MODEL PROFILE

NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF

AtomicChat · NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF

Atomic Dynamic (AD) quants of NVIDIA's Nemotron 3.5 Bolt E4B, built from NVIDIA's original BF16 weights. From 4.0 GB to 8.0 GB on disk. Bolt E4B is NVIDIA's small reasoning and chat model, post-trained from Google's Gemma-4-E4B-it . You can now run Nemotron 3.5 Bolt E4B, NVIDIA's on-device model, in Atomic Chat . Every file here was measured against the BF16 original on one stand, next to the other publishers' files. > [!NOTE] > **Built on release day, 23 October.** Every `AD-` file below `AD-Q6_K` was > rebuilt the same evening with a better layout (below). `AD-Q4_K_M` uses our own > importance matrix, collected on the BF16 weights over a corpus rendered through > this model's chat template (`imatrix/imatrix.gguf` in the metrics repo). The > other files use Unsloth's published one (`imatrix_unsloth.gguf` from > unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF): > we built `AD-Q5_K_M` to `AD-IQ3_S` with both, and on held-out code the two > came out within noise of each other, slightly ahead with Unsloth's at 3 bits. > `AD-IQ3_XXS` (3.82 GB) was taken down the same evening: it picked the > original's next word only 74.6% of the time, below the 75% we publish at.

Text generationlicense:other
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:15:50
OVERVIEW

Model overview

Atomic Dynamic (AD) quants of NVIDIA's Nemotron 3.5 Bolt E4B, built from NVIDIA's original BF16 weights. From 4.0 GB to 8.0 GB on disk. Bolt E4B is NVIDIA's small reasoning and chat model, post-trained from Google's Gemma-4-E4B-it . You can now run Nemotron 3.5 Bolt E4B, NVIDIA's on-device model, in Atomic Chat . Every file here was measured against the BF16 original on one stand, next to the other publishers' files. > [!NOTE] > **Built on release day, 23 October.** Every `AD-` file below `AD-Q6_K` was > rebuilt the same evening with a better layout (below). `AD-Q4_K_M` uses our own > importance matrix, collected on the BF16 weights over a corpus rendered through > this model's chat template (`imatrix/imatrix.gguf` in the metrics repo). The > other files use Unsloth's published one (`imatrix_unsloth.gguf` from > unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF): > we built `AD-Q5_K_M` to `AD-IQ3_S` with both, and on held-out code the two > came out within noise of each other, slightly ahead with Unsloth's at 3 bits. > `AD-IQ3_XXS` (3.82 GB) was taken down the same evening: it picked the > original's next word only 74.6% of the time, below the 75% we publish at.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

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Sources & traceability

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。