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Ollama 0.5 or newer runs Llama 3.3 70B and Mistral Large 2 locally on Ubuntu 24.04: Q4_K_M quantization lets a single NVIDIA GPU with 24 GB of VRAM, an RTX 4090 for example, handle the full model. This guide installs the drivers, sets up Open WebUI, and exposes the service behind Traefik with TLS.
A year ago open weights were a gamble; today they are a real production option. I review what has worked, what has not, and how Llama, DeepSeek, Qwen, and Mistral are fitting into enterprise architectures that used to depend on closed APIs.
Meta released Llama 3.2 with models as small as 1B and 3B parameters, built specifically to run on the device rather than in a datacentre. A look at what they can genuinely do, where the small sizes fall short, and how they compare with the alternatives.
Choosing an open LLM for enterprise in 2024 is no longer just Llama 2: Mistral, Mixtral, Qwen, Yi, DeepSeek, and Phi-2 all compete with different licences and sizes. The criteria that actually decide are commercial licence, available hardware, language support, and your own evaluation on real use cases, not just the trendy benchmark.
