QUICK ANSWER
Nemotron 3 Ultra is available on Amazon SageMaker JumpStart from day zero - June 4, 2026. One-click deployment, NVFP4 precision (5x faster inference, 30% lower cost), 1M token context, and full SageMaker IAM/CloudWatch/VPC integration. Deploy via the SageMaker console model catalog or the SageMaker Python SDK. Best for: AWS teams that want Nemotron 3 Ultra in production without managing raw Hugging Face weights or NVIDIA NIM directly.
Why SageMaker JumpStart Is the Fastest Path to Production for AWS Teams
Deploying a 550B parameter model from raw Hugging Face weights requires significant infrastructure knowledge: selecting the right instance types, configuring tensor parallelism across multiple GPUs, setting up inference server software (vLLM, TGI, or TensorRT-LLM), and managing the operational overhead of a custom deployment. SageMaker JumpStart handles all of that. You select Nemotron 3 Ultra from the model catalog, choose from pre-defined deployment configurations optimized for the model's architecture, and click deploy. AWS provisions the infrastructure, loads the weights in NVFP4 precision, and returns an endpoint URL.
The NVFP4 optimization is the key efficiency advantage. NVFP4 is NVIDIA's 4-bit floating-point format, optimized for the Blackwell GPU architecture. Running Nemotron 3 Ultra in NVFP4 versus BF16 delivers approximately 5x faster inference throughput and 30% lower cost per inference. AWS confirmed these figures in the SageMaker JumpStart launch post. For teams running agentic workloads - where inference throughput determines how quickly an agent can complete a multi-step task - the 5x speed improvement is directly reflected in end-to-end task completion times.
How to Deploy Nemotron 3 Ultra on SageMaker JumpStart
Option 1: SageMaker Console (no-code)
Open the Amazon SageMaker console - navigate to JumpStart - search "Nemotron 3 Ultra" - select the model card - choose a deployment configuration - click Deploy. AWS provisions the endpoint automatically. No Python, no CLI required.
Option 2: SageMaker Python SDK
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="nvidia-nemotron3-ultra-550b")
predictor = model.deploy()
response = predictor.predict({
"inputs": "Explain mixture-of-experts architecture",
"parameters": {"max_new_tokens": 512}
})
Architecture Deep Dive - Hybrid Transformer-Mamba MoE
AWS's JumpStart launch blog provided the most detailed public description of Nemotron 3 Ultra's architecture to date. The model uses a hybrid Transformer-Mamba Mixture-of-Experts (MoE) architecture - combining three types of layers:
Mamba-2 state-space layers
Handle long-range sequence dependencies with constant memory cost - unlike attention which scales quadratically with sequence length. This is what enables the 1M token context window at practical inference speeds. At million-token contexts, pure Transformer attention would be computationally prohibitive; Mamba-2 layers handle the long-range context efficiently.
Sparse MoE routing layers
Route each token to the 55 billion most relevant parameters out of 550 billion total, keeping active compute fixed regardless of total model size. This is what delivers frontier intelligence at a fraction of the compute cost of a dense model with equivalent total parameters.
Selective Transformer attention blocks
Standard Transformer attention applied selectively at specific positions where precise local attention is needed - typically for tasks requiring immediate context integration that Mamba-2 handles less precisely.
The practical implication for agentic workloads: agents do not just answer once. They plan, execute tool calls, observe results, revise plans, and loop. The MoE architecture keeps throughput high even at million-token context lengths - meaning Nemotron 3 Ultra can sustain planning and tool-calling loops across hundreds of turns while maintaining coherence and managing cost. AWS specifically highlighted agent orchestrators, coding agents across large repositories, deep research synthesis, and complex enterprise workflow automation as the primary use cases.
SageMaker JumpStart vs Hugging Face vs NVIDIA NIM - Which to Use
| Platform |
Best For |
Setup Effort |
Cost Model |
| SageMaker JumpStart |
AWS teams; production; enterprise governance |
One click |
EC2 instance hours + data transfer |
| NVIDIA NIM (build.nvidia.com) |
API access without own infrastructure |
Low (API key + URL) |
Per token (pay as you go) |
| Hugging Face (weights) |
Custom deployments; fine-tuning; research |
High (custom infra required) |
Your own GPU cluster cost |
| OpenRouter |
Multi-model routing; existing OpenRouter users |
Low (model name change) |
Per token (OpenRouter rates) |
Frequently Asked Questions
What EC2 instance types are required for Nemotron 3 Ultra on SageMaker?
AWS has not published the exact required instance types in the public launch blog. Given the model's 550B parameter count and NVFP4 optimization for Blackwell GPUs, expect p5.48xlarge (8x H100) or p5e.48xlarge (8x H200) instances at minimum for practical throughput. Check the SageMaker JumpStart model card for the specific supported instance configurations after navigating to the model in the console.
What is NVFP4 and why does it give 5x faster inference?
NVFP4 is NVIDIA's 4-bit floating-point precision format, optimized for the Blackwell GPU architecture's tensor cores. Running a model in 4-bit precision rather than 16-bit (BF16) reduces memory bandwidth requirements and increases throughput substantially. The 5x figure reflects NVFP4 vs BF16 on Blackwell hardware specifically - the combination of a hardware format designed for the GPU's compute units and a model large enough that memory bandwidth is the primary bottleneck produces outsized throughput gains.
Is Nemotron 3 Ultra available on Amazon Bedrock as well?
The June 4 launch announcement specifically confirmed SageMaker JumpStart availability. Amazon Bedrock availability has not been confirmed in the initial launch materials. Previous Nemotron 3 tiers (Nano, Super) are available on Bedrock Marketplace; Ultra availability on Bedrock is expected to follow. Check the Amazon Bedrock model catalog for current availability.