Review · AISource: NVIDIA
Nemotron Bolt claims a 24GB footprint; its deployment recipes target larger GPUs
NVIDIA positions Nemotron Bolt for local agentic work, including coding, with a stated 24GB memory footprint. [2] Its published GGUF deployment recipes target discrete GPUs with at least 32GB or 48GB of memory, depending on configuration. [1] NVIDIA announced availability on October 15, 2026. [2] The quantized model card lists 74 billion total parameters and 9 billion active parameters. It supports multimodal inputs and text output. [1] It specifies mixed quantization: IQ2-XXS experts, NVFP4 Mamba layers and Q8_0 embeddings and output. [1] The setup instructions use llama.cpp with a separate LiLiCorr drafter for speculative decoding. [1]
Quantized AI model
NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB GGUF
VerdictWait and see
NVIDIA's published deployment recipes do not establish practical coding performance or demonstrate operation on a 24GB GPU. [1]
- Best for
- Developers evaluating NVIDIA's published GGUF deployment recipes for coding agents [1]
- Pricing
- Commercial and noncommercial use under OpenMDW-1.1; the model card specifies no subscription price or hosted free-tier allowance. [1]
Pros
- Official quantized artifact with llama.cpp deployment instructions [1]
- Documented context and concurrency settings for larger GPUs [1]
- Commercial and noncommercial use under OpenMDW-1.1 [1]
Cons
- Published GGUF recipes target 32GB+ or 48GB+ GPUs [1]
- Memory requirements vary with context length and speculative-decoding settings; the published recipes do not demonstrate a 24GB GPU configuration [1]
- Unpinned engine and model revisions in setup instructions [1]
Why it matters
The smaller recipe specifies 65,536 context tokens and one sequence on a GPU with at least 32GB. [1] The 262,144-token recipe targets GPUs with at least 48GB. [1] The card lists a 256K maximum context. Its quick start uses 98,304 tokens. [1] NVIDIA compares Bolt with Qwen3.6-35B-A3B and Gemma4 26B-A4B in BF16 precision. [1] That comparison does not establish an advantage at equivalent local memory budgets.
Attribution
Sources
- 1NVIDIANVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB GGUF model card
- 2NVIDIANVIDIA announces Nemotron Super VL and Bolt
- 3NVIDIANVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB packed model card
Read the original source for full details and context.
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OpenHands outlines screenshot investigation and UI review with Nemotron 3.5 Super VL
OpenHands’ announced integration with NVIDIA Nemotron 3.5 Super VL describes use cases including screenshot-based bug investigation and UI review against designs or product specifications. OpenHands announced its role as an NVIDIA launch partner on October 15, 2026. It describes its product as an open-source platform for software engineering agents. The company says NVIDIA’s model could help agents investigate screenshots alongside logs and review interfaces against specifications. NVIDIA’s BF16 serving example uses http://localhost:8000/v1. It accepts a base64 PNG data URL through an image_url content block. It documents vllm/vllm-openai:v0.32.0, qwen3_coder tool calling and nemotron_v3 reasoning parsers.
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