Home › Comparison tool › Granite 4.0 H-Tiny 7B-A1B vs Qwen 2.5 VL 7B

Granite 4.0 H-Tiny 7B-A1BvsQwen 2.5 VL 7B

Complete comparison between Granite 4.0 H-Tiny 7B-A1B (7B parameters, IBM) and Qwen 2.5 VL 7B (7B, 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 Granite 4.0 H-Tiny 7B-A1B Qwen 2.5 VL 7B
Settings 7B 7B
Family Granite Qwen
Author IBM Alibaba
Origin US CN
License Apache 2.0 Apache 2.0
Context 128,000 tokens 128,000 tokens
Output October 2025 January 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 Granite 4.0 H-Tiny 7B-A1B Qwen 2.5 VL 7B
Q4_K_M (lightweight) 4 GB 6 GB
Q5_K_M (balanced) 5 GB 7 GB
Q8 (near-lossless) 7 GB 10 GB
FP16 (max quality) 14 GB 18 GB
CPU-only RAM 8 GB 10 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 Granite 4.0 H-Tiny 7B-A1B Qwen 2.5 VL 7B
RTX 4090 (24 GB) 350 tok/s · FP16 60 tok/s · FP16
RTX 4080 (16 GB) 180 tok/s · FP16 25 tok/s · Q8
RTX 3060 12GB (12 GB) 60 tok/s · Q8 8 tok/s · Q8
Apple M4 Pro (48 GB) (36 GB) 180 tok/s · FP16 25 tok/s · FP16

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.

General chat
Close match — it depends on the exact use case. The two are equivalent on this criterion; decide based on your VRAM or licensing constraints.
Vision / image
Qwen 2.5 VL 7B wins. Natively supports image inputs.
RAG / long documents
Close match — it depends on the exact use case. The two are equivalent on this criterion; decide based on your VRAM or licensing constraints.
Agents & tool use
Close match — it depends on the exact use case. The two are equivalent on this criterion; decide based on your VRAM or licensing constraints.
Lightweight deployment
Close match — it depends on the exact use case. The two are equivalent on this criterion; decide based on your VRAM or licensing constraints.
The Local AI Kit

Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.

  • Lifetime online access
  • PDF + files
  • 30-day money-back guarantee

Strengths and weaknesses

IBM · 7B

Granite 4.0 H-Tiny 7B-A1B

Hybrid 7B/1B active. Extremely low compute cost. Edge/embedded.

  • Very low compute cost
  • Apache 2.0
  • Efficient long context
  • Below 3B dense quality for certain tasks

Installation

ollama run granite4:tiny-h
Alibaba · 7B

Qwen 2.5 VL 7B

State-of-the-art 7B vision. Video >1h, bbox grounding, multilingual OCR. DocVQA 95.7.

  • SOTA vision at 7B
  • Excellent multilingual OCR
  • Long video
  • Apache 2.0
  • Requires a VLM-compatible Ollama backend ≥ 0.5

Installation

ollama run qwen2.5vl:7b

Frequently asked questions

Which model runs best on RTX 4090 (24 GB): Granite 4.0 H-Tiny 7B-A1B or Qwen 2.5 VL 7B?

On an RTX 4090, Granite 4.0 H-Tiny 7B-A1B runs at FP16 (~350 tok/s), Qwen 2.5 VL 7B in FP16 (~60 tok/s). In pure throughput, Granite 4.0 H-Tiny 7B-A1B wins. See the configurator to test your exact GPU.

Which uses the least VRAM?

In Q4_K_M, Granite 4.0 H-Tiny 7B-A1B fits in 4 GB versus 6 GB for its rival—a 2 GB difference, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.

Can these models be used in commercial production?

Granite 4.0 H-Tiny 7B-A1B is under Apache 2.0 — free for commercial use. Qwen 2.5 VL 7B 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: Granite 4.0 H-Tiny 7B-A1B (7B). More capable: Granite 4.0 H-Tiny 7B-A1B (7B). 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.

Go further