Llama 3.2 Vision 11B vs Qwen 2 VL 7B

The two models at a glance

About Llama 3.2 Vision 11B

Meta's first official multimodal Llama. An 11B vision-language model built on Llama 3.1 8B with added image adapters and a 128k text context. Strengths: 128k text context with image input, Strong OCR and image description, Built on the well-supported Llama 3 base, First-party Meta multimodal release.

About Qwen 2 VL 7B

Alibaba's Qwen 2 VL 7B — a top-tier open-weight vision model with dynamic resolution, multilingual OCR, and short video understanding. Strengths: Dynamic resolution from 20px up to 16K, Best-in-class OCR and document handling at 7B, Apache 2.0 license, Short video input support.

How they compare

Llama 3.2 Vision 11B comes from Meta and Qwen 2 VL 7B 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 11B vs 7B parameters, Llama 3.2 Vision 11B is the larger of the two. At Q4, Qwen 2 VL 7B fits in about 6 GB of VRAM versus 8 GB for the other — a 2 GB difference that matters on consumer GPUs.

Where they overlap on benchmarks, Qwen 2 VL 7B takes DocVQA with 94.5 against 88.4 — a clear 6.1-point margin. On MMMU the edge goes to Qwen 2 VL 7B (54.1 vs 50.7). For workloads weighted toward that benchmark, Qwen 2 VL 7B is the stronger default.

On a typical mid-range GPU, Qwen 2 VL 7B pushes roughly 25 tokens/sec versus 22, so it is the more responsive choice for interactive or high-volume use. For long-context work, Llama 3.2 Vision 11B offers the bigger window (128k vs 32k tokens).

Memory, quantization & throughput

Across quantization levels, Llama 3.2 Vision 11B requires Q4 ≈ 8 GB, Q5 ≈ 10 GB, Q8 ≈ 14 GB, FP16 ≈ 24 GB, while Qwen 2 VL 7B requires Q4 ≈ 6 GB, Q5 ≈ 7 GB, Q8 ≈ 10 GB, FP16 ≈ 18 GB. In practice Llama 3.2 Vision 11B fits an 8 GB card 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 3.2 Vision 11B needs roughly 14 GB of system RAM to run on CPU and Qwen 2 VL 7B about 10 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 22 tokens/sec from Llama 3.2 Vision 11B and 25 from Qwen 2 VL 7B, scaling up to 55 and 60 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 3.2 Vision 11B or Qwen 2 VL 7B to the card you actually own:

• On a 8 GB GPU: Llama 3.2 Vision 11B runs at Q4 (8 GB); Qwen 2 VL 7B runs at Q5 (7 GB).
• On a 12 GB GPU: Llama 3.2 Vision 11B runs at Q5 (10 GB); Qwen 2 VL 7B runs at Q8 (10 GB).
• On a 16 GB GPU: Llama 3.2 Vision 11B runs at Q8 (14 GB); Qwen 2 VL 7B runs at Q8 (10 GB).
• On a 24 GB GPU: Llama 3.2 Vision 11B runs at FP16 (24 GB); Qwen 2 VL 7B runs at FP16 (18 GB).

Benchmark scores

Reported benchmarks for Llama 3.2 Vision 11B: MMMU 50.7, DocVQA 88.4.

Reported benchmarks for Qwen 2 VL 7B: MMMU 54.1, DocVQA 94.5, OCRBench 845.

Bottom line: which should you pick?

• Pick Qwen 2 VL 7B if you need a permissive (Apache 2.0) license for commercial deployment.
• Pick Llama 3.2 Vision 11B for long-context work (up to 128k tokens).
• Pick Qwen 2 VL 7B for lower VRAM and faster inference; pick Llama 3.2 Vision 11B for maximum headline quality.
• Pick Qwen 2 VL 7B if DocVQA performance is your priority (94.5 vs 88.4).

Which GPU should you buy to run Llama 3.2 Vision 11B?

To run Llama 3.2 Vision 11B locally at Q4, you need ~8 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Frequently asked questions

What is the difference between Llama 3.2 Vision 11B and Qwen 2 VL 7B?

The headline differences: Llama 3.2 Vision 11B is a 11B model and Qwen 2 VL 7B is 7B; their context windows differ (128k vs 32k tokens); they ship under different licenses (Llama 3 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 3.2 Vision 11B and Qwen 2 VL 7B run on a 24 GB GPU?

At a Q4 quantization, Llama 3.2 Vision 11B needs about 8 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 2 VL 7B needs about 6 GB and fits comfortably on a 24 GB GPU. Qwen 2 VL 7B is the lighter option for tight VRAM budgets.

Is Llama 3.2 Vision 11B or Qwen 2 VL 7B more capable?

On DocVQA, Qwen 2 VL 7B scores higher (94.5 vs 88.4), a 6.1-point advantage on this benchmark.

Which is faster, Llama 3.2 Vision 11B or Qwen 2 VL 7B?

Qwen 2 VL 7B is the smaller model (7B vs 11B), 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 3.2 Vision 11B or Qwen 2 VL 7B?

Qwen 2 VL 7B ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Llama 3 Community — check its terms before commercial deployment.

Which has the longer context window, Llama 3.2 Vision 11B or Qwen 2 VL 7B?

Llama 3.2 Vision 11B has the larger context window (128k vs 32k tokens), so it handles longer documents and codebases in a single prompt.