshot of a software interface and a written instruction asking it to identify a visible problem.
The model could analyze the supplied information and generate a textual explanation or structured response.
The availability of these capabilities depends on the model and deployment configuration. Nemotron 3.5 Lightning is a text model and should not be confused with the multimodal Super VL model.
7. Open Weights, Data, and Training Resources
NVIDIA describes its Nemotron family as a collection of open models that provides access to model weights, training data, and training recipes.
This can give researchers and developers more opportunities to inspect, evaluate, customize, and deploy models than they would have with an API-only service.
Depending on the model and release, available resources may include:
Base-model checkpoints.
Post-trained model weights.
Quantized checkpoints.
Training datasets.
Post-training datasets.
Evaluation recipes.
Technical reports.
Fine-tuning instructions.
Inference optimization resources.
These resources are useful for researchers studying model behavior and developers adapting a model to a particular domain.
Important licensing distinction: NVIDIA's use of terms such as "open models" does not automatically mean that every Nemotron release qualifies as open source under every definition. Model access is governed by its applicable license. Review the specific license and its commercial-use, redistribution, and other conditions before deployment.
NVIDIA Nemotron 3.5 Model Comparison
Choosing the right model requires understanding how the main variants differ.
Nemotron 3.5 Lightning
Nemotron 3.5 Lightning is a 30-billion-parameter model with approximately 3 billion active parameters.
It is designed for efficient execution of specialized tasks within larger agentic systems.
Potential uses include:
Coding assistants.
Tool-calling agents.
Chatbots.
Retrieval-augmented generation systems.
High-volume task execution.
Specialized AI workflows.
Its text-focused design makes it suitable for applications that do not require the model itself to interpret images or video.
Lightning was announced on August 11, 2026, and is available through NVIDIA's model ecosystem.
Nemotron 3.5 Super VL
Super VL is a 120-billion-parameter multimodal model with approximately 12 billion active parameters.
It is designed for demanding agentic workflows that combine reasoning, tool use, coding, and visual understanding.
Potential applications include:
Multimodal AI assistants.
Video and image analysis.
Visual software agents.
Document understanding.
Complex coding workflows.
Enterprise AI applications.
Its multimodal capabilities make it more suitable than Lightning for tasks involving images and video.
However, its total parameter count and deployment requirements are substantially larger.
Nemotron 3.5 Bolt 74B-A9B 24GB
Nemotron 3.5 Bolt is designed to bring more capable agentic AI to supported local systems with a limited memory budget.
The 74B-A9B variant has approximately 74 billion total parameters and 9 billion active parameters. NVIDIA designed it to fit within a 24GB memory budget using aggressive compression and quantization.
Its intended uses include everyday local agent tasks on supported NVIDIA RTX systems and DGX Spark.
This model is particularly interesting for users who want to run an AI model locally rather than depend entirely on a remote inference service.
However, the 24GB target is a model-design and deployment target, not a guarantee that every computer with a nominally 24GB GPU will run every configuration equally well. Runtime support, memory overhead, context length, and other applications affect feasibility.
Nemotron 3.5 Bolt E4B 4GB
NVIDIA has also announced Nemotron 3.5 Bolt E4B 4GB, intended for compact conversational AI deployments.
As of October 19, 2026, NVIDIA's announcement schedules its release for October 23, 2026.
It should therefore be treated as announced but not yet generally available on the date of this article.
Its target is a much smaller memory footprint, making it potentially attractive for compact local applications and supported edge systems.
Before attempting to deploy it, check NVIDIA's release notes for the final model specification, supported devices, and software requirements.
Quick Comparison Table
| Specification | Lightning | Super VL | Bolt 74B-A9B 24GB | Bolt E4B 4GB |
|---|---|---|---|---|
| Total parameters | 30B | 120B | 74B | Compact model; check final release documentation |
| Active parameters | 3B | 12B | 9B | Check final release documentation |
| Primary input | Text | Text, images, video | Check the specific release documentation | Compact conversational workloads |
| Main purpose | Efficient agent execution | Multimodal reasoning and agents | Local agentic AI | Compact conversational AI |
| Context length | Up to 1M tokens | Up to 1M tokens | Check model documentation | Check final release documentation |
| Memory target | Depends on precision and runtime | High-memory deployment; depends on precision | Designed for a 24GB memory budget | Designed around a 4GB memory target |
| Availability on October 19, 2026 | Available | Available | Announced and available | Scheduled for October 23, 2026 |
The table deliberately distinguishes confirmed specifications from information that should be verified in the final model card. Hardware and memory targets should not be interpreted as universal guarantees of performance.
Is NVIDIA Nemotron 3.5 Open Source?
NVIDIA promotes Nemotron as an open-model family with model weights and other development resources available for inspection and use.
This is important because many commercial AI systems can only be accessed through a hosted service. Open-weight models offer developers the opportunity to download a checkpoint and run it on supported infrastructure.
Potential benefits include:
Greater deployment control: Developers can run supported models on their own infrastructure.
Customization: Teams can adapt models to specific tasks through supported fine-tuning methods.
Research access: Researchers can examine model documentation, training resources, and evaluation recipes.
Flexible deployment: Depending on the model, developers may deploy locally, on workstations, or in data centers.
Reduced dependence on a single hosted endpoint: A self-hosted deployment can provide greater operational control, although it also creates responsibility for infrastructure, maintenance, and security.
However, the term "open source" should be used carefully.
Nemotron model releases are governed by their applicable licenses. Access to model weights and training resources does not automatically mean that every component has an unrestricted license or that all dependencies are open source.
Before using a model in a commercial application, check the relevant model card and license terms.
The official NVIDIA Nemotron developer page provides links to model resources and deployment options.
NVIDIA Nemotron 3.5 Hardware Requirements
Hardware requirements are among the most important factors when deciding whether to use Nemotron 3.5.
A model can be downloadable without being practical to run on a particular computer.
The required hardware depends on the model's size, numerical precision, context length, inference engine, and deployment configuration.
1. Hardware for Nemotron 3.5 Lightning
Lightning is smaller than Super VL in total parameter count, but its memory requirements are still substantial.
NVIDIA's deployment documentation provides the following approximate memory requirements for running Lightning on DGX Spark at a single tensor-parallel rank, before allowing additional memory for the key-value cache:
| Model precision | Approximate unified memory required |
|---|---|
| NVFP4 | 30GB |
| W4A16 | 32GB |
| BF16 | 66GB |
These figures illustrate why precision matters.
Quantization reduces the number of bits used to represent model weights. A lower-precision model can often occupy less memory than a BF16 version.
However, the figures above are specific to the documented deployment configuration. They should not be treated as universal minimums for every inference engine or GPU.
A developer using a different GPU or serving framework should consult the corresponding hardware support matrix.
2. Hardware for Nemotron 3.5 Super VL
Super VL is a much larger model, and its deployment requirements vary significantly according to precision.
NVIDIA's model artifacts documentation lists the following hardware profiles for its released configurations:
| Hardware | NVFP4 configuration | BF16 configuration |
|---|---|---|
| NVIDIA H200 | 1 GPU | 4 GPUs |
| NVIDIA B200 | 1 GPU | 2 GPUs |
| NVIDIA GB200 | 1 GPU | 2 GPUs |
| NVIDIA B300 | 1 GPU | 2 GPUs |
| NVIDIA GB300 | 1 GPU | 2 GPUs |
| NVIDIA H100 SXM 80GB | 2 GPUs | 8 GPUs |
| NVIDIA A100 SXM4 80GB | 2 GPUs | Not available in the listed matrix |
| DGX Spark | 1 system | Not available in the listed matrix |
These are the documented GPU profiles for the listed NVIDIA NIM model artifacts, not a guarantee that every software configuration or workload will fit identically.
NVIDIA also lists support for selected Blackwell RTX Pro hardware and DGX Station configurations.
Super VL is therefore better suited to supported high-memory GPU systems or specialized local AI platforms than to an ordinary laptop.
3. Hardware for Nemotron 3.5 Bolt
Bolt 74B-A9B 24GB was designed to fit within a 24GB memory budget.
This makes it an interesting option for supported NVIDIA RTX hardware and DGX Spark deployments.
Its aggressive compression is central to its design. A model with 74 billion total parameters would ordinarily require substantially more memory if stored in a conventional higher-precision representation.
Nevertheless, users should check the exact supported GPU, driver, runtime, and memory configuration before downloading and deploying the model.
A GPU's advertised memory capacity does not guarantee that a model will run at a desired context length or speed.
4. Can You Run Nemotron 3.5 on a Normal Laptop?
The answer depends on which model you choose and what hardware your laptop has.
A typical laptop without a supported NVIDIA GPU is not a suitable target for the larger models' optimized NVIDIA deployment configurations.
The main considerations are:
Available GPU or unified memory.
Supported GPU architecture.
Model precision.
Inference software compatibility.
Required context length.
Operating-system support.
Expected generation speed.
For a laptop user without sufficient local hardware, a hosted NVIDIA inference endpoint may be a more practical starting point.
If local execution is essential, choose a model specifically designed for the available hardware and verify its compatibility before installing it.
How to Use NVIDIA Nemotron 3.5
There are several ways to experiment with Nemotron 3.5, depending on whether you prefer a hosted service or a self-hosted deployment.
Method 1: Use NVIDIA's Hosted Inference Service
For many beginners, using a hosted endpoint is the easiest way to get started.
You do not need to download the entire model or configure a multi-GPU server just to test its responses.
Follow these steps:
Step 1: Open NVIDIA's model catalog.
Visit NVIDIA Build and search for Nemotron 3.5 Lightning or Super VL.
Step 2: Select a model.
Choose the model that matches your intended task. Use Lightning for text-based agentic workloads or Super VL when the application requires visual understanding.
Step 3: Open the model playground or API instructions.
Review the current instructions for accessing the model, authentication requirements, and usage limits.
Step 4: Test a simple prompt.
For example, you could ask the model to explain a Python function, summarize a short report, or propose a plan for a small coding project.
Step 5: Evaluate the result.
Check whether the model follows your instructions, provides accurate information, and produces the required format.
Step 6: Integrate the API if needed.
If the test is successful, follow the current API documentation to integrate the model into your application.
Hosted access can change over time, and some endpoints may have rate limits, account requirements, or usage charges. Check the current terms before using an endpoint in a production application.
Method 2: Download the Model Weights
If you want to run a supported Nemotron model on your own hardware, you can explore the released checkpoints on NVIDIA's official model repositories and Hugging Face.
Begin with the NVIDIA Nemotron developer resources.
The general process is:
Identify the exact model variant you want to use.
Read its model card and license.
Check the supported hardware and precision.
Install a compatible inference framework.
Download the appropriate checkpoint.
Configure the runtime and memory settings.
Run a small test before increasing the context length or workload.
The choice of checkpoint matters. A BF16 model may need substantially more memory than an NVFP4 version of the same model.
Do not assume that a command or installation procedure for Lightning will work unchanged for Super VL or Bolt.
Method 3: Run Nemotron with an Inference Framework
NVIDIA lists open inference frameworks such as vLLM, SGLang, Ollama, and llama.cpp among the options used across the broader Nemotron ecosystem.
However, support is model-specific. Not every framework supports every Nemotron 3.5 architecture, quantization format, or multimodal feature.
Before installing a framework, check its current documentation and the relevant NVIDIA model card.
For production deployments, NVIDIA also offers NIM microservices, which provide packaged inference services for supported models and hardware.
A successful deployment typically requires:
A compatible model checkpoint.
Supported GPU hardware.
Suitable GPU drivers and software libraries.
A compatible inference runtime.
Enough memory for model weights and runtime overhead.
Configuration appropriate for the intended context length and concurrency.
For example, a developer might deploy Lightning behind an application programming interface and connect it to an agent framework. The application could then use the model for selected subtasks while a separate system manages tools and permissions.
Method 4: Customize a Nemotron Model
Developers who need domain-specific behavior may be able to fine-tune a supported Nemotron checkpoint.
Potential customization methods include supervised fine-tuning and low-rank adaptation, commonly known as LoRA.
NVIDIA's NeMo ecosystem provides resources for training and customization, although the exact workflow depends on the model.
For example, a company might adapt a model to follow its preferred response format or perform a specialized task using an appropriate training dataset.
Before fine-tuning, consider:
Whether the base model already meets the requirement.
The quality and permissions associated with the training data.
The computational resources needed for training.
How the customized model will be evaluated.
Whether the resulting model can be redistributed under the applicable license.
Fine-tuning should be evaluated using representative data and held-out tests. A model that performs well on its training examples may still fail on unfamiliar inputs.
Practical Applications of NVIDIA Nemotron 3.5
Nemotron 3.5 offers several possibilities for developers and organizations.
1. Building AI Coding Assistants
Developers can use Lightning or Super VL in supported coding-agent workflows.
Potential tasks include:
Explaining code.
Generating functions.
Suggesting code changes.
Reviewing error messages.
Creating tests.
Assisting with repository-level development.
A coding agent should still run tests and review generated changes before modifying important production systems.
2. Creating Autonomous AI Agents
Agentic applications combine models with tools, memory systems, application logic, and task-management components.
Lightning is designed to handle specialized execution tasks within these systems.
For example, an agent might use one model for complex planning and Lightning for repeated, well-defined subtasks. This approach can help developers balance performance, latency, and cost.
The overall system still needs appropriate orchestration, error handling, and permission controls.
3. Developing Document-Analysis Systems
Nemotron models can be used as part of systems that process documents and generate summaries or structured information.
Potential applications include:
Summarizing reports.
Extracting key information.
Organizing technical documentation.
Answering questions about a document collection.
Supporting internal knowledge systems.
For document collections, developers may combine a model with retrieval-augmented generation, or RAG. This approach retrieves relevant passages from an external knowledge base and supplies them to the model.
RAG can help keep responses connected to source material, but it does not eliminate incorrect answers. The application should preserve citations or document references where appropriate.
4. Building Multimodal AI Applications
Super VL enables developers to explore applications involving text, images, and video.
For example, a business might build a tool that accepts visual documents and written questions, or a system that analyzes selected video frames.
Other possibilities include visual quality inspection, screenshot analysis, and interfaces that combine visual evidence with textual reasoning.
These are potential application patterns, not guarantees that the model will perform every task accurately without additional engineering.
5. Deploying Local AI Assistants
Bolt 74B-A9B 24GB is designed for local agentic workloads on supported NVIDIA systems.
Running a model locally may help organizations maintain greater control over their deployment environment and reduce dependence on a hosted inference endpoint.
However, local deployment transfers more responsibility to the operator.
You must manage software updates, access control, hardware reliability, monitoring, and model performance.
Local execution also does not automatically guarantee privacy. Data can still be transmitted by surrounding applications or integrations if they are configured to do so.
6. Supporting Academic Research
Researchers can investigate Nemotron models for tasks involving language processing, code generation, reasoning, and AI-agent design.
The availability of technical reports, model weights, and evaluation resources can be particularly useful for reproducibility and comparative experiments.
Researchers should document:
The exact model checkpoint.
The software and hardware configuration.
Prompt and inference settings.
Training or fine-tuning data.
Evaluation datasets.
The methods used to calculate performance metrics.
This information makes it easier for other researchers to reproduce and assess the results.
NVIDIA Nemotron 3.5 vs. Other AI Models
Nemotron competes in a broader market that includes open-weight models and proprietary AI services.
The best model depends on the application rather than on a single overall ranking.
Nemotron vs. OpenAI Models
OpenAI offers AI models through ChatGPT and developer APIs.
Nemotron's major advantage for certain developers is its open-model approach, which can allow supported checkpoints to be deployed and customized on the user's own infrastructure.
A hosted OpenAI model may be more convenient for users who do not want to manage GPU hardware or inference software.
The right choice depends on the task, cost, privacy requirements, and available deployment resources.
Nemotron vs. Google Gemini
Google's Gemini models support a range of tasks, including reasoning, coding, and multimodal processing.
Nemotron 3.5 Super VL is also designed for multimodal reasoning and agentic workflows, while Lightning focuses on efficient text-based task execution.
Developers should compare the exact model versions and test them using the same representative tasks.
Nemotron vs. Meta's Llama Models
Meta's Llama family includes models that developers can use to build AI applications.
Nemotron's hybrid architecture, speculative-decoding techniques, and NVIDIA-oriented deployment resources may make it attractive for certain GPU-accelerated agentic workloads.
Llama may be preferable in other situations depending on model size, community tooling, fine-tuning requirements, or licensing.
Neither family is universally superior. The choice should reflect the actual needs of the project.
Advantages and Limitations of NVIDIA Nemotron 3.5
Advantages
Multiple deployment options: The family includes models targeting specialized agent tasks, multimodal reasoning, and local execution.
Efficient architecture: Hybrid Mamba-Transformer and MoE designs can reduce computational demands relative to some alternative architectures.
Long context: Lightning and Super VL support contexts of up to one million tokens.
Customization opportunities: Released model weights and development resources support experimentation and customization under the applicable terms.
Agentic capabilities: The models are designed for workflows involving tools, coding, and multiple steps.
Quantized checkpoints: Lower-precision versions can reduce memory requirements for supported deployments.
Limitations
Hardware requirements remain substantial: Large models still need significant memory and suitable inference software.
Compatibility varies: Not every model variant works with every inference framework or GPU.
Open weights do not mean unrestricted use: Developers must follow the relevant model license.
AI-generated output can be incorrect: Code, analysis, and factual responses require appropriate verification.
Deployment requires technical work: Self-hosting introduces responsibilities for software configuration, security, monitoring, and updates.
Performance depends on the workload: Benchmarks and speed claims may not predict results in a different environment.
Is NVIDIA Nemotron 3.5 Worth Trying?
NVIDIA Nemotron 3.5 is worth exploring if you want to build AI applications with more control over model deployment, customization, and hardware selection.
Lightning is particularly relevant for developers building efficient text-based agents. Super VL is a stronger candidate when visual understanding and multimodal workflows are required. Bolt 74B-A9B 24GB is designed for users interested in running more capable agentic AI on supported local hardware.
The most suitable model depends on your available GPU memory, desired context length, software stack, and application requirements.
If you are a beginner, start with a hosted endpoint rather than buying hardware immediately. Test the model on a small task, measure its quality and latency, and estimate the cost of your expected usage.
If you are an experienced developer or researcher, compare the official model cards and technical reports before choosing a checkpoint for local deployment or fine-tuning.
The key is to select a model based on evidence from your own use case rather than assuming that a larger model or a higher benchmark score will automatically produce the best application.
Frequently Asked Questions (FAQ)
1. What is NVIDIA Nemotron 3.5?
NVIDIA Nemotron 3.5 is a family of AI models designed for reasoning, coding, long-context processing, multimodal understanding, and agentic AI applications. The family includes models with different sizes and deployment targets.
2. Is NVIDIA Nemotron 3.5 open source?
NVIDIA describes Nemotron as an open-model family and releases model weights and other resources for supported variants. However, developers should check the specific license before commercial use, redistribution, or customization. The term "open model" does not automatically mean every component has an unrestricted open-source license.
3. What are the main NVIDIA Nemotron 3.5 models?
The principal models highlighted in the October 2026 announcements include Nemotron 3.5 Lightning, Nemotron 3.5 Super VL, and Nemotron 3.5 Bolt 74B-A9B 24GB. NVIDIA has also announced Nemotron 3.5 Bolt E4B 4GB, scheduled for October 23, 2026.
4. What is the difference between Nemotron 3.5 Lightning and Super VL?
Lightning is a text-based 30-billion-parameter model with approximately 3 billion active parameters, designed for efficient agentic task execution. Super VL is a 120-billion-parameter multimodal model with approximately 12 billion active parameters and support for text, images, and video.
5. How many parameters does Nemotron 3.5 Lightning have?
Nemotron 3.5 Lightning has 30 billion total parameters and approximately 3 billion active parameters per token.
6. How many parameters does Nemotron 3.5 Super VL have?
Nemotron 3.5 Super VL has 120 billion total parameters and approximately 12 billion active parameters per token.
7. Can NVIDIA Nemotron 3.5 run locally?
Yes, supported models can be deployed locally on compatible hardware. The required memory and GPU configuration depend on the model, precision, inference framework, and workload. Bolt 74B-A9B 24GB is specifically designed for supported local deployments within a 24GB memory budget.
8. Can I run Nemotron 3.5 on a laptop?
It depends on the model and laptop hardware. A typical laptop may not have sufficient memory or a compatible GPU for the larger models. Users with limited hardware can start with an NVIDIA-hosted inference endpoint or select a model designed for smaller systems.
9. What GPU do I need for Nemotron 3.5 Lightning?
Requirements depend on precision and runtime. NVIDIA's DGX Spark deployment guidance lists approximately 30GB of unified memory for NVFP4, 32GB for W4A16, and 66GB for BF16 at a single tensor-parallel rank, before additional key-value cache headroom. Other deployment configurations may differ.
10. What hardware does Nemotron 3.5 Super VL require?
The requirements depend on precision and the supported GPU profile. NVIDIA's documented NVFP4 configuration can run on a single H200, B200, GB200, B300, or GB300 GPU, while the listed BF16 configuration requires multiple GPUs. Consult the official hardware matrix before deployment.
11. What does NVFP4 mean?
NVFP4 is a low-precision numerical format used in NVIDIA's AI optimization ecosystem. It can reduce model-weight memory requirements compared with higher-precision representations, although actual memory usage also depends on runtime overhead and other components.
12. Does Nemotron 3.5 support images and video?
Nemotron 3.5 Super VL supports text, images, and video as inputs. Lightning is a text model, so developers should select Super VL when their application requires the documented multimodal capabilities.
13. Does Nemotron 3.5 support a one-million-token context window?
NVIDIA's technical report lists context lengths of up to one million tokens for Lightning and Super VL. Actual limits depend on the model configuration and serving environment.
14. Can I use Nemotron 3.5 for coding?
Yes. The family is designed to support coding and agentic workflows. Developers can use compatible models to assist with code generation, debugging, software testing, and repository-level tasks, but generated code should be reviewed and tested.
15. Can I fine-tune NVIDIA Nemotron 3.5?
Supported checkpoints can be customized using appropriate methods, such as supervised fine-tuning and LoRA. The available training methods and hardware requirements depend on the particular model and NVIDIA's supported tooling.
16. How do I access NVIDIA Nemotron 3.5?
You can start by exploring NVIDIA's model catalog and developer resources. Depending on the model, options may include hosted inference endpoints, downloadable checkpoints, and supported NVIDIA NIM deployments.
17. Is Nemotron 3.5 free to use?
Some model resources and hosted endpoints may be available without an initial charge, but access conditions vary. Hosted services may have rate limits or usage terms, while self-hosting requires hardware and operating costs. Check the current terms for the model and service you intend to use.
18. Is NVIDIA Nemotron 3.5 better than ChatGPT or Gemini?
There is no universal winner. Nemotron may be attractive when developers want supported open-model deployment and customization. Hosted ChatGPT or Gemini services may be more convenient for other tasks. Compare specific models on your own requirements, hardware, cost, and evaluation criteria.
19. What is Nemotron 3.5 Bolt 74B-A9B 24GB?
It is a compressed model with 74 billion total parameters and approximately 9 billion active parameters, designed to fit within a 24GB memory budget for supported local agentic workloads.
20. When will Nemotron 3.5 Bolt E4B 4GB be available?
NVIDIA's October 2026 announcement schedules the release for October 23, 2026. As of October 19, 2026, it should be treated as announced but not yet generally available.
Final Thoughts
NVIDIA Nemotron 3.5 expands the possibilities for developers who want to build AI agents, coding assistants, multimodal applications, and locally deployed AI systems.
Its different model variants serve different purposes. Lightning emphasizes efficient text-based task execution, Super VL combines reasoning with visual understanding, and Bolt 74B-A9B 24GB targets supported local systems with a limited memory budget.
The family's hybrid architecture, MoE design, long-context capabilities, and speculative decoding techniques are intended to improve the efficiency of complex AI workloads. Its released weights and development resources also provide opportunities for customization and research.
However, model selection should be guided by verified specifications, licensing, hardware compatibility, and real-world testing.
For developers, the best starting point is straightforward: choose a model that matches your task, test it through a hosted endpoint or compatible local setup, and measure its accuracy, speed, memory use, and cost before committing to a larger deployment.
As open-weight AI models continue to develop, NVIDIA Nemotron 3.5 is an important option for anyone exploring the next generation of AI-powered software and agentic systems.
Post a Comment