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For most local AI users, 16GB of system RAM is the practical starting point. A discrete GPU with 8GB of VRAM can run small, quantized models; 12–16GB is a useful middle tier for models around 9B–12B parameters; and 24GB or more is the level to consider for models around 27B. Those figures describe a workable baseline, not a guaranteed fit: context length, quantization, vision inputs, other running applications, and CPU offload all consume additional memory. Plan for headroom rather than matching the model file’s size exactly.
What memory does a local AI model actually use?
A local model uses several kinds of memory at the same time. The weights are the largest item, but they are not the only one. The runtime also needs space for the model’s working buffers, the conversation’s key-value (KV) cache, tokenizer and application overhead, and any operating-system or graphics tasks sharing the machine.
- Model weights: the parameters loaded from the model file. Quantization reduces their footprint by storing values at lower precision, so two versions of the same model can require very different amounts of memory.
- KV cache: memory used to retain the current prompt and conversation. A longer context window increases this allocation as the session grows.
- Runtime overhead: temporary buffers, kernels, and the front-end application need room in addition to the weights.
- System and application memory: your operating system, browser, editor, documents, and background services compete for RAM. On unified-memory computers they also compete with the GPU workload for the same pool.
That is why a model that appears to be smaller than your VRAM can still fail to load, reduce context length, or spill layers into system RAM. The practical question is not only “How large is the model file?” but “How much free memory remains after the model, context, runtime, and everything else are accounted for?”
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How much system RAM should you install?
Use 16GB as the minimum sensible target and 32GB as the safer choice. Sixteen gigabytes is the baseline cited by current local-AI software guidance, while 32GB leaves room for long contexts, multitasking, and partial CPU offload.
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| System RAM | Best fit | What to expect |
|---|---|---|
| 8GB | Small models and light, single-purpose use | Little headroom for the operating system, browsers, long prompts, or offloaded layers. Current Mac guidance says 8GB systems may work only with smaller models and modest context sizes. |
| 16GB | Practical starting point for local text models | Suitable for small to midrange quantized models when you close unnecessary applications and keep context targets reasonable. |
| 32GB | Multitasking, longer contexts, coding, and CPU offload | More room for the operating system, development tools, document retrieval, and layers that do not fit in VRAM. |
| 64GB or more | Large unified-memory systems, heavier offload, or batch work | Useful when several models or large contexts share memory, but it does not remove a discrete GPU’s VRAM ceiling when the runtime requires dedicated graphics memory. |
LM Studio’s 2026 requirements recommend 16GB or more. On Windows it also recommends at least 4GB of dedicated VRAM. On macOS it specifies Apple silicon with macOS 14 or newer and notes that 8GB Macs are limited to smaller models and modest context sizes. These are software recommendations, not a promise that every model will fit.
How much GPU VRAM do common local-model sizes need?
For a discrete GPU, VRAM usually sets the performance ceiling. Keeping all or most model layers in VRAM avoids the slower transfer path to system RAM. The following bands are practical starting points from NVIDIA’s 2026 model-selection guidance; actual results vary with quantization, context length, architecture, and runtime overhead.
| GPU memory | Representative model tier | Typical use | Important qualification |
|---|---|---|---|
| 6–8GB VRAM | Qwen 3.5 4B | Small chat, summarization, lightweight coding, and local experimentation | Long contexts, vision inputs, and other GPU applications can push a model over the limit. |
| 8–16GB VRAM | Qwen 3.5 9B | General-purpose text and coding at a common midrange size | The RTX Spark llama.cpp guidance gives this range; lower-memory cards may need a smaller context or more CPU offload. |
| 12–16GB VRAM | Gemma 4 12B | More capable reasoning and coding than small 4B–9B models | Leave room for the KV cache and runtime rather than allocating every gigabyte to weights. |
| 16–24GB VRAM | Qwen 3.6 35B on RTX Spark guidance | Larger-model experimentation when the runtime supports split or unified memory | This is a guidance range, not a guarantee; quantization and context determine the actual load. |
| 24GB or more | Qwen 3.6 27B | 20B–30B-class text and coding models | 24GB opens this tier, but long contexts, multimodal inputs, and concurrent applications may still require more. |
| 24–48GB VRAM | Qwen3.8 27B on RTX Spark guidance | Higher headroom for a 27B-class workload | The published range reflects configuration differences; it should not be read as a fixed requirement. |
NVIDIA’s broader local-AI table describes GeForce RTX systems with up to 32GB of VRAM and model capacity up to 50B parameters. That capacity figure is a platform description, not a recommendation that every 50B model will run at every context length or speed.
Can you run a 7B, 14B, or 27B model on your GPU?
Often, but the parameter label alone is not enough to decide. A 7B or similarly sized model is generally in the small-to-midrange category; a 14B model usually needs more memory than a 9B model; and a 27B model belongs in the 24GB-plus planning tier. Quantization can make a model fit where a full-precision copy would not, while a long context can consume the headroom that made the initial load possible.
For a 7B-class model
An 8GB card may be workable for a quantized, text-only build with a restrained context. A 12GB or 16GB card gives more room for the KV cache, a larger context, and background graphics tasks. If the runtime reports that layers were offloaded to CPU, expect lower throughput than an all-GPU load.
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For a 14B-class model
Plan around the 12–16GB tier only after checking the model’s quantized variant and context target. A 16GB card is a more comfortable starting point than an 8GB card, and 32GB of system RAM helps if some layers must be placed in CPU memory.
For a 27B-class model
Start at 24GB of VRAM and verify the runtime’s memory report before increasing context. The 24GB figure is an entry point for this class, not a universal ceiling: multimodal versions, large KV caches, or higher-precision weights can push the requirement into the 32–48GB range.
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Is unified memory better than a separate graphics card?
Unified memory trades a discrete VRAM limit for a shared pool, but the operating system and applications consume that pool. Apple silicon systems and NVIDIA’s RTX Spark and DGX Spark platforms are examples of architectures where CPU and GPU workloads can draw from the same memory pool. This can let a model use more total memory than a small discrete card, but it does not make memory free or guarantee discrete-GPU-class speed.
| Architecture | Memory characteristic | When it helps | Trade-off |
|---|---|---|---|
| Discrete GPU | Dedicated VRAM plus separate system RAM | Fast inference when the model remains in VRAM; easy to compare cards by VRAM capacity | A model larger than VRAM may spill into RAM, and the transfer path can reduce performance. |
| Unified memory | One pool shared by CPU, GPU, operating system, and applications | Large models can fit without a separate VRAM boundary; useful for compact workstations | Every desktop application reduces memory available to the model, and bandwidth and software support still determine speed. |
NVIDIA lists RTX Spark systems with up to 128GB of unified memory and model capacity up to 120B parameters, and DGX Spark systems with up to 128GB and capacity up to 200B parameters per device. Those are platform-level capacities; the usable amount depends on the installed configuration, operating system, context, quantization, and workload.
What should you check before buying a GPU for local LLMs?
Match the card to the model tier you will actually use, then reserve memory for the rest of the workload. Use this checklist before comparing prices or performance:
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- Model and quantization: identify the exact model family, parameter size, and quantized format you intend to run.
- Context target: decide whether you need short chats, long documents, or coding sessions with large files. A larger context increases KV-cache usage.
- Modality: vision and other multimodal inputs require additional buffers beyond a text-only session.
- Concurrency: serving multiple users or generating several requests at once multiplies working memory.
- VRAM headroom: avoid choosing a card whose advertised capacity is already consumed by the model weights.
- System RAM: use 16GB as a floor and 32GB when you expect offload, development tools, or heavy multitasking.
- Runtime support: confirm that your operating system, drivers, compute capability, and inference engine support the card.
- Power and cooling: a higher-memory card may require a stronger power supply, case airflow, and physical space.
For NVIDIA hardware, Ollama’s documentation lists GeForce RTX 30-, 40-, and 50-series families, including the RTX 4090, subject to the required compute capability and drivers. Support for a card does not imply that every model will fit or that every backend will deliver the same speed.
How do you estimate a workable configuration?
Use a staged estimate instead of treating the model-file size as the specification.
- Choose the model tier. Start with the parameter band you need: small (around 4B), midrange (around 9B–12B), or larger (around 27B and above).
- Select the quantized build. Compare the published memory requirement for the exact quantization and runtime. Do not substitute a full-precision file’s size for a quantized file’s requirement.
- Add context headroom. Increase the memory allowance for the context window you plan to use, especially for document analysis or coding.
- Reserve operating memory. Keep system RAM available for the operating system, browser, editor, and any retrieval or document tools.
- Check the runtime’s allocation report. After loading, verify how much VRAM is used and whether any layers were placed in CPU memory.
- Test the real workload. Run the longest prompt, largest document, or concurrent request pattern you expect. A model that loads in an empty desktop may fail during normal use.
The goal is a stable configuration, not the largest model that can be forced into memory. If loading succeeds only after reducing context or closing every application, move up a memory tier.
What happens when the model does not fit?
A runtime may refuse to load, automatically offload layers to system RAM, reduce the usable context, or begin swapping. These symptoms usually indicate insufficient headroom rather than a damaged model.
Load failure or out-of-memory error
Try a smaller quantized variant, lower the context length, close GPU-accelerated applications, or choose a card with more VRAM. Increasing system RAM helps only when the runtime supports CPU offload; it cannot increase the physical VRAM on a discrete card.
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Very slow generation
Check whether layers were offloaded to CPU memory. Keeping the model on the GPU is generally faster, so reduce the model or context, or move to a higher-VRAM card. Also check that the correct driver and hardware backend are active.
It works for short prompts but fails on long documents
The KV cache grows with context. Lower the context target, process the document in chunks, or select a configuration with more memory. A successful short chat does not prove that the advertised maximum context is practical on your machine.
Performance changes when other applications open
On discrete systems, close applications that reserve VRAM. On unified-memory systems, close memory-heavy applications and monitor total shared-memory pressure; the model and the desktop are drawing from the same pool.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which configuration is a sensible starting point?
| Goal | Recommended starting memory | Why |
|---|---|---|
| Experiment with small local models | 16GB system RAM and 8GB VRAM | Targets small quantized models such as the 4B class while retaining modest desktop headroom. |
| Everyday text and coding | 32GB system RAM and 12–16GB VRAM | Fits the common 9B–12B tier with more room for context and development tools. |
| Larger 20B–30B-class models | 32GB or more system RAM and at least 24GB VRAM | Provides the practical starting point for around 27B, subject to quantization and context. |
| Large unified-memory workstation | At least 64GB shared memory when budget and software support justify it | Useful for workloads that exceed ordinary discrete-card VRAM, while remembering that the operating system shares the pool. |
Choose the smallest tier that meets your workload with headroom. Buying for a model size you may never use usually costs more and can add power, heat, and driver complexity without improving the tasks you actually run.
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Does more RAM make local AI faster?
More RAM primarily prevents failures and excessive offload; it does not automatically increase generation speed. If the model already fits entirely in VRAM and the system has adequate RAM, adding memory may have little effect on token throughput. It becomes valuable when you run long contexts, keep several applications open, use retrieval or coding tools, or must place some model layers in CPU memory.
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Likewise, more VRAM is useful only when the runtime can use it. A card with a larger memory pool can accommodate a larger model or context, but generation speed also depends on GPU architecture, memory bandwidth, software backend, quantization, and the number of simultaneous requests.
What is the bottom-line buying rule?
Start with 16GB of system RAM, move to 32GB if you multitask or expect CPU offload, and choose VRAM by model tier: 8GB for small models, 12–16GB for common midrange models, and 24GB or more for roughly 27B-class models. Treat those as planning bands, then verify the exact quantization, context length, runtime, and operating system before purchase.
Frequently Asked Questions
Can I run local AI without a dedicated GPU?
Yes, a CPU-only runtime can load models into system RAM, but generation is generally slower than keeping the workload on a supported GPU. The practical RAM requirement still depends on the model, quantization, context, and applications running alongside it.
How much memory does a local vision model need?
Vision and other multimodal models need additional buffers for image or media processing, so they can exceed the memory used by a text-only version of the same parameter size. Plan extra headroom and test with the largest image or batch you expect to process.
Does lowering the context window reduce memory use?
Yes. A shorter context reduces KV-cache growth, which can turn an otherwise marginal configuration into a usable one. The trade-off is that the model can retain less conversation or document content in a single session.
Can two GPUs combine their VRAM for one model?
Some runtimes can split layers across devices, but the result depends on backend and driver support and may involve communication overhead. The capacities should not be assumed to add together with the same performance as one larger card.
Why does the same model have different memory requirements in different apps?
Applications can use different quantization formats, context defaults, kernels, and offload strategies. Compare the exact model file, runtime, context setting, and allocation report rather than relying on the parameter count alone.
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