SmolLM vs Gemma: Which Small Model to Use?

In the SmolLM vs Gemma choice, pick Gemma 4 E2B or E4B if you want the stronger general assistant with image and audio input, 128K context and over 35 supported languages. Pick SmolLM3 (3 billion parameters) if you want a text model you can fully study and reproduce, because Hugging Face published its data mixture, training configs and checkpoints. Both are Apache 2.0, both run in Ollama and llama.cpp, and both fit on an ordinary laptop at 4 bit.

The two families answer different questions. Gemma is Google DeepMind's product line, built for quality per byte on phones and consumer GPUs. SmolLM is Hugging Face's research line, built to show how a small model gets made. Below we compare the current releases (SmolLM3 and SmolLM2 against Gemma 4 and Gemma 3), what each needs in memory, how to run them, and which one suits which job.

SmolLM vs Gemma at a glance

All figures come from the official model cards and docs linked in each section.

What SmolLM is

SmolLM is Hugging Face's family of small open models. The current flagship, SmolLM3, was published on July 8, 2025. According to the SmolLM3-3B model card, it is a 3B decoder transformer using grouped query attention and NoPE, pretrained on 11.2 trillion tokens of web, code, maths and reasoning data, then given 140 billion tokens of reasoning midtraining, supervised fine tuning and preference alignment with APO. It was trained at 64K context and reaches 128K with YaRN extrapolation. If you are choosing between SmolLM3 and Meta's 3B model instead, see SmolLM vs Llama.

Its main selling point is openness. The SmolLM3 blog post calls the release an "engineering blueprint": architecture details, the exact data mixtures for its three pretraining stages, and the method for building a hybrid reasoning model. Intermediate checkpoints are on the Hub too. If you want to learn how small models are trained, our guides to training a small LLM from scratch and the best LLM pretraining datasets cover the same public data (FineWeb-Edu, DCLM, The Stack) that SmolLM uses.

SmolLM2 is the older, smaller line. Its model card lists 135M, 360M and 1.7B sizes, with the 1.7B trained on 11 trillion tokens. These are the models to reach for when you need something tiny, such as a 271 MB download on Ollama for smollm2:135m.

What Gemma is

Gemma is Google DeepMind's open weight family. The Gemma 4 E2B model card lists five sizes (E2B, E4B, 12B, 26B A4B and 31B). The two small ones compete with SmolLM. The "E" means effective parameters: E2B behaves like a 2.3B model but carries 5.1B parameters once Per Layer Embeddings are counted, and E4B is 4.5B effective with 8B in total. Both take text, images, audio up to 30 seconds and video as frames, with a 128K context window.

Gemma 3 still matters at the very small end. Google's Gemma 3 model card lists a 270M model trained on 6 trillion tokens and a 1B trained on 2 trillion, both with 32K context, while 4B and up get 128K. Gemma 3 ships under the Gemma Terms of Use rather than Apache 2.0, so read those terms before you ship it in a product. For how licences like these differ, see open weight vs open source.

For a full Gemma 4 setup with every size, see how to run Gemma 4 locally.

SmolLM3 vs Gemma 4: quality

There is no fair head to head benchmark table from either lab, and you should be wary of anyone who builds one by mixing numbers from two model cards with different settings. What the official sources do say:

• The SmolLM GitHub repository says SmolLM3 outperforms Llama 3.2 3B and Qwen2.5 3B and stays "competitive with larger 4B alternatives (Qwen3 & Gemma3)". That comparison is with Gemma 3, not Gemma 4.
• The SmolLM3 card's own tables show it behind Qwen3-4B on most reasoning tests, but first on IFEval instruction following in no thinking mode (76.7).
• The Gemma 4 card reports E4B at 69.4% on MMLU Pro and E2B at 60.0%. On the same table Gemma 3 27B scores 67.6%, so the small E4B edges past the old 27B model. These are Google's numbers, not independent replications.

So the honest read is that Gemma 4 E4B is the stronger all round model, and SmolLM3 is a capable 3B text model that holds its own on instruction following and tool calling. If you need images or speech, the question is settled: SmolLM3 is text only (Hugging Face's separate SmolVLM line handles vision).

Memory and speed

Both families are small enough for a laptop. The table uses official file sizes and Google's published estimates; long contexts add KV cache on top (see how much VRAM you need for an LLM).

Google's Gemma 4 overview also lists mobile builds of E2B at about 1.1 GB (0.84 GB text only). For an 8 GB machine, SmolLM3 at Q4_K_M or Gemma 4 E2B at 4 bit both leave room for context. On 16 GB, Gemma 4 E4B at 8 bit is comfortable. If the quant names are new to you, read GGUF quantization explained.

How to run each one locally

Gemma has official Ollama tags, and SmolLM2 does too. SmolLM3 has no official Ollama library entry yet, but Hugging Face lets Ollama pull any GGUF repo directly, as its Ollama docs describe:

# Gemma
ollama run gemma4:e2b
ollama run gemma4:e4b
ollama run gemma3:1b

# SmolLM
ollama run hf.co/ggml-org/SmolLM3-3B-GGUF:Q4_K_M
ollama run smollm2:1.7b

With llama.cpp, point llama-server at the same GGUF files. On a Mac, MLX builds exist for both (for example mlx-community/SmolLM3-3B-4bit), which mlx-lm can load. For serving, the SmolLM3 card documents vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes for vLLM, and SGLang works as well. In Transformers, SmolLM3 needs version 4.53.0 or later.

For which local runner to choose, see Ollama vs llama.cpp vs LM Studio.

Turning reasoning on and off

SmolLM3 thinks by default. Put /no_think in the system prompt, or pass enable_thinking=False to the chat template, for short direct answers. Hugging Face recommends temperature 0.6 and top_p 0.95. Gemma 4 also has a thinking mode; the card notes that E2B and E4B skip the empty thought block when thinking is off, which keeps output clean for simple chat apps.

Fine tuning SmolLM vs Gemma

Both are easy to fine tune because they are small. A 3B or 4B model trains with LoRA on a single consumer GPU, and Unsloth publishes a Gemma 4 guide. For SmolLM, Hugging Face's own TRL library is the natural fit, since it comes from the same company and loads the model through Transformers. Our walkthrough on how to fine tune an LLM locally applies to either, and LoRA vs QLoRA explains the memory tradeoff.

The difference shows up when you want to change pretraining. Only SmolLM gives you the base checkpoints from every stage plus the data recipe, so you can resume, ablate or swap data. With Gemma you start from the released base or instruct weights.

Which should you use?

• Private assistant on a laptop or phone: Gemma 4 E2B or E4B. It is the stronger model, it reads images and speech, and it handles many languages.
• Learning how LLMs are built, or research you need to reproduce: SmolLM3. Open data, open configs and intermediate checkpoints make it a teaching model.
• Tiny text tasks on devices with very little memory: SmolLM2 135M or 360M, or Gemma 3 270M if its terms suit you.
• Commercial product with a simple licence: SmolLM3, SmolLM2 or Gemma 4, all Apache 2.0. Gemma 3 adds the Gemma Terms.
• European languages beyond English in a 3B text model: SmolLM3 covers French, Spanish, German, Italian and Portuguese natively; Gemma 4 covers far more.

If neither fits, our list of open source LLM models to run locally includes Qwen and gpt-oss options at similar sizes.

FAQ

Is SmolLM better than Gemma?

Not overall. Gemma 4 E4B reports stronger scores across Google's tables and adds vision and audio. SmolLM3 wins on openness, since its full training recipe is public, and it compares well with Gemma 3 at the 3B to 4B scale according to Hugging Face.

Is SmolLM3 multimodal?

No. SmolLM3 takes text only. Hugging Face's SmolVLM models are the vision line from the same team, while every Gemma 4 size accepts images and the E2B and E4B models also accept audio.

Can I use SmolLM and Gemma commercially?

SmolLM3, SmolLM2 and Gemma 4 are published under Apache 2.0 on their Hugging Face cards. Gemma 3 uses the Gemma Terms of Use, which add conditions. Always check the licence file of the exact version you ship.

Which is smallest, SmolLM2 135M or Gemma 3 270M?

SmolLM2 135M is smaller, with a 271 MB Ollama download compared with 292 MB for gemma3:270m. Gemma 3 270M has a 32K context window and was trained on 6 trillion tokens.

Does SmolLM3 work in Ollama?

Yes, through Hugging Face's GGUF integration: ollama run hf.co/ggml-org/SmolLM3-3B-GGUF:Q4_K_M. There is no official smollm3 tag in the Ollama library yet, only smollm2.

How much RAM do SmolLM3 and Gemma 4 E2B need?

At 4 bit, the SmolLM3 Q4_K_M file is 1.92 GB and Google estimates about 2.9 GB for Gemma 4 E2B at Q4_0. Add room for the context window and your operating system, so 8 GB of RAM is a comfortable floor for either.