Nemotron Mini 4B vs Gemma 3 4B
Side-by-side comparison of hardware requirements, quantization options, and specifications to help you choose the right model for your device.
Specifications Comparison
| Spec | Nemotron Mini 4B | Gemma 3 4B |
|---|---|---|
| Parameters | 4B | 4B |
| Architecture | nemotron | gemma3 |
| License | Custom | Gemma |
| Context Length | 8K tokens | 32K tokens |
| Category | Language Model | Language Model |
| Author | NVIDIA | |
| HF Downloads | 320.3K | 1.9M |
| VRAM Range | 3.01 - 4.65 GB | 2.82 - 4.35 GB |
| Quantizations | 2 options | 2 options |
| Best Quality Score | 98% | 98% |
Quantization Options
Nemotron Mini 4B
Gemma 3 4B
In-depth comparison
Gemma 3 4B is the better choice for most users due to its longer context length and higher popularity, but Nemotron Mini 4B is more suitable for edge devices with limited VRAM.
When to choose Nemotron Mini 4B
Nemotron Mini 4B is the better pick for users who need a model that can run efficiently on edge devices with limited VRAM, such as Raspberry Pis or older laptops. It has a slightly higher minimum VRAM requirement (3.0GB) compared to Gemma 3 4B, but it is optimized for edge deployment, ensuring smooth performance even on less powerful hardware.
When to choose Gemma 3 4B
Gemma 3 4B is the better choice for users who require a longer context length for their tasks, such as summarizing long documents or generating detailed narratives. With a context length of 32,768 tokens, it can handle much larger inputs than Nemotron Mini 4B, which only supports 8,192 tokens. Additionally, its higher download count and likes indicate a broader community support and trust.
Quality
Both models have the same parameter count and best quality score of 98%, suggesting similar output quality in terms of coherence and relevance. However, Gemma 3 4B's longer context length gives it an edge in tasks requiring deeper understanding of long-form content.
Performance & hardware fit
Nemotron Mini 4B requires slightly more VRAM (3.0GB) compared to Gemma 3 4B (2.8GB), but it is optimized for edge deployment, making it more suitable for lower-end hardware. In terms of speed, both models should perform similarly given their identical parameter counts, but Gemma 3 4B may have a slight advantage in handling larger contexts.
Use-case fit
| coding | Tie | Both models have similar capabilities in generating code snippets, but Gemma 3 4B might be slightly better for longer, more complex codebases due to its longer context length. |
| creative writing | Gemma 3 4B | Gemma 3 4B's longer context length makes it more suitable for creative writing tasks that involve detailed storytelling or world-building. |
| RAG / retrieval | Gemma 3 4B | Gemma 3 4B's ability to handle longer contexts is beneficial for RAG tasks, where understanding and synthesizing large amounts of information is crucial. |
| agent / tool use | Tie | Both models are capable of handling agent and tool use tasks, but Gemma 3 4B might have a slight edge in tasks requiring longer conversations or more detailed instructions. |
| running on consumer GPU (8-12GB) | Gemma 3 4B | Gemma 3 4B requires less VRAM (2.8GB) and is more likely to run smoothly on consumer GPUs with 8-12GB of VRAM. |
| long context (16K+) | Gemma 3 4B | Gemma 3 4B supports a context length of 32,768 tokens, making it the clear winner for tasks requiring long contexts. |
Gemma 3 4B wins for most users due to its longer context length and higher community support, but Nemotron Mini 4B is the better choice for edge devices with limited VRAM.