Llama 4 Scout 109BvsGemma 4 2B
Complete comparison between Llama 4 Scout 109B (109B parameters, Meta) and Gemma 4 2B (2B, Google). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Llama 4 Scout 109B | Gemma 4 2B |
|---|---|---|
| Settings | 109B | 2B |
| Family | Llama | Gemma |
| Author | Meta | |
| Origin | US | US |
| License | Llama 4 Community | Gemma |
| Context | 10,000,000 tokens | 128,000 tokens |
| Output | April 2025 | May 6, 2026 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Llama 4 Scout 109B | Gemma 4 2B |
|---|---|---|
| Q4_K_M (lightweight) | 65 GB | 1.2 GB |
| Q5_K_M (balanced) | 78 GB | 1.4 GB |
| Q8 (near-lossless) | 117 GB | 2.1 GB |
| FP16 (max quality) | 218 GB | 4 GB |
| CPU-only RAM | 100 GB | 2.6 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Llama 4 Scout 109B | Gemma 4 2B |
|---|---|---|
| RTX 4090 (24 GB) | ✗ too heavy | 200 tok/s · FP16 |
| RTX 4080 (16 GB) | ✗ too heavy | 100 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | ✗ too heavy | 35 tok/s · FP16 |
| Apple M4 Pro (48 GB) (36 GB) | ✗ too heavy | 100 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Llama 4 Scout 109B
109B/17B active MoE, native multimodal, 10M context. Fits on an H100. HF gated.
- 10M context (!)
- Fits on an H100
- Native vision
- HF gated
- Llama 4 license (clause >700M MAU)
Installation
Gemma 4 2B
Gemma 4 base 2B multimodal (text+image), Gemma license, 128k ctx. Runs on an integrated GPU or Raspberry Pi 5. Released May 2026.
- Fits on integrated GPU (1.2 GB Q4 VRAM)
- Text + image multimodal
- License Gemma
- 128k context on 2B
- Limited reasoning vs. 4B/26B
- Gated model on Hugging Face (click-through)
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Llama 4 Scout 109B or Gemma 4 2B?
On an RTX 4090, Llama 4 Scout 109B does not fit in 24 GB — part of it must be offloaded to the CPU, Gemma 4 2B in FP16 (~200 tok/s). Check the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Gemma 4 2B fits in 1.2 GB versus 65 GB for its rival—a 63.8 GB gap, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Llama 4 Scout 109B is under Llama 4 Community — check the restrictions (especially the monthly user thresholds). Gemma 4 2B is under Gemma — also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Gemma 4 2B (2B). More capable: Llama 4 Scout 109B (109B). If you're unsure, run it configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.