IBM Granite 4.1 Open-Source Models: Real-World SVG Generation Comparison Across 21 GGUF Quantization Variants
IBM's Apache 2.0 Granite 4.1 quantization test reveals small model capability limits mask precision differences.
IBM released the Apache 2.0 licensed Granite 4.1 open-source LLM series (3B/8B/30B). Unsloth provided 21 GGUF quantization variants for the 3B model. Simon Willison tested all variants with an SVG pelican generation task and found no clear pattern between output quality and quantization precision—because the 3B model itself lacks sufficient spatial reasoning capability, quantization differences are drowned out by the model's capability ceiling. The experiment shows that evaluating quantization impact requires tasks where the model already excels.
IBM Granite 4.1: An Open-Source Model Family Under the Apache 2.0 License
IBM recently released the Granite 4.1 series of large language models under the Apache 2.0 open-source license, available in three parameter sizes: 3B, 8B, and 30B. Apache 2.0 is one of the most permissive licenses in the open-source software world, allowing users to freely use, modify, distribute, and commercialize derivative works without requiring those derivatives to also be open-sourced—a stark contrast to "copyleft" licenses like the GPL. In the LLM space, many so-called "open-source" models actually come with various usage restrictions: for example, Meta's Llama series uses a custom license with additional restrictions for companies with over 700 million monthly active users; Mistral's early models, while offering open weights, also had reservations in their commercial use terms. IBM's choice of Apache 2.0 means Granite 4.1 has virtually no legal barriers for enterprise users, which is a significant differentiating advantage for developers who need to embed models in commercial products, making it quite competitive in the current open-source model ecosystem.
Granite team member Yousaf Shah provided a detailed account of the model's training process on the Hugging Face blog, offering transparent technical details for the community.
Unsloth Releases 21 GGUF Quantization Variants: Full Coverage from 1.2GB to 6.34GB
The well-known model optimization team Unsloth quickly followed up by releasing a collection of GGUF-format quantized versions of the Granite 4.1 3B model. Unsloth is an open-source project founded by brothers Daniel and Michael Han, focused on training acceleration and inference optimization for large models. Their Unsloth library can speed up model fine-tuning by 2-5x without sacrificing precision while reducing VRAM usage by up to 80%. The team is renowned in the open-source community for their rapid response to new model releases and high-quality quantization versions—typically providing a full suite of quantization variants within hours of a model's release, serving as an important bridge between model developers and end-deployment users.
This time, they delivered 21 different quantization variants in one go, with file sizes ranging from 1.2GB to 6.34GB, totaling approximately 51.3GB.
GGUF (GPT-Generated Unified Format) is a model storage format designed by Georgi Gerganov, the creator of the llama.cpp project, specifically optimized for efficient inference on CPUs and consumer-grade GPUs. It is the successor to the earlier GGML format, addressing its predecessor's shortcomings in metadata management and forward compatibility. It is widely used in inference frameworks like llama.cpp and Ollama, and is currently one of the most popular formats for local LLM deployment.
Quantization is the technique of compressing model weights from high-precision floating-point numbers (like FP16, where each parameter takes 2 bytes) to lower-precision representations (like 4-bit integers, where each parameter takes only 0.5 bytes). Common quantization levels include Q2_K, Q3_K_S, Q4_K_M, Q5_K_M, Q6_K, Q8_0, etc., where higher numbers indicate greater preserved precision. The "K" series quantization methods employ k-quant technology, which assigns different quantization precision to different layers based on their importance, preserving as much model performance as possible at the same compression ratio. Unsloth's 21 variants cover the entire range from extreme compression (approximately 2-bit) to near-lossless (8-bit), letting users flexibly choose based on their hardware capabilities. Different quantization levels make different tradeoffs between model size, inference speed, and output quality—larger quantized files theoretically retain more model capability, while smaller files sacrifice some precision for lower hardware requirements.
The Pelican-on-a-Bicycle Experiment: Testing Quantization Precision Differences with SVG Generation
Prominent developer Simon Willison, upon seeing these 21 quantized files, decided to run an experiment he had been contemplating for a while: using the same prompt—"Generate an SVG of a pelican riding a bicycle"—to test all 21 quantization variants and observe the actual impact of different quantization levels on output quality.
The experiment design is quite clever. SVG (Scalable Vector Graphics) is an XML-based vector graphics description language that defines images using coordinates, paths, shapes, and other mathematical descriptions rather than pixel grids. When asking an LLM to generate SVG, the model needs to simultaneously possess multiple capabilities: first, visual concept understanding of the target objects—knowing that pelicans have large throat pouches and bicycles have two wheels with chain-drive mechanisms; second, spatial reasoning ability—being able to reasonably arrange the position and proportions of various components in a 2D coordinate system; and finally, code generation ability—outputting syntactically correct SVG markup. This makes SVG generation an extremely comprehensive benchmark test. Notably, even models with far more parameters (like GPT-4, Claude 3.5 Sonnet) frequently produce proportion errors or structural mistakes on complex SVG generation tasks, indicating that spatial reasoning remains a common weakness of current LLM architectures. By comparing outputs from different quantized versions of the same model, one could theoretically visualize the degree to which quantization loss affects model capabilities.
Surprising Results
However, the results did not show the clear pattern one might expect. Simon admitted: "The results were far less interesting than I had hoped. There's no discernible pattern between quality and model size—they're all pretty bad!"
Looking at the displayed images, most outputs were abstract assemblages of geometric shapes, bearing little resemblance to either a pelican or a bicycle. Interestingly, the smallest 1.2GB model actually generated the most bicycle-like graphic, while the largest model only managed to produce a shape vaguely resembling a pelican.
Experimental Conclusions: Quantization Precision vs. Fundamental Model Capability
The SVG Generation Ceiling of a 3B Parameter Model
The core finding of this experiment may not be about quantization at all, but rather about model scale. A 3B (3 billion) parameter model falls into the "small model" category in the current LLM ecosystem. For reference, GPT-3 has 175 billion parameters, the largest version of Llama 3.1 is 405B, and even Phi-3 Mini, considered "compact," has 3.8B parameters. There is an approximate scaling law relationship between model parameter count and capability—research proposed by Kaplan et al. in 2020 and later refined by the Chinchilla paper shows that model performance follows power-law relationships with parameter count, training data volume, and compute. 3B models typically perform reasonably well on text summarization, simple Q&A, and basic code completion, but significantly lag behind larger models on tasks requiring complex reasoning chains, multi-step logic, or precise spatial understanding.
Therefore, a 3B parameter model, regardless of quantization precision, simply lacks the capacity for SVG graphic generation tasks that demand strong spatial reasoning. The precision loss from quantization is likely a drop in the bucket compared to the model's inherent capability limitations.
Quantization Selection Recommendations for Local Deployment
For 3B-scale models on tasks like SVG generation, choosing a higher-precision quantization version does not yield noticeably better quality. This means that in practical deployment, developers can more boldly opt for smaller quantized versions to save VRAM and compute resources—at least for certain task types, without worrying too much about quality loss. Generally, the community rule of thumb is that Q4_K_M (4-bit medium-precision quantization) provides the best performance-to-size balance in most scenarios, while Q5_K_M and above are suitable for production environments with higher output quality requirements. But Simon's experiment reminds us that these rules of thumb assume the model already possesses sufficient baseline capability on the target task.
More Convincing Experiments Are on the Way
Simon indicated he would repeat this experiment in the future with a model that is better at drawing pelicans. This points to an important direction: when a base model already has sufficient capability on a specific task, the differences between quantization levels may become significant and informative. For example, running the same test with Granite 4.1's 30B version or other large-parameter models that excel at code generation tasks might reveal the true gradient of quantization loss—from Q8 to Q4 to Q2, the quality degradation curve would become much more clearly discernible.
Summary
The release of IBM Granite 4.1 adds a strong option to the open-source model ecosystem, and while Simon Willison's pelican experiment produced "disappointing" results, it revealed a practical insight: in domains where a model lacks fundamental capability, differences in quantization precision get drowned out by noise. Meaningful quantization quality comparisons need to be conducted on tasks where the model already excels to be informative. For developers focused on local LLM deployment, this experiment provides an important decision-making framework—first confirm whether the model itself can handle the target task, then consider the tradeoffs of quantization levels.
Key Takeaways
- IBM released the Granite 4.1 series of open-source models (Apache 2.0) in three sizes: 3B/8B/30B
- Unsloth released 21 GGUF quantization variants for the 3B model, ranging from 1.2GB to 6.34GB
- Testing all 21 quantized versions with an SVG pelican generation task showed no clear pattern between quality and quantization precision
- The 3B parameter model lacks sufficient capability for SVG spatial reasoning tasks, and quantization precision differences are masked by the model's inherent capability ceiling
- The experiment demonstrates that evaluating quantization impact requires choosing tasks where the model already has sufficient capability
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