NVIDIA Nemotron 3.5 Bolt 74B A9B: Efficient Vision-Language Agent Model
NVIDIA Nemotron 3.5 Bolt 74B A9B is a vision-language model from NVIDIA built to run everyday agentic tasks: tool calling, web browsing and terminal use. It reads text, images and video and answers in text. NVIDIA released it on Hugging Face on October 15, 2026.
It ships in two forms. The BF16 release holds the full-precision weights and is the reference checkpoint for customization and post-training. The 24GB release uses a native 2-bit quantization; NVIDIA recommends it for latency- and throughput-optimized inference, and publishes it both as a GGUF for llama.cpp and as safetensors for vLLM.
Architecture
Bolt is a hybrid that interleaves Mamba-2 state-space layers, Mixture-of-Experts layers and a small number of attention layers, using NVIDIA's LatentMoE design. Images and video frames go through a C-RADIO v4-H vision encoder, connected to the language model by an MLP projector. The model supports up to 256K tokens of context, shared by the prompt, visual inputs, conversation history, tool results and the generated response.
It was compressed from Nemotron 3.5 Super VL 120B-A12B with Star Elastic, NVIDIA's router-guided architecture search and compression method. A budget-conditioned router chose the number of experts and the expert width separately for each layer, while the number of experts used per token was fixed at 9, down from 22 in the parent. Knowledge recovery ran for 120B tokens, first at 8K context and then at 128K.
Training
After compression, NVIDIA re-attached the parent model's vision tower and retrained the vision adapter on about 6.6M image-text samples covering captioning, visual question answering, counting, documents and OCR. A vision-heavy supervised fine-tuning stage followed, targeting GUI grounding and web navigation alongside text reasoning, coding and tool-use data, with checkpoint merging to keep text and vision skills balanced. Reinforcement learning then trained the model across agentic environments for MCP tool use and software engineering, with an RLHF stage that also penalized excess reasoning for token efficiency.
For the 24GB release, the experts were quantized to 2-bit (IQ2_XXS) with quantization-aware distillation to recover accuracy. Mamba layers are kept in NVFP4 and the embedding and output layers in 8-bit.
Key Features
- Text, image and video input, with video clips of up to two minutes
- Reasoning on or off per request through the chat template, plus a medium-effort reasoning mode
- Native tool calling for agent harnesses
- Up to 256K tokens of context
- English and programming languages as primary, with Spanish, French, German, Italian and Japanese also supported
- Post-training data through May 2026
Benchmarks
NVIDIA reports that the 24GB release scores 86.43 on GPQA Diamond, 50.98 on Terminal-Bench 2.1, 66.75 on BrowseComp, 52.20 on MCP-Atlas, 50.86 on WebArena with vision, 64.06 on AA-LCR, 41.02 on SciCode, 25.30 on HLE without tools, 28.47 on GDPVal-AA-V2 and 87.82 on PinchBench.
The full-precision BF16 release scores a few points higher on most of the same tests, for example 88.83 on GPQA Diamond, 53.93 on Terminal-Bench 2.1, 70.00 on BrowseComp, 55.60 on MCP-Atlas and 90.35 on PinchBench.
Use Cases
- Local and self-hosted agents that call tools, browse the web and work in a terminal
- Visual tasks such as screen and GUI understanding, documents, charts and web navigation
- Video understanding and summarization
- Long-document analysis and retrieval-augmented generation
- Post-training, domain adaptation and building your own quantized variants from the BF16 weights
Sampling
NVIDIA recommends a temperature of 1.0 and top-p of 0.95.
