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Yes—you can run some local language models without a discrete graphics card. Ollama documents CPU-only inference, and newer acceleration options can use certain AMD and Intel devices or Apple Silicon. But “it runs” does not mean “it runs quickly”: Ollama warns that large models can be slow without a strong GPU. The practical choice depends on the model, format, available memory and how long you are willing to wait for a response.
What “no discrete GPU required” actually means
A discrete GPU is not a prerequisite for every local LLM workflow. Ollama’s official Docker instructions include a CPU-only setup, while its Windows guidance says the software can use CPU instruction sets such as AVX and AVX2 when the processor supports them. Those documents establish that CPU execution is possible; they do not promise a particular speed or that every model will be practical on every computer. Ollama’s Docker instructions and its Windows guidance describe these routes.
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It also helps to distinguish a discrete GPU from other forms of acceleration. Ollama 0.30, released June 5, 2026, says Vulkan is enabled by default to extend GPU acceleration to a wider range of hardware, including AMD and Intel devices. That is a release-specific statement, not a guarantee that every integrated GPU is supported equally or will deliver the same results. The release also expands GGUF compatibility through llama.cpp. Ollama 0.30 release notes
Choose an execution route
CPU-only
CPU inference is the simplest answer when a computer has no suitable GPU acceleration path. It can be useful for trying a model or handling a workload where slower responses are acceptable. Ollama’s guidance is explicit about the trade-off: “Large models are slow on a computer without a strong GPU.” That is product guidance from Ollama, not an independent performance test. Ollama download guidance
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AMD or Intel acceleration through Vulkan
Ollama 0.30’s default Vulkan support broadens the potential acceleration options for AMD and Intel hardware. Whether a particular machine benefits depends on its hardware and software environment; the release announcement does not establish uniform support or comparative performance for all integrated graphics. Ollama 0.30 release notes
Apple Silicon with MLX
Apple Silicon is a separate route rather than a general claim about integrated graphics. In a June 11, 2026 update, Ollama described its MLX engine as using Apple Silicon unified memory and the Metal-backed MLX framework. Its example command is ollama run gemma4:12b-mlx. Treat that as an example of a supported setup, not a universal capacity recommendation for every Apple computer. Ollama’s MLX update
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Cloud inference
If local hardware cannot provide the capacity or response speed you need, a cloud model is another option. Ollama says its cloud models run on Ollama servers and do not need to be downloaded locally. That changes where inference runs: it is not local execution on your computer. Ollama download guidance
What determines whether a model is practical?
- Model and format: Model size and format affect what is feasible. Ollama supports GGUF files by letting you point a Modelfile’s
FROMdirective at a file or directory, then create and run the model. Quantization can make some formats more practical, but there is no universal memory formula established here. Ollama 0.30 release notes - Available memory: Memory is a constraint to consider alongside the intended model and workload. Ollama recommends checking both GPU and memory against the model you plan to use; the available guidance does not set a universal minimum RAM figure. Ollama download guidance
- Workload and response speed: A small or occasional task with patient wait times is a different target from frequent long prompts or fast, interactive output. The reviewed guidance does not provide cross-platform benchmark results to predict a specific machine’s speed.
For a local GGUF file, Ollama’s documented flow is to create a Modelfile whose FROM line points to the model file or its directory, create an Ollama model from that Modelfile, and run it. Consult the linked GGUF guidance for the exact commands and format details for your file. Ollama 0.30 release notes
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How to decide before committing to local use
- Pick the model and intended task first. A machine should be judged against the model and workload you actually want, not a generic claim that it “runs LLMs.”
- Identify the available route. Determine whether you will use CPU-only execution, a supported Vulkan acceleration path, or—on Apple Silicon—the MLX engine.
- Check memory and expected speed. Use Ollama’s guidance to check the target model against available GPU and memory, then decide whether the likely response time is acceptable. No universal RAM cutoff or CPU-versus-integrated-graphics benchmark is established by the cited material. Ollama download guidance
- Use cloud inference if the local trade-off does not work. Cloud models run on Ollama’s servers rather than being downloaded to the computer. Ollama download guidance
What the published performance figures do—and don’t—show
Ollama’s June 5, 2026 release post reports up to 20% faster performance on NVIDIA hardware for its 0.30 update. That is a vendor-reported improvement for NVIDIA, not evidence that integrated graphics match a discrete GPU. Ollama 0.30 release notes
Separately, Ollama’s June 11, 2026 MLX post reports about 20% faster output for NVFP4 than q4_K_M, averaged over 10 runs with an 8,300-token input prompt on its stated Apple Silicon MLX setup. This specific result should not be generalized to other hardware or compared directly with the NVIDIA figure. Ollama’s MLX update
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These are vendor-reported results, not independent cross-platform comparisons. The cited material does not establish a universal memory threshold or consistent CPU-versus-integrated-GPU performance measurements.
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