Llama 4 Scout 109B vs Qwen 3 30B-A3B

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 Qwen 3 30B-A3B

Alibaba's Qwen 3 MoE with 30B total and just 3B active parameters, supporting hybrid thinking mode. MMLU 81.4, AIME24 80.4, 100+ languages, Apache 2.0. Strengths: 3B active parameters keeps inference fast and cheap, MMLU 81.4 and AIME24 80.4 — strong on both general and reasoning, Apache 2.0, Hybrid thinking toggle per request.

How they compare

Llama 4 Scout 109B comes from Meta and Qwen 3 30B-A3B from Alibaba, they belong to the Llama and Qwen 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 30B parameters, Llama 4 Scout 109B is the larger of the two. At Q4, Qwen 3 30B-A3B fits in about 19 GB of VRAM versus 65 GB for the other — a 46 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 Qwen 3 30B-A3B leans toward chat, general, reasoning, multilingual, moe. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, Qwen 3 30B-A3B pushes roughly 40 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 128k 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 Qwen 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 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 Qwen 3 30B-A3B about 32 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 40 from Qwen 3 30B-A3B, scaling up to 30 and 100 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 Qwen 3 30B-A3B to the card you actually own:

• On a 24 GB GPU: Llama 4 Scout 109B does not fit; Qwen 3 30B-A3B runs at Q5 (23 GB).

Benchmark scores

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

Reported benchmarks for Qwen 3 30B-A3B: MMLU (base) 81.38, AIME 2024 80.4.

Bottom line: which should you pick?

• Pick Qwen 3 30B-A3B if you need a permissive (Apache 2.0) license for commercial deployment.
• Pick Llama 4 Scout 109B for long-context work (up to 9765k tokens).
• Pick Qwen 3 30B-A3B for lower VRAM and faster inference; pick Llama 4 Scout 109B for maximum headline quality.
• Pick Llama 4 Scout 109B if your workload is vision.
• Pick Qwen 3 30B-A3B if your workload is reasoning.

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 Qwen 3 30B-A3B?

The headline differences: Llama 4 Scout 109B is a 109B model and Qwen 3 30B-A3B is 30B; their context windows differ (9765k vs 128k tokens); they ship under different licenses (Llama 4 Community vs Apache 2.0). 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 Qwen 3 30B-A3B 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); Qwen 3 30B-A3B needs about 19 GB and fits comfortably on a 24 GB GPU. Qwen 3 30B-A3B is the lighter option for tight VRAM budgets.

Which is faster, Llama 4 Scout 109B or Qwen 3 30B-A3B?

Qwen 3 30B-A3B is the smaller model (30B vs 109B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

Which license is safer for commercial use, Llama 4 Scout 109B or Qwen 3 30B-A3B?

Qwen 3 30B-A3B ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Llama 4 Community — check its terms before commercial deployment.

Which has the longer context window, Llama 4 Scout 109B or Qwen 3 30B-A3B?

Llama 4 Scout 109B has the larger context window (9765k vs 128k tokens), so it handles longer documents and codebases in a single prompt.