Llama 4 Scout 109BvsLlama 3.3 70B Instruct
Complete comparison between Llama 4 Scout 109B (109B parameters, Meta) and Llama 3.3 70B Instruct (70B, Meta). VRAM required by quantization, tokens per second measured on 4 reference GPUs, use-case verdict, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Llama 4 Scout 109B | Llama 3.3 70B Instruct |
|---|---|---|
| Settings | 109B | 70B |
| Family | Llama | Llama |
| Author | Meta | Meta |
| Origin | US | US |
| License | Llama 4 Community | Llama 3.3 Community |
| Context | 10,000,000 tokens | 128,000 tokens |
| Output | April 2025 | December 2024 |
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 | Llama 3.3 70B Instruct |
|---|---|---|
| Q4_K_M (lightweight) | 65 GB | 40 GB |
| Q5_K_M (balanced) | 78 GB | 48 GB |
| Q8 (near-lossless) | 117 GB | 75 GB |
| FP16 (max quality) | 218 GB | 140 GB |
| CPU-only RAM | 100 GB | 64 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 | Llama 3.3 70B Instruct |
|---|---|---|
| RTX 4090 (24 GB) | ✗ too heavy | ✗ too heavy |
| 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 | ✗ too heavy |
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
Llama 3.3 70B Instruct
Llama 3.1 405B quality at 1/6 the size. Weights under a community license, gated HF access.
- 405B quality at 1/6 the size
- 128k ctx
- Very strong at reasoning and coding
- HF gated (accept Meta's terms)
- Community license with a >700M MAU clause
- No vision
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Llama 4 Scout 109B or Llama 3.3 70B Instruct?
On a RTX 4090, Llama 4 Scout 109B does not fit in 24 GB — part of it must be offloaded to the CPU; Llama 3.3 70B Instruct does not fit in 24 GB either. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Llama 3.3 70B Instruct fits in 40 GB versus 65 GB for its rival—a 25 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). Llama 3.3 70B Instruct is under Llama 3.3 Community—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: Llama 3.3 70B Instruct (70B). 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.