Llama 4 Scout 109B vs Gemma 4 2B

The two models at a glance

About Llama 4 Scout 109B

Meta's compact Llama 4 MoE — 109B total, 17B active, natively multimodal, with an unprecedented 10M token context. Fits on a single H100. Strengths: 10M token context — unmatched among open models, Runs on a single H100 thanks to MoE sparsity, Native multimodal input — no separate vision adapter needed, 17B active parameters keeps inference fast.

About Gemma 4 2B

Google's 2B base model in the Gemma 4 family with text and image input, 128k context, and a 1.2GB Q4 footprint that runs on integrated graphics or a Raspberry Pi 5. Strengths: Runs on integrated GPUs at ~1.2GB VRAM in Q4, Multimodal text and image input out of the box, 128k context unusual at this parameter count, Permissive Gemma license.

How they compare

Llama 4 Scout 109B comes from Meta and Gemma 4 2B from Google, they belong to the Llama and Gemma families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.

At 109B vs 2B parameters, Llama 4 Scout 109B is the larger of the two. At Q4, Gemma 4 2B fits in about 1.2 GB of VRAM versus 65 GB for the other — a 63.8 GB difference that matters on consumer GPUs.

The two models target different sweet spots: Llama 4 Scout 109B is tuned for chat, general, vision, moe, multilingual, while Gemma 4 2B leans toward chat, vision, multilingual, small. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, Gemma 4 2B pushes roughly 100 tokens/sec versus 12, so it is the more responsive choice for interactive or high-volume use. For long-context work, Llama 4 Scout 109B offers the bigger window (9765k vs 125k tokens).

Memory, quantization & throughput

Across quantization levels, Llama 4 Scout 109B requires Q4 ≈ 65 GB, Q5 ≈ 78 GB, Q8 ≈ 117 GB, FP16 ≈ 218 GB, while Gemma 4 2B requires Q4 ≈ 1.2 GB, Q5 ≈ 1.4 GB, Q8 ≈ 2.1 GB, FP16 ≈ 4 GB. In practice Llama 4 Scout 109B spills past 24 GB even at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.

Without a GPU, Llama 4 Scout 109B needs roughly 100 GB of system RAM to run on CPU and Gemma 4 2B about 2.6 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 12 tokens/sec from Llama 4 Scout 109B and 100 from Gemma 4 2B, scaling up to 30 and 200 tokens/sec on high-end hardware.

Which fits your GPU

Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Llama 4 Scout 109B or Gemma 4 2B to the card you actually own:

• On a 8 GB GPU: Llama 4 Scout 109B does not fit; Gemma 4 2B runs at FP16 (4 GB).
• On a 12 GB GPU: Llama 4 Scout 109B does not fit; Gemma 4 2B runs at FP16 (4 GB).
• On a 16 GB GPU: Llama 4 Scout 109B does not fit; Gemma 4 2B runs at FP16 (4 GB).
• On a 24 GB GPU: Llama 4 Scout 109B does not fit; Gemma 4 2B runs at FP16 (4 GB).

Benchmark scores

Reported benchmarks for Llama 4 Scout 109B: MMLU-Pro 74.

Bottom line: which should you pick?

• Pick Llama 4 Scout 109B for long-context work (up to 9765k tokens).
• Pick Gemma 4 2B for lower VRAM and faster inference; pick Llama 4 Scout 109B for maximum headline quality.
• Pick Llama 4 Scout 109B if your workload is general, moe.
• Pick Gemma 4 2B if your workload is small.

Which GPU should you buy to run Llama 4 Scout 109B?

To run Llama 4 Scout 109B locally at Q4, you need ~65 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).

Frequently asked questions

What is the difference between Llama 4 Scout 109B and Gemma 4 2B?

The headline differences: Llama 4 Scout 109B is a 109B model and Gemma 4 2B is 2B; their context windows differ (9765k vs 125k tokens); they ship under different licenses (Llama 4 Community vs Gemma). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Llama 4 Scout 109B and Gemma 4 2B run on a 24 GB GPU?

At a Q4 quantization, Llama 4 Scout 109B needs about 65 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); Gemma 4 2B needs about 1.2 GB and fits comfortably on a 24 GB GPU. Gemma 4 2B is the lighter option for tight VRAM budgets.

Which is faster, Llama 4 Scout 109B or Gemma 4 2B?

Gemma 4 2B is the smaller model (2B vs 109B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

What licenses do Llama 4 Scout 109B and Gemma 4 2B use?

Llama 4 Scout 109B is licensed under Llama 4 Community and Gemma 4 2B under Gemma.

Which has the longer context window, Llama 4 Scout 109B or Gemma 4 2B?

Llama 4 Scout 109B has the larger context window (9765k vs 125k tokens), so it handles longer documents and codebases in a single prompt.