A new Python library called OLLM is making waves in the AI community by allowing users to run massive language models on budget hardware. The tool enables running models with up to 80 billion parameters on consumer GPUs with just 8GB of memory - something that would normally require enterprise-grade hardware costing thousands of dollars USD.
 |
|---|
| Screenshot of the OLLM GitHub repository, showcasing the project's files and details about its capabilities |
Apple Silicon Compatibility Concerns Emerge
While OLLM shows impressive results on NVIDIA GPUs, Apple Silicon users are finding themselves excluded from this breakthrough. Community discussions reveal that Mac users with M-series chips cannot take advantage of OLLM's disk offloading capabilities, forcing them to rely on traditional quantized models that fit entirely in RAM. This limitation is particularly frustrating for users with 32GB of RAM who hoped to use OLLM's SSD offloading to run larger models during emergencies or special tasks.
The situation highlights a growing divide in AI accessibility between NVIDIA and Apple hardware ecosystems. While Mac users can still run large models using MLX-optimized versions at decent speeds (around 30-40 tokens per second), they miss out on OLLM's key innovation of running models that exceed their system's RAM capacity.
Performance Trade-offs Spark Debate
OLLM achieves its memory efficiency through aggressive offloading techniques, storing model weights and attention caches on SSD storage rather than keeping everything in GPU memory. However, this approach comes with significant speed penalties. The 80-billion parameter Qwen3-Next model runs at just 1 token every 2 seconds - a pace that has some users questioning whether the GPU provides any meaningful advantage over CPU processing at such speeds.
CPU is much slower than GPU. You can actually use both by offloading some layers to CPU... It is faster to load from RAM than from SSD.
The library's hybrid approach allows users to keep some layers in CPU memory for faster access while offloading others to disk, providing a middle ground between speed and memory usage.
Diffusion Model Applications Remain Unclear
Beyond language models, community members are exploring whether OLLM's techniques could benefit other AI applications like image generation. While the core concept of layer-by-layer weight loading could theoretically apply to diffusion models, the different architectures mean the current codebase wouldn't work directly. This represents an untapped opportunity for expanding memory-efficient AI inference beyond text generation.
The release demonstrates how creative engineering can democratize access to cutting-edge AI models, even as platform-specific limitations continue to fragment the user experience across different hardware ecosystems.
Reference: OLLM: LLM Inference for Large-Context Offline Workloads