Llama 4 Scout 109BvsQwen 3 30B-A3B
Complete comparison between Llama 4 Scout 109B (109B parameters, Meta) and Qwen 3 30B-A3B (30B, Alibaba). 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 | Qwen 3 30B-A3B |
|---|---|---|
| Settings | 109B | 30B |
| Family | Llama | Qwen |
| Author | Meta | Alibaba |
| Origin | US | CN |
| License | Llama 4 Community | Apache 2.0 |
| Context | 10,000,000 tokens | 131,072 tokens |
| Output | April 2025 | April 2025 |
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 | Qwen 3 30B-A3B |
|---|---|---|
| Q4_K_M (lightweight) | 65 GB | 19 GB |
| Q5_K_M (balanced) | 78 GB | 23 GB |
| Q8 (near-lossless) | 117 GB | 35 GB |
| FP16 (max quality) | 218 GB | 62 GB |
| CPU-only RAM | 100 GB | 32 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 | Qwen 3 30B-A3B |
|---|---|---|
| RTX 4090 (24 GB) | ✗ too heavy | 100 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | ✗ too heavy | 40 tok/s · Q8 |
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
Qwen 3 30B-A3B
MoE 30B/3B active hybrid thinking. MMLU 81.4, AIME24 80.4. 100+ languages.
- Fast thanks to the 3B active parameters
- MMLU 81.4
- AIME24 80.4
- Apache 2.0
- 19 GB in Q4
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Llama 4 Scout 109B or Qwen 3 30B-A3B?
On a RTX 4090, Llama 4 Scout 109B does not fit in 24 GB — part of it must be offloaded to the CPU, Qwen 3 30B-A3B in Q5_K_M (~100 tok/s). See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 3 30B-A3B fits in 19 GB versus 65 GB for its rival—a 46 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). Qwen 3 30B-A3B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 3 30B-A3B (30B). 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.