To run a generative AI model locally, install an NVIDIA driver and the matching CUDA components, choose a model whose quantized weights and working memory fit your GPU’s VRAM, then launch it with a CUDA-aware runtime such as Ollama or llama.cpp. Finally, verify the GPU is doing the work with nvidia-smi or the runtime’s device information; a successful installation alone does not prove that inference is accelerated.
What you need before installing anything
A local model is practical only when three constraints line up: the GPU must support CUDA, the software stack must recognize that GPU, and the model must fit in available memory. A CUDA-capable NVIDIA GeForce RTX card is the usual desktop starting point, although professional and data-center NVIDIA GPUs can also run the same software with different memory capacities and driver considerations.
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Confirm CUDA capability
NVIDIA identifies GPU generations by compute capability. Check your exact GPU against NVIDIA’s current CUDA GPU list rather than assuming that every NVIDIA card supports the same path. A card missing from that list should not be treated as CUDA-compatible for this workflow. Compute capability affects which drivers, CUDA versions, kernels and precompiled binaries can run.
Measure usable VRAM, not just the advertised capacity
VRAM is shared by model weights, the key-value (KV) cache used for the conversation, temporary work buffers, graphics applications and, for vision-language models, a multimodal projector. Windows may reserve memory for the desktop; a display attached to the card can therefore leave less capacity than the number printed on the box. Close other GPU applications before a first test and leave headroom instead of planning to consume every last megabyte.
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Weight storage can be estimated as parameter count × bits per parameter ÷ 8, but that is only a lower bound. Quantization metadata, runtime buffers, the KV cache and context length add to it. Two launches of the same model can have different footprints when one uses a longer context, a larger batch or more simultaneous requests.
Use NVIDIA’s VRAM bands as planning guidance
NVIDIA’s current Run LLMs on Your NVIDIA Device playbook groups example model choices into the following planning bands. They are recommendations for matching workloads, not universal minimums: quantization, context length, batching, multimodal components and CPU offload can move a model into a different band.
| Available VRAM | How to interpret the band |
|---|---|
| 8–16 GB | Entry-level local models and aggressively quantized versions are the realistic starting point. Keep context and concurrent requests modest. |
| 16–24 GB | More parameters or less aggressive quantization become possible, with room for a useful context window on many text models. |
| 24–48 GB | Larger quantized models and some multimodal workloads are practical, provided projector and cache memory are included. |
| 48–128 GB | High-parameter models, longer contexts and heavier local serving loads have substantially more room. |
| 128–256 GB | Very large models or larger batches can be hosted, often on professional cards or multiple GPUs. |
| 256–384 GB | Multi-GPU deployments can keep a large fraction of a model on device while retaining cache and request headroom. |
| 384–500 GB | This range targets exceptionally large local deployments rather than ordinary desktop use. |
| 500 GB+ | Specialized multi-GPU or server configurations are required; plan power, cooling, PCIe topology and software placement together. |
When a model is close to the limit, first lower the quantization size or context length before concluding that the GPU is incompatible. A model can load successfully with one short prompt and fail later when the conversation grows because the KV cache expands.
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Choose a runtime for the job
The runtime determines how much setup you do, how directly you control GPU placement and whether you are building an application or simply using a local model.
| Runtime | Setup effort | Model and GPU control | Best fit |
|---|---|---|---|
| Ollama | Lowest friction: install a service and pull a model from its library. | Managed model packaging and device selection; less low-level tuning. | Interactive local use, a simple local API and first-time users. |
| llama.cpp | More manual: obtain a CUDA-enabled build and a compatible GGUF file. | Direct control over GPU layers, context, batching, server flags and CPU offload. | Developers who want a lightweight C/C++ runtime or precise hardware control. |
| TensorRT | Higher integration effort. | Highly optimized NVIDIA deployment after an engine is built for a supported model and configuration. | Performance-focused serving on NVIDIA hardware. |
| PyTorch | Environment and dependency management are part of your application. | In-process tensor, model and CUDA control. | Model development, experimentation and custom pipelines. |
| SGLang or vLLM | Server-oriented setup. | Scheduling, batching and API-serving controls for supported models. | Production-style serving rather than a single-user desktop chat. |
| WebNN or WebGPU | Browser and platform dependent. | Abstraction aimed at web applications rather than direct CUDA tuning. | In-browser or web-integrated inference where supported. |
NVIDIA describes Ollama as a simple local model service and llama.cpp as a lightweight runtime that works across operating systems and hardware. The same comparison separates those tools from TensorRT, PyTorch and production servers because their objectives differ; choosing the most specialized option is not automatically better for a personal workstation.
Install the NVIDIA and CUDA prerequisites
Windows
NVIDIA’s CUDA 13.4 installation guide lists the required CUDA-capable GPU, supported Windows release, supported Visual Studio version and CUDA Toolkit. Follow that guide’s compatibility table for your exact edition and GPU generation. Starting with CUDA 13.1, the NVIDIA display driver is installed separately from the Toolkit, so do not assume that installing the Toolkit alone updates the driver.
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- Install a current NVIDIA driver that supports your GPU and the CUDA version required by your chosen runtime.
- Install the CUDA Toolkit only if the runtime or your development workflow requires its compiler, libraries or headers. A packaged application may ship the CUDA components it needs.
- Restart Windows if the installer requests it.
- Open PowerShell and run
nvidia-smi. Confirm that the GPU name, driver version and available memory are displayed before installing a model runtime. - If you are compiling llama.cpp or another project, install the Visual Studio version named in the CUDA guide and enable the C++ desktop workload.
Linux
Use NVIDIA’s CUDA 13.4 Linux guide for the supported distribution, host compiler, repository or package method, environment setup and verification commands. Do not mix an untested distribution package with a different CUDA repository simply because the version number looks close; driver, kernel and user-space library compatibility matters.
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- Install the CUDA Toolkit or the runtime libraries required by your selected application.
- Reboot if the driver installation requires it, then run
nvidia-smi. - Check that the GPU is visible to the user account that will run the model and that no container, permission or remote-session restriction hides it.
- Only after this baseline succeeds, install Ollama, a prebuilt llama.cpp CUDA binary or a source build.
The installation guide’s supported-version tables change over time. Treat the CUDA version and driver relationship as a compatibility requirement, not as a suggestion to install the newest Toolkit indiscriminately.
Fastest path: run a model with Ollama
Ollama is the simplest route when you want a local service without manually managing model files and launch flags.
- Install the Windows or Linux release appropriate to your operating system.
- Open a terminal and pull a model tag listed in the current Ollama library:
ollama pull <model-tag>. - Start an interactive session with
ollama run <model-tag>. - Send a short prompt first. This establishes whether the model loads before you spend VRAM on a long context or a large batch.
- For an application, use Ollama’s local HTTP service and the API format documented for the installed release. Keep the service bound to a trusted interface unless you deliberately configure authentication and network access.
Model tags, quantization choices and command-line options can change in the library, so select the exact tag shown by the installed client rather than copying an old tutorial’s name. If several NVIDIA GPUs are installed, Ollama documents CUDA_VISIBLE_DEVICES for selecting which devices it may use. On Linux, suspend and resume can occasionally cause Ollama to miss the GPU and fall back to CPU; check device visibility again after resuming.
More control: run a GGUF model with llama.cpp
llama.cpp uses GGUF model files and exposes launch controls that are useful when VRAM is tight or when you need a predictable server configuration. Obtain a GGUF file that matches the model architecture and a quantization level appropriate for your memory budget. A file intended for a different architecture or an incomplete download will fail regardless of CUDA setup.
Install or obtain a CUDA-enabled build
- Choose a llama.cpp release or build method that explicitly includes CUDA support for your operating system.
- Verify the build by checking its help output for CUDA-related options such as GPU-layer offload. If those options are absent, you have a CPU-only binary.
- Place the GGUF model in a path without accidental renaming or partial-download suffixes.
Launch a text model
A typical Windows server command is:
llama-server.exe -m "C:modelsmodel.gguf" -ngl 999 --host 127.0.0.1 --port 8080The equivalent interactive program is:
llama-cli.exe -m "C:modelsmodel.gguf" -ngl 999-ngl 999 asks the runtime to offload as many layers as possible; the actual number is limited by VRAM. If loading fails, replace it with a smaller layer count and watch memory use. Option names can differ between llama.cpp releases, so check the executable’s built-in help.
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Launch a multimodal model
Vision-language models generally need both a GGUF language model and a compatible multimodal projector. A representative server layout is:
llama-server.exe -m "C:modelsmodel.gguf" --mmproj "C:modelsmmproj-f16.gguf" -ngl 999 --host 127.0.0.1 --port 8080The projector must match the model family and build expectations. It consumes additional memory, so a text model that fits may not fit with image input enabled. NVIDIA’s current playbook demonstrates this pattern with a Windows llama-server.exe launch, a Hugging Face GGUF model and a projector; use the playbook’s current file names and URLs when reproducing that example.
Prove that CUDA is actually being used
Do not treat a clean install or a responsive chat window as proof of acceleration. Verify during model loading and generation.
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nvidia-smirepeatedly while the model loads and generates. Look for the inference process, rising VRAM allocation and GPU utilization. - On Windows, open Task Manager, select the GPU under Performance and choose a compute or CUDA engine graph where available. A 3D graph alone is not evidence of model execution.
- Check the runtime log. A CUDA device name, allocated GPU layers or a message that the model was placed on an NVIDIA device confirms selection more directly than elapsed time.
- Run the same short prompt twice after the model is warm. Compare device utilization and memory, not an unsupported universal tokens-per-second claim; speed varies with GPU, model, quantization, context, batch size and runtime.
- If you have multiple GPUs, set the documented device mask before starting the process, for example
CUDA_VISIBLE_DEVICES=0on Linux or the corresponding environment variable in PowerShell, then verify that the intended GPU’s memory changes.
A CPU fallback can still produce correct text, but it will usually consume system RAM and show little or no additional NVIDIA memory. Investigate that state before tuning prompts or blaming the model.
Make a model fit when loading fails
Apply changes in this order so you know which constraint was responsible:
- Confirm compatibility. Recheck compute capability, driver version, CUDA libraries and whether the binary was built with CUDA.
- Use fewer parameters. Move to a smaller model family or parameter count that belongs in your GPU’s VRAM band.
- Choose a more aggressive quantization. Lower-bit weights use less memory but can change output quality. Select quantization together with model size and context requirements, not as an afterthought.
- Shorten the context. A large context increases the KV cache, sometimes after the initial load appears successful.
- Reduce concurrency and batch size. Multiple simultaneous requests multiply cache and temporary-buffer demand.
- Offload layers to the CPU. In llama.cpp, lower the GPU-layer count so the remainder stays in system memory. This can make an otherwise runnable model slower.
- Disable optional multimodal components. Remove the projector and test the text model alone when image support is not required.
Keep several gigabytes of unallocated VRAM where possible. Operating-system graphics allocations, allocator fragmentation and a longer-than-expected prompt can otherwise turn a marginally successful launch into an out-of-memory error.
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Troubleshoot common failures
The command says no CUDA device is found
- Run
nvidia-smi. If it fails, fix the driver or kernel installation before touching the model. - Confirm that the GPU appears in NVIDIA’s supported CUDA list and that the runtime’s build includes CUDA.
- On Linux, check whether a container, remote session or user permission prevents access to
/dev/nvidia*. - After suspend or resume, restart the service or reboot if the runtime no longer sees the device.
The model loads but runs on the CPU
- Watch
nvidia-smiduring generation rather than relying on an idle-time check. - Inspect the runtime log for a CUDA backend and GPU-layer allocation.
- Remove a restrictive device mask, or set
CUDA_VISIBLE_DEVICESto the intended GPU. - Use a CUDA-enabled llama.cpp build or the supported NVIDIA installation path for the managed runtime.
Loading stops with an out-of-memory error
- Reduce model size or quantization level, then retry with a short context.
- Lower GPU-layer offload in llama.cpp or permit CPU offload.
- Close other applications using VRAM and disable unnecessary multimodal components.
- Reduce concurrent requests and batch size in a server deployment.
Generation is unexpectedly slow
- Confirm that the GPU is active and that the intended device, not an integrated adapter, is selected.
- Check whether most layers were forced onto the CPU by a low offload setting.
- Reduce context length and concurrent work if cache growth is causing memory pressure.
- Compare only within a fixed configuration. There is no meaningful generic speed number that applies to every NVIDIA GPU, model, quantization, context and runtime.
A GGUF or projector will not open
- Verify that the download completed and that the file is not a partial or renamed archive.
- Use a GGUF architecture supported by the installed llama.cpp version.
- For multimodal inference, obtain a projector made for the same model family and compatible runtime.
Decide whether you need a service, an application or a server
Personal chat or a small local API
Use Ollama when you value a managed library, quick installation and a consistent local service more than per-layer tuning. It is the lowest-friction choice for testing several models and connecting a desktop application to one local endpoint.
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Hardware experiments and precise placement
Use llama.cpp when you need to select GPU layers, adjust context and batching, combine GPU and CPU memory, or run a lightweight command-line or HTTP server from a GGUF file. Its extra setup buys visibility into the decisions that a managed runner hides.
Model development
Use PyTorch when the model is part of Python code, training, fine-tuning or a custom preprocessing pipeline. This route requires you to manage the environment and CUDA-compatible packages, but it exposes the tensors and operations directly.
Production-style serving
Consider SGLang or vLLM when scheduling, batching, throughput and an API-serving architecture matter more than a one-user desktop workflow. Consider TensorRT when you are prepared to build and maintain an NVIDIA-optimized deployment for a supported model. These choices increase operational complexity and should follow a demonstrated serving requirement.
Security, updates and maintenance
- Keep the NVIDIA driver and runtime versions aligned; update one component only after checking the other’s compatibility.
- Bind a local server to
127.0.0.1unless you intentionally need network clients. A model endpoint exposed on a LAN or the internet needs authentication, access control and an update plan. - Store model files on a drive with enough space for the weights, alternate quantizations and temporary downloads.
- Record the model tag or GGUF filename, quantization, context length, runtime version, driver version and GPU used. This makes a later regression diagnosable.
- Re-run the GPU verification after driver updates, operating-system upgrades and suspend/resume events.
NVIDIA describes an ecosystem with more than 400 CUDA-X libraries and a stated installed base of 150 million local NVIDIA GPUs. Those figures describe ecosystem scale, not a guarantee that a particular model will be fast or that every card supports every CUDA feature.
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- Identify the exact NVIDIA GPU and check its CUDA compute capability.
- Record usable VRAM with other GPU applications closed.
- Install the supported driver and, where required, the CUDA Toolkit for your operating system.
- Run
nvidia-smisuccessfully before installing a model runtime. - Select a runtime based on your goal: Ollama for the simplest managed service, llama.cpp for direct GGUF and GPU-layer control, PyTorch for development, or a production server for multi-request serving.
- Choose a quantized model in the appropriate NVIDIA VRAM planning band and account for context, batching and any projector.
- Start with a short prompt and conservative concurrency.
- Watch GPU memory and utilization while the model loads and generates.
- If it falls back to CPU or runs out of memory, fix compatibility first, then reduce model size, quantization, context, concurrency or GPU-layer count.
Once those checks pass, you have a reproducible local CUDA workflow rather than merely an installed application: the model, runtime, driver and memory plan are all known, and GPU use is observable during inference.
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