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Yes—you can run some large language models locally without a discrete graphics card. CPU-only execution is supported, and newer acceleration paths can use certain non-discrete GPUs. The trade-off is performance: running a model is not the same as running it quickly, especially when the model is large.
What “running locally” means
With local inference, the model runs on your own computer rather than on a provider’s remote servers. A discrete GPU is one way to accelerate that work, but it is not a universal prerequisite. Ollama documents a CPU-only setup, and its Windows guidance says it can use CPU instruction sets such as AVX and AVX2 when the processor supports them.
That establishes a way to run models, not a speed guarantee for every computer or model. Ollama’s own guidance warns that large models are slow on a computer without a strong GPU. Ollama download guidance
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Three ways to run a model without a discrete GPU
CPU-only inference
Ollama’s Docker instructions include a CPU-only container command, so a compatible CPU can be enough to get started. Whether the experience is usable depends on the processor, the model, available memory, and how quickly you need responses. The documentation does not establish a universal minimum specification or a consistent CPU performance benchmark.
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Integrated or other supported GPU acceleration
Ollama 0.30, released June 5, 2026, enabled Vulkan by default, extending its GPU acceleration to a wider range of hardware, including AMD and Intel devices. That is a specific runtime release and vendor statement—not a guarantee that every integrated GPU is supported equally or will deliver the same performance. The release also expanded GGUF compatibility through llama.cpp. Ollama 0.30 release notes
Apple Silicon with MLX
Apple Silicon offers a different route: Ollama’s June 11, 2026 update describes an MLX engine that uses unified memory and the Metal-backed MLX framework. The post gives ollama run gemma4:12b-mlx as a setup example. Treat it as an example of the MLX path, not as a universal compatibility promise or a recommendation that every Apple Silicon machine can run that model well. Ollama’s MLX update
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What determines whether local inference feels practical
There is no single hardware number that settles the question. Consider the combination of execution route, model, memory, and response-time expectations:
- Execution route: CPU-only, a supported GPU acceleration path, or cloud inference.
- Model and format: Model size and file format affect what is practical. Ollama supports GGUF files through a Modelfile whose
FROMdirective points to a file or directory, then creating and running the model. Quantization can make some formats more practical, but the available guidance does not provide a general memory formula. - Available memory: Check memory as well as graphics hardware against the model you intend to run; no universal RAM threshold is established.
- Acceptable speed: A workload that can tolerate slower responses may be viable on a CPU where an interactive, demanding workload is not.
Ollama’s download guidance recommends checking both GPU and memory for the intended model. A computer with 32 GB of RAM may be one category worth evaluating, but that figure is not a stated requirement for local inference. Ollama download guidance
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When cloud inference is the better fit
If your computer cannot run the model you want with acceptable capacity or speed, cloud inference is an alternative. Ollama says its cloud models run on Ollama servers without requiring a local download. This changes where inference happens: unlike local execution, it uses remote servers. Ollama cloud models
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What performance claims do—and don’t—tell you
Ollama reported up to 20% faster performance on NVIDIA hardware in its June 5, 2026, version 0.30 update. That is a vendor-reported improvement for NVIDIA hardware, not a comparison showing how integrated graphics perform against CPUs.
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In a separate June 11, 2026 post, Ollama reported about 20% faster output for NVFP4 than q4_K_M, averaged over 10 runs with an 8,300-token input prompt on the stated Apple Silicon MLX setup. This result is specific to that setup and test; it should not be generalized to other computers or compared directly with the NVIDIA claim. Neither figure provides a universal CPU-versus-integrated-graphics benchmark.
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A practical way to choose
- Pick the model and workload first. Decide what you want to run and whether slower responses are acceptable.
- Check the available execution paths. Confirm whether your runtime supports CPU execution, acceleration for your graphics hardware, or a platform-specific engine such as MLX.
- Check memory and model format. Match the intended model to the computer’s available memory, and review whether a supported format or quantized variant fits your needs.
- Try local inference and judge the actual response time. Documentation can confirm a supported route, but it cannot predict the speed on every machine.
- Use cloud inference if the local trade-off does not work. Remote execution avoids downloading and running the model on your computer, but it is no longer local inference.
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