Phi-3.5 Mini 3.8B vs Gemma 2 2B
Side-by-side comparison of hardware requirements, quantization options, and specifications to help you choose the right model for your device.
Specifications Comparison
| Spec | Phi-3.5 Mini 3.8B | Gemma 2 2B |
|---|---|---|
| Parameters | 3.8B | 2.6B |
| Architecture | phi3 | gemma2 |
| License | MIT | Gemma |
| Context Length | 128K tokens | 8K tokens |
| Category | Language Model | Language Model |
| Author | Microsoft | |
| HF Downloads | 671.4K | 385.1K |
| VRAM Range | 2.73 - 4.28 GB | 2.09 - 3.09 GB |
| Quantizations | 3 options | 2 options |
| Best Quality Score | 98% | 98% |
Quantization Options
Phi-3.5 Mini 3.8B
Gemma 2 2B
In-depth comparison
Phi-3.5 Mini 3.8B is the better choice for most users due to its larger context window and higher parameter count, which translates to better performance in complex tasks.
When to choose Phi-3.5 Mini 3.8B
Phi-3.5 Mini 3.8B is the better pick when you need to handle more complex tasks or longer context lengths. With 3.8 billion parameters and a context length of 131,072 tokens, it can generate more detailed and coherent outputs, making it ideal for tasks like summarizing long documents, generating creative content, or handling intricate coding tasks. Additionally, its higher quality score and larger parameter count ensure that it can produce more nuanced and accurate results.
When to choose Gemma 2 2B
Gemma 2 2B is the better pick when you have very limited VRAM or need to run the model on devices with minimal resources. With a minimum VRAM requirement of 2.1GB, it can run smoothly on older or less powerful hardware, making it a good choice for mobile devices or low-end GPUs. Despite having fewer parameters, it still performs well in basic text generation tasks and can be a viable option for simpler use cases.
Quality
Both models have a best quality score of 98%, indicating similar overall performance. However, Phi-3.5 Mini 3.8B, with its 3.8 billion parameters, is likely to produce higher-quality outputs in more complex tasks due to its larger size and context window. Gemma 2 2B, while slightly smaller at 2.6 billion parameters, is still capable but may fall short in tasks requiring deep understanding or extensive context.
Performance & hardware fit
Phi-3.5 Mini 3.8B requires 2.7GB of VRAM, which is slightly more than Gemma 2 2B's 2.1GB requirement. This makes Phi-3.5 Mini 3.8B more demanding in terms of hardware, but it also means it can handle more complex tasks and longer sequences of text. Gemma 2 2B is more efficient and can run on lower-end hardware, making it a better choice for resource-constrained environments.
Use-case fit
| coding | Phi-3.5 Mini 3.8B | Phi-3.5 Mini 3.8B's larger parameter count and context window make it better suited for handling complex coding tasks and generating more detailed code snippets. |
| creative writing | Phi-3.5 Mini 3.8B | Phi-3.5 Mini 3.8B's ability to handle longer context lengths and generate more detailed content makes it superior for creative writing tasks. |
| RAG / retrieval | Phi-3.5 Mini 3.8B | Phi-3.5 Mini 3.8B's larger context window allows it to process and generate more relevant information in retrieval-augmented generation tasks. |
| agent / tool use | Phi-3.5 Mini 3.8B | Phi-3.5 Mini 3.8B's larger parameter count and context window make it better for handling complex interactions and tool use scenarios. |
| running on consumer GPU (8-12GB) | Phi-3.5 Mini 3.8B | Both models can run on consumer GPUs, but Phi-3.5 Mini 3.8B's higher performance and larger context window make it the better choice for most tasks. |
| long context (16K+) | Phi-3.5 Mini 3.8B | Phi-3.5 Mini 3.8B has a context length of 131,072 tokens, making it the clear winner for tasks requiring long context lengths. |
Phi-3.5 Mini 3.8B wins for most users due to its superior performance in complex tasks and longer context lengths. Gemma 2 2B is the better choice only when running on very low-end hardware with limited VRAM.