DeepSeek V3.2vsLlama 4 Scout 109B
Complete comparison between DeepSeek V3.2 (685B parameters, DeepSeek) and Llama 4 Scout 109B (109B, Meta). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, and installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | DeepSeek V3.2 | Llama 4 Scout 109B |
|---|---|---|
| Settings | 685B | 109B |
| Family | DeepSeek | Llama |
| Author | DeepSeek | Meta |
| Origin | CN | US |
| License | MIT | Llama 4 Community |
| Context | 128,000 tokens | 10,000,000 tokens |
| Output | December 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 | DeepSeek V3.2 | Llama 4 Scout 109B |
|---|---|---|
| Q4_K_M (lightweight) | 410 GB | 65 GB |
| Q5_K_M (balanced) | 490 GB | 78 GB |
| Q8 (near-lossless) | 735 GB | 117 GB |
| FP16 (max quality) | 1370 GB | 218 GB |
| CPU-only RAM | 512 GB | 100 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 | DeepSeek V3.2 | Llama 4 Scout 109B |
|---|---|---|
| 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
DeepSeek V3.2
685B MoE with DeepSeek Sparse Attention. IMO gold medal. #2 by volume on OpenRouter.
- IMO gold medal
- #2 volume OpenRouter
- MIT
- 410+ GB in Q4
Installation
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
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): DeepSeek V3.2 or Llama 4 Scout 109B?
On a RTX 4090, DeepSeek V3.2 does not fit in 24 GB — part of it must be offloaded to the CPU; Llama 4 Scout 109B 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 4 Scout 109B fits in 65 GB versus 410 GB for its rival—a 345 GB gap, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
DeepSeek V3.2 is under the MIT license — free for commercial use. Llama 4 Scout 109B is under Llama 4 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 4 Scout 109B (109B). More capable: DeepSeek V3.2 (685B). If you're unsure, run the 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.