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For local AI, 16GB of system RAM is a sensible starting point. If you plan to run a model on a discrete graphics card, 8GB of VRAM can handle small, quantized models; 12–16GB is a more practical middle tier; and 24GB or more gives you room to consider models around 27B parameters. These are starting bands, not guarantees: model format, context length, other running apps, and the amount of work sent to the CPU all affect memory use.
How much RAM do I need to run an AI model locally?
Start with 16GB of system RAM for a basic local-AI setup. Choose 32GB if you expect to multitask, use longer context windows, or run some model layers from system memory because they do not fit in GPU memory. Your actual requirement depends on the model and runtime, so treat these figures as planning guidance rather than a promise that every model will fit.
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System RAM and GPU memory do different jobs. System RAM supports the operating system, the AI application, and any model data handled by the CPU. VRAM is the memory on a discrete graphics card; it holds model data and working information when the GPU runs inference. A machine can have ample system RAM and still be constrained by a GPU with too little VRAM for the model you want to run efficiently.
Use the operating-system requirements as a floor, not a model-size guarantee
LM Studio’s 2026 published requirements recommend at least 16GB of RAM on Windows and at least 4GB of dedicated VRAM. For macOS, LM Studio specifies Apple Silicon and macOS 14 or newer, and recommends 16GB or more of RAM. It says Macs with 8GB may work with smaller models and modest context sizes. Those are application-level requirements, not a claim that every model will run well on hardware that meets them.
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For comfortable use rather than minimum compatibility, leave room for the system and other applications. A browser, code editor, or image tool can consume memory while the model is loaded. If available memory gets tight, the computer may have to shift work between memory pools or storage, which can make the experience less responsive. The amount of headroom needed varies with the workload.
What is the difference between RAM and VRAM for local AI?
RAM is the computer’s general-purpose working memory; VRAM is the dedicated memory on a discrete GPU. For GPU inference, available VRAM often determines whether a model can stay on the graphics card, which is usually preferable for performance. System RAM can help accommodate work that does not fit in VRAM, but that does not make it equivalent to having enough GPU memory.
- VRAM: Prioritize it when you want a discrete GPU to run the model. If the model and its working data fit, the GPU can handle the workload without relying as heavily on system memory.
- System RAM: It supports the operating system, applications, and CPU-side model work. More RAM can help with multitasking and partial CPU offload, but it does not remove the performance trade-off of moving work off the GPU.
- Unified memory: On Apple Silicon and other unified-memory systems, the CPU and GPU draw from a shared pool rather than separate system-RAM and VRAM pools. The operating system and applications also use that pool, so its headline capacity is not all available to the model.
Do not add RAM and VRAM together as if they were one interchangeable capacity. A computer with 16GB of RAM and an 8GB graphics card does not have a single 24GB pool available to every model. Whether memory is shared, and how a particular runtime uses it, depends on the hardware and software.
How much VRAM do different model sizes need?
For a discrete GPU, use 8GB as an entry point for small quantized models, 12–16GB as a practical middle tier for roughly 9B–12B models, and 24GB or more when considering models around 27B. Model size alone does not determine whether a model fits: quantization, context length, model architecture, and runtime overhead also affect the memory requirement.
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| Discrete GPU memory | Practical starting target | What to keep in mind |
|---|---|---|
| 6–8GB VRAM | Small models; NVIDIA’s 2026 model-selection playbook names Qwen 3.5 4B for this band. | An 8GB card is an entry point, not a guarantee for every 4B model or configuration. Context size and quantization matter. |
| 12–16GB VRAM | Roughly 9B–12B models; NVIDIA’s playbook names Qwen 3.5 9B or Gemma 4 12B. | Leave room for the runtime and working data. A large context or demanding model configuration can change what fits. |
| 24GB or more | Models around 27B; NVIDIA’s playbook names Qwen 3.6 27B for 24GB+. | This is a tier to consider, not a universal minimum for every 27B model. Check the exact model, quantization, and context you intend to use. |
These examples are NVIDIA’s 2026 model-selection guidance. They are useful as a starting map, not a cross-runtime compatibility table. Another NVIDIA playbook for llama.cpp gives different ranges: 8–16GB VRAM for Qwen 3.5 9B, 16–24GB for Qwen 3.6 35B, and 24–48GB for Qwen3.8 27B. The differences underline why a model name or parameter count cannot, on its own, establish a guaranteed memory requirement.
What do 7B, 14B, and 27B mean for my GPU?
A 7B model is a reasonable target to investigate on an 8GB card when using a suitable quantized version, but do not assume every format, context size, or runtime will fit. A 14B model is beyond the available data’s specific 9B–12B middle-tier examples, so a 12–16GB card should not be treated as a guaranteed fit; check the exact model’s memory requirements. For a model around 27B, 24GB or more is the practical tier to consider, with the same need to verify its format and intended context.
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Parameter counts describe model scale, not total runtime memory. A quantized model stores its weights using fewer bits than a higher-precision version, reducing the weight-memory footprint, but the model also needs memory for context and runtime work. Longer contexts and multimodal input can increase that demand. Because exact model files and configurations vary, use the figures as a first filter and check the specific model before installing or buying hardware.
How do context length and quantization change memory use?
Quantization reduces the memory used to represent model weights, which is why the VRAM bands above assume that smaller models may be run in a quantized form. The trade-off is that results and performance can vary with the particular model and quantization; the available data does not establish one best quantization level for every use case.
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Context length matters because a model must handle the conversation or other input it is processing, not just load its weights. A longer context can use more working memory, and the increase depends on the model and runtime. Vision or other multimodal features, multiple simultaneous sessions, and batch generation can add pressure as well. That means a setup that handles short text prompts may struggle when asked to process long documents, images, or several requests at once.
When comparing two model setups, compare the same model format and intended context length. Otherwise, a statement such as “this model fits in 8GB” may describe a small quantized file with a modest context, while your intended use requires more memory.
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Can I run a local model if it does not fit entirely in VRAM?
Some runtimes can place part of a model’s work in system RAM instead of keeping everything in GPU memory. This can make a model usable on hardware with less VRAM, but it shifts work away from the GPU and can reduce performance. How much can be offloaded and what performance to expect depends on the model, runtime, and hardware.
For that reason, CPU offload is better treated as a compatibility option than as a substitute for a GPU with adequate VRAM. If your priority is responsive GPU inference, size the card so the model configuration you intend to use can stay within its available memory. If you are willing to accept slower generation to run a model that otherwise would not fit, more system RAM may help, but no single RAM figure guarantees a satisfactory result.
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Is unified memory better than a separate graphics card for local AI?
Neither approach is automatically better. Unified memory can give the CPU and GPU access to a larger shared pool, which can be useful for models that would exceed a discrete card’s VRAM. A discrete GPU keeps its VRAM separate from system RAM, and its available VRAM is the key capacity to check for GPU inference. Choose based on the memory pool available to the model, the runtime you plan to use, and the workload—not the combined headline memory figures alone.
| Consideration | Unified-memory computer | Discrete-GPU computer |
|---|---|---|
| Memory available to the model | Shared pool used by the CPU and GPU; the operating system and applications take a share. | GPU work has a dedicated VRAM pool; system RAM remains separate. |
| Useful capacity check | Check total unified memory and allow for system and application use. | Check VRAM first for GPU inference; system RAM does not simply add to it. |
| Model-size guidance in the available data | NVIDIA lists RTX Spark systems with up to 128GB unified memory and capacity up to 120B parameters, and DGX Spark systems with up to 128GB unified memory and capacity up to 200B per device. | NVIDIA lists GeForce RTX systems with up to 32GB VRAM and capacity up to 50B parameters. |
| What the capacity claim means | NVIDIA’s 2026 table describes system and model capacity ceilings; it does not establish the performance or memory needs of every model configuration. | The same qualification applies: the listed capacity is not a guarantee for every model, context, or runtime. |
The listed capacities are NVIDIA’s published system guidance, not a direct speed comparison between products. A larger memory pool may allow a larger model to be loaded, but it does not by itself say how quickly that model will generate output. Runtime support, memory bandwidth, model settings, and workload also matter.
What GPU should I buy for local LLMs?
Choose a GPU by the largest model and context you realistically plan to run, then check that GPU’s VRAM and runtime support. For small quantized models, 8GB is an entry point. For the common middle tier of roughly 9B–12B models, target 12–16GB; for models around 27B, consider 24GB or more. If you are buying for a particular model, verify its memory needs in the intended format instead of relying on parameter count alone.
- Pick your workload: Decide whether you primarily need text chat, coding help, vision or other multimodal input, or batch generation. More demanding input, longer contexts, and concurrent work can require extra memory.
- Choose a model tier: Use 8GB, 12–16GB, and 24GB+ as initial VRAM bands for small, middle-tier, and roughly 27B-class models respectively.
- Check the exact model configuration: Confirm the quantization and context length you want to use. The same parameter count does not guarantee the same memory use across models or runtimes.
- Check runtime and driver support: Confirm that your chosen software supports the operating system and GPU. Ollama’s 2026 hardware documentation lists NVIDIA GeForce RTX 30-, 40-, and 50-series families, including the RTX 4090, subject to compute-capability and driver requirements.
- Leave headroom: Account for the operating system, applications, runtime overhead, and any extra work such as image input or concurrent requests.
For software choice, NVIDIA’s 2026 playbook describes llama.cpp as “Recommended” for users who want to control how models run on their hardware for maximum performance. That recommendation is guidance for that playbook, not evidence that one runtime is best for every user or model. Confirm the runtime’s current compatibility information before committing to hardware.
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How can I tell whether memory is the bottleneck?
Start by checking the model’s stated memory needs and your system’s actual available VRAM and RAM while the model is loaded. If the workload cannot fit on the GPU and the runtime shifts some work to the CPU, generation may be slower; if the operating system and applications are also short of memory, close unnecessary apps or choose a smaller model, shorter context, or lighter configuration.
- The model will not load: Try a smaller model or a more memory-efficient quantized version, and reduce the context setting if the runtime permits it.
- The model loads but feels slow: Check whether it is using CPU offload or whether other GPU workloads are competing for memory. A smaller model or GPU with more VRAM may be a better fit for performance-sensitive use.
- Short prompts work but long documents do not: Reduce the context target or choose a setup with more memory headroom.
- Text works but image input does not: Account for the extra demands of multimodal processing and test the exact model and runtime configuration you plan to use.
- Performance changes when other apps are open: Close memory-intensive apps and reassess. A model’s usable headroom is not just its file size.
These checks help distinguish a capacity problem from a configuration or workload problem. If you are evaluating a system before purchase, use the precise model, runtime, context, and input type you expect to use; generic parameter-count claims cannot replace that match.
Frequently Asked Questions
Does Ollama need a separate GPU to run a model locally?
The supplied Ollama hardware information identifies supported NVIDIA GeForce RTX families, but it does not say a discrete GPU is required for every Ollama workload. Check the current Ollama hardware and installation documentation for the specific operating system and device you plan to use.
Does a bigger model always give better answers?
Parameter count is a measure of model scale, not a guarantee of answer quality for a particular task. Model choice, quantization, prompt, and the task itself all matter, so test candidates on the work you actually need done.
Can I use local AI without an internet connection?
Whether a setup can work offline depends on having the model files and required software already available on the device. The hardware-memory figures alone do not establish download, licensing, or offline-use terms for a particular model.
How much storage space do I need for local AI models?
The memory figures in this guide are about RAM and GPU or unified memory, not disk capacity. Model-file storage varies by model and quantization, so check the download size of the exact file rather than inferring it from a RAM or VRAM tier.
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