Google Gemma open-weight model family

Google’s Gemma family is a good sweet spot for laptops. A 4B checkpoint fits comfortably in 8GB of VRAM, the license is permissive enough for internal tooling, and Ollama pulls the weights in one command. It is also not the only small, open-weight model that runs well on a modest desktop. Since Gemma 3 landed we have tested six other families on the same 16GB laptop and a 24GB desktop, and several of them beat Gemma on tasks that matter to us. Below are the Gemma alternatives we ended up leaving installed.

Quick comparison

Model family Best for Free plan License Standout feature
Llama 3 (Meta) General chat, tool use Yes, weights on Hugging Face Meta community license Widest tooling and quant support
Qwen 3 (Alibaba) Multilingual reasoning Yes Apache 2.0 Built-in thinking mode you can toggle
Phi-4 (Microsoft) 8GB laptops Yes MIT Best small-model reasoning per gigabyte
Mistral 7B / Nemo Code and fast chat Yes Apache 2.0 Ships with function calling out of the box
DeepSeek V3 / R1 distills Math, code, chains of thought Yes MIT (distills) Explicit <think> tags
IBM Granite 3.3 Enterprise-safe drafts Yes Apache 2.0 Strong on structured extraction
SmolLM 3 Raspberry Pi and old laptops Yes Apache 2.0 3B model that runs under 4GB RAM

Why people look past Gemma

Gemma 3 is capable, but a few real gaps push us to other model families:

The alternatives

Llama 3 (Meta) - Best for the widest ecosystem

Llama 3 is the default local model for most people who host their own AI, and the reason is boring: every runner supports it, every quant format targets it first, and every fine-tune community writes for it. The 8B model runs on the same 16GB laptop that Gemma 4B fits on, with a bit more RAM headroom, and the 70B model scales up to workstation GPUs.

Where it falls short: the Meta community license has usage restrictions that Apache 2.0 does not. Multilingual quality is behind Qwen 3.

Pricing:

Migrating from Gemma: ollama pull llama3.1:8b and swap the model name in your Open WebUI or LM Studio config. Prompts port cleanly. Chat templates differ, so re-run any system prompts once.

Download: ollama.com/library/llama3.1

Bottom line: pick Llama 3 when tooling breadth matters more than raw benchmarks. Skip it if you need Apache 2.0.

Qwen 3 (Alibaba) - Best all-round default

Qwen 3 has become the safest general-purpose recommendation for local use. The family covers 0.6B to 32B dense checkpoints, plus a 30B and 235B MoE variant, so you can pick by VRAM and swap between checkpoints without changing your prompt style. Reasoning quality on math and code beats Gemma at the same parameter count, and there is a proper thinking mode you can enable at inference time.

Where it falls short: Alibaba’s license clauses around user-count thresholds trip up some enterprises. The MoE variants need a runner that supports mixture-of-experts, which not every desktop app handles gracefully.

Pricing:

Migrating from Gemma: identical Ollama flow. If you use Gemma’s built-in system prompt heuristics, Qwen accepts the same instructions. Turn thinking mode on with /set think in the CLI.

Download: ollama.com/library/qwen3

Bottom line: the model we suggest to anyone who asks “what should I run locally” without more context.

Phi-4 (Microsoft) - Best for weak hardware

Phi-4 and its Phi-4-mini sibling target small machines. The 14B model punches above its weight on reasoning benchmarks, and Phi-4-mini at 3.8B runs on 8GB of RAM with no GPU at all. Microsoft licenses everything under MIT, so it drops into commercial projects without paperwork.

Where it falls short: general chat feels drier than Llama or Qwen, and creative writing is weak. Not the model to use for marketing copy or storytelling.

Pricing:

Migrating from Gemma: direct swap. Phi-4-mini’s context window is smaller (128k on the full model, 32k on mini), so audit long-document prompts first.

Download: ollama.com/library/phi4

Bottom line: pick Phi-4-mini for old laptops and Phi-4 when reasoning quality matters more than personality.

Mistral 7B and Mistral Nemo - Best for code and function calling

Mistral 7B stays sharp, and the newer Nemo 12B model that Mistral built with NVIDIA is the family we reach for when we want fast code completion and reliable function calling. Nemo’s tool use worked on the first try in every desktop runner we tested. Apache 2.0, no strings.

Where it falls short: the base models are less chatty than Llama 3, and outputs can feel terse. Newer larger Mistral models are gated behind their commercial API.

Pricing:

Migrating from Gemma: Ollama pull, adjust chat template to Mistral’s [INST] format. If you use Gemma for tool-calling agents, Nemo will feel like an upgrade the first time you run it.

Download: mistral.ai

Bottom line: the alternative when Gemma’s tool support has let you down.

DeepSeek V3 and R1 distills - Best for reasoning

DeepSeek ships two families worth knowing: V3, a strong general MoE model, and the R1 distills that fine-tune Llama and Qwen bases into thinking machines. The R1 distills expose their chain of thought inside <think> tags, which is what the XDA writer meant by “a local LLM that actually thinks before answering.” On math word problems and multi-step code changes, the R1 distills often beat Gemma 3 27B while running in less VRAM.

Where it falls short: thinking mode chews through tokens, which slows first-token latency and eats context. The MoE V3 model is heavy for most desktops.

Pricing:

Migrating from Gemma: pull deepseek-r1:14b and prompt exactly as you would Gemma. Filter <think> tags from your UI if end users should not see them.

Download: ollama.com/library/deepseek-r1

Bottom line: the reasoning-first pick.

IBM Granite 3.3 - Best for structured extraction

IBM Granite is quiet, unglamorous, and very good at the corporate work most local LLMs are actually used for: extracting fields from documents, drafting emails, classifying tickets. Apache 2.0 license, Watsonx tooling on the enterprise side, and the 8B model runs alongside Gemma on the same hardware.

Where it falls short: conversational tone is stiff. Creative tasks feel like a legal memo.

Pricing:

Migrating from Gemma: direct swap. If you use Gemma with a JSON-mode prompt wrapper, Granite honors it more reliably.

Download: ollama.com/library/granite3.3

Bottom line: the boring right answer for extraction and classification pipelines.

SmolLM 3 - Best for old laptops and single-board computers

SmolLM 3 from Hugging Face is a 3B model designed to run on tiny hardware. It fits under 4GB of RAM, boots on a Raspberry Pi 5, and holds a coherent conversation. It will not out-reason Gemma 4B, but it will keep going on a machine where Gemma would swap to disk.

Where it falls short: knowledge cutoff is older, and long-context recall is weaker.

Pricing:

Migrating from Gemma: pull via Ollama or run through llama.cpp directly. Expect to rework any prompt that assumes Gemma’s specific knowledge cutoff.

Download: huggingface.co/HuggingFaceTB/SmolLM3-3B

Bottom line: the fallback when the machine cannot handle anything bigger.

How to choose

Pick by memory first, task second.

FAQ

What is a good Gemma alternative for a 16GB laptop? Qwen 3 8B or Phi-4-mini both fit and both outperform Gemma 3 4B on general reasoning. Qwen 3 wins on multilingual work, Phi wins on math.

Is DeepSeek R1 better than Gemma for coding? For multi-step debugging and refactor tasks the R1 distill at 14B usually beats Gemma 3 27B, mostly because the chain-of-thought helps it catch missed edge cases.

Can I run Llama 3 alongside Gemma in Ollama? Yes. Ollama swaps models on demand. Pull both, switch by name at the CLI or in Open WebUI.

Which lightweight local LLM has the most permissive license? Qwen 3, Mistral 7B, IBM Granite, and SmolLM 3 all ship under Apache 2.0. Phi-4 and DeepSeek R1 distills are MIT.

Is there a Gemma alternative with a built-in thinking mode? Qwen 3 has an explicit thinking toggle, and the DeepSeek R1 distills wrap reasoning in <think> tags. Gemma 3 does not expose either.