Hello, Has anyone here used the Ubuntu VM AI development package? I understand that it includes Ollama pre-installed. Does anyone know if Ollama inside the Ubuntu VM can utilize the Apple silicon GPU? Thanks, Hubert
Yes, I've worked with the Ubuntu VM AI development package a few times. From what I've seen, Ollama running inside the Ubuntu VM doesn't directly utilize the Apple Silicon GPU -- mainly because the virtualized environment can't access Apple's Metal API. The GPU acceleration is limited to what the VM layer can expose, so most of the heavy lifting still happens on the CPU side. If you're looking for better GPU performance on Apple Silicon for AI workloads, I'd suggest running Ollama natively on macOS instead of inside a VM. That setup allows direct GPU access, and you'll notice a big speed improvement for model inference. I ran into the same issue when testing local LLM setups for client AI apps at Guru Technolabs, where we handle end-to-end AI development and optimization. Running Ollama natively has consistently given us the best performance results.
Yes, the Ubuntu VM AI development package does include Ollama pre-installed, which makes it convenient for AI model deployment and testing. However, when running inside a virtual machine (VM) on Apple Silicon (like M1 or M2 chips), Ollama cannot directly utilize the Apple GPU for acceleration because GPU access is restricted within virtualized environments. If you need full GPU support, it's best to run Ollama natively on macOS rather than inside a VM. For more tech insights, AI tools, and digital growth strategies, visit Virtual Real Design -- Best Digital Marketing Agency.
GPU utilization in AI development environments often comes down to configuration rather than hardware limits. In Linux virtual machines, proper GPU passthrough and compatible drivers are essential for frameworks like TensorFlow or PyTorch to recognize and use the GPU efficiently. I've noticed that many users assume their AI package is GPU-enabled, but without matching CUDA, cuDNN, and driver versions, workloads silently fall back to CPU. Verifying GPU access with system-level tools before training models can save a lot of time. From a broader perspective, optimized AI workflows are becoming increasingly important, especially as AI-driven tools are now widely used in digital marketing services such as campaign automation, audience analysis, and performance prediction. Efficient GPU usage directly impacts processing speed and scalability in these applications. It would be interesting to hear how others are balancing VM-based GPU setups versus cloud or containerized solutions for AI-related projects. Do you want know more about Digital marketing Services visit website : https://tribhadigital.com/blog/
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