Ollama has launched a significant update to its local AI platform, introducing a proprietary engine designed to enhance multimodal model support. This development signals a strategic shift from its prior reliance on the llama.cpp framework. The new engine aims to deliver improved performance, reliability, and accuracy for users running AI models that interpret both text and images directly on their own hardware, as detailed in the company’s official announcement.
The new engine‘s primary goal, as Ollama explained, is to better handle the increasing complexity of multimodal systems, which combine diverse data types. This initiative seeks to provide a more stable and efficient foundation for current vision models—such as Meta’s Llama 4, Google’s Gemma 3, Alibaba’s Qwen 2.5 VL, and Mistral Small 3.1—and paves the way for future capabilities. These include speech processing, AI-driven image and video generation, and expanded tool integration, promising a more robust local AI experience. The release also noted functional updates like WebP image support.
Ollama’s move to an in-house engine addresses the difficulties of integrating diverse multimodal architectures. The company explained its rationale, stating, “as more multimodal models are released by major research labs, the task of supporting these models the way Ollama intends became more and more challenging.”
This difficulty arose within the confines of the existing ggml-org/llama.cpp project. The new architecture emphasizes model modularity; according to Ollama, the aim is to “confine each model’s “blast radius” to itself—improving reliability and making it easier for creators and developers to integrate new models.” This design, with examples available on Ollama’s GitHub repository, allows each model to be self-contained with its own projection layer, thereby simplifying integration for model creators.
Architecture and Performance Enhancements
A core tenet of Ollama’s new engine is the pursuit of greater accuracy in local inference, particularly when processing large images that can translate into a substantial volume of tokens. The system now incorporates additional metadata during image processing. It is also engineered to manage batching and positional data more precisely, as Ollama highlights that incorrect image splitting can negatively impact output quality.
Memory management also sees significant improvements. The engine introduces image caching, ensuring that once an image is processed, it remains readily accessible for subsequent prompts without being prematurely discarded. Ollama has also rolled out KVCache optimizations—a technique to speed up transformer model inference by caching key and value states.
Furthermore, the company is actively collaborating with hardware giants like NVIDIA, AMD, Qualcomm, Intel, and Microsoft. This partnership aims to refine memory estimation through accurate hardware metadata detection and involves testing Ollama against new firmware releases.
Specific adaptations have been made for models like Meta’s Llama 4 Scout—a 109-billion-parameter mixture-of-experts (MoE) model where different parts of the input are processed by specialized sub-models—and Maverick, incorporating features such as chunked attention (processing sequences in segments to save memory) and specialized 2D rotary embedding (a method for encoding positional information in transformers).
Context in the Evolving Local AI Ecosystem
Ollama’s announcement lands amidst a period of dynamic evolution in the open-source local AI sphere. Notably, the llama.cpp project itself recently integrated comprehensive vision support via its new `libmtmd` library. The llama.cpp documentation describes its own multimodal support as a rapidly developing sub-project.
The relationship between Ollama and the foundational llama.cpp project has been a point of discussion within the user community. In a Hacker News thread dissecting Ollama’s announcement, some participants sought clarity on what was fundamentally new.
Patrick_Devine, a member of the Ollama team, clarified their development process, explaining, “we did our implementation in golang, and llama.cpp did theirs in C++. There was no “copy-and-pasting” as you are implying.” He added that their work was done in parallel with llama.cpp, not based on it, and acknowledged, “I am really appreciative of Georgi catching a few things we got wrong in our implementation.”
Another user in the discussion, ‘nolist_policy’, highlighted a specific technical advantage, claiming, “For one Ollama supports interleaved sliding window attention for Gemma 3 while llama.cpp doesn’t. iSWA reduces kv cache size to 1/6.” referencing a GitHub issue for further context. Interleaved Sliding Window Attention (iSWA) is an efficiency technique for transformer models.
Future Capabilities and Broader Implications
With its new engine now operational, Ollama is setting its sights on further expanding its platform’s capabilities. The company’s roadmap includes ambitions to support significantly longer context sizes, enable more sophisticated reasoning processes within the models, and introduce tool calling with streaming responses. These planned enhancements aim to make locally run AI models more versatile and powerful across a broader spectrum of applications.
This strategic pivot by Ollama to develop a custom engine underscores a wider trend in the AI industry towards specialized tooling required to fully leverage the potential of multimodal AI. By asserting greater control over the inference pipeline, Ollama intends to offer a more streamlined and dependable platform for both developers and end-users who wish to utilize advanced AI models on their personal computing devices.
However, while users benefit from enhanced multimodal tools, such advancements could also present new avenues for misuse, such as in the creation of forged documents or manipulated digital imagery.