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The model you can run locally is determined by more than the number printed on your graphics card. Start with usable VRAM—physical VRAM minus desktop/OS use and other GPU workloads—then reserve space for the model’s quantized weights, the KV cache, and runtime overhead. As practical starting points, NVIDIA places 4B-class models in the 6–8GB tier, 9B–12B models around 12–16GB, and 27B-class models at 24GB or more. Those are starting points rather than guarantees: context length, quantization, backend, drivers and other applications can change the result. A model that exceeds the remaining VRAM may spill into system RAM and become dramatically slower.
What does VRAM determine when running a local AI model?
VRAM is the fast memory shared by the model, its context (the KV cache), your display environment and any other GPU work. A model is usable only when all of those allocations fit at the same time. A file that appears to fit by itself can still fail once a long conversation, a browser with hardware acceleration or another graphics workload consumes the remaining margin.
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Use this budgeting rule before downloading a checkpoint:
usable VRAM = physical VRAM − desktop/OS use − other GPU workloads
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Then check whether quantized model weights + KV cache + runtime overhead fit inside that usable amount. The Vulkan Documentation Project expresses the complete calculation as:
Total VRAM − (OS/Desktop + other application peak + model weights + KV cache) = safety margin
The safety margin is important. If the result is zero, a small context increase or a background application can force offloading to system RAM. Offloading allows a run to continue, but memory bandwidth is much lower than VRAM, so generation can become painfully slow.
How much VRAM do common local models need?
NVIDIA’s current RTX guidance gives useful starting points, not hard compatibility guarantees. The following bands assume a suitable runtime, a practical quantization and enough room for context and overhead.
| Physical VRAM | NVIDIA starting examples | What to expect |
|---|---|---|
| 6–8GB | Qwen 3.5 4B | Small models and moderate context. Leave room for the desktop and do not assume the largest available context will work. |
| 12–16GB | Qwen 3.5 9B or Gemma 4 12B | More capable models are practical, but long contexts and GPU applications can still force a smaller model or lower context. |
| 24GB or more | Qwen 3.6 27B | 27B-class models become possible, subject to the exact file, context length, runtime and workload. |
A separate NVIDIA playbook gives broader, differently named bands: Qwen 3.5 9B at 8–16GB, Qwen 3.6 35B at 16–24GB, and Qwen3.8 27B at 24–48GB. Because those recommendations come from a different playbook and release context, treat them as dated examples rather than universal specifications. Always inspect the exact checkpoint file and runtime requirements before committing your storage or download bandwidth.
NVIDIA’s local-AI hardware table currently lists GeForce RTX configurations with up to 32GB of VRAM. Card capacity alone does not identify a suitable model: two cards with the same capacity can have different driver support, memory bandwidth and acceleration behavior.
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What can I run with 8GB of VRAM?
Plan around a 4B-class quantized model, such as the Qwen 3.5 4B example in NVIDIA’s 6–8GB guidance. Keep a meaningful margin for the operating system, display compositor and context. If your card is already using several gigabytes for a game, browser or recording application, a model that fits on an idle desktop may no longer fit during normal use.
What can I run with 12GB or 16GB of VRAM?
The 12–16GB tier is aimed at models such as Qwen 3.5 9B and Gemma 4 12B in NVIDIA’s current examples. Choose between a larger model with a shorter context and a smaller model with a longer context; the right choice depends on whether answer quality or document length matters more to you.
What can I run with 24GB or more?
Twenty-four gigabytes opens the door to 27B-class starting points, including NVIDIA’s Qwen 3.6 27B example. It does not guarantee a long context or a high-bit quantization. A 24GB card can have almost no headroom when other applications consume memory, so reserve capacity before selecting the checkpoint.
How do I calculate whether a specific model will fit?
Use the checkpoint’s actual file size, not parameter count alone, and add a context allowance. This four-step estimate catches most surprises before launch.
- Record physical and free VRAM. Note the card’s total capacity, then check how much is free with your normal desktop, monitor setup and background applications running.
- Find the exact quantized file size. A 4-bit GGUF, an 8-bit build and an FP16 file of the same model can have radically different weight sizes. Include any split files if the checkpoint is distributed in parts.
- Budget the KV cache. Longer context means more cached keys and values. Add the runtime’s estimate for your target context rather than assuming the model file is the whole requirement.
- Keep a safety margin. Subtract the weights and KV cache from usable VRAM. If the remainder is tiny or negative, lower context, choose a smaller quantization or model, close other GPU applications, or accept system-RAM offload and its speed penalty.
The Vulkan documentation gives a concrete scale reference: a 30B model in Q4_K_M is roughly 18GB, while a 16k context can add another 1–2GB. On a 24GB card, other workloads using 6GB would leave no practical margin for that combination. Reducing the context or selecting a smaller model restores headroom.
Why is parameter count only a rough guide?
Parameter count describes how many learned values a model has, but storage depends on the number of bits used for each value and on format overhead. A dense model keeps all parameters active for every token. A mixture-of-experts (MoE) model may activate only some experts per token, improving compute efficiency, while still requiring memory for the complete set of weights. Compare the actual file size and runtime behavior rather than treating an MoE’s active-parameter figure as its total memory requirement.
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Which quantization should I choose?
Quantization stores weights with fewer bits, reducing memory use at a possible quality cost. NVIDIA recommends Q4_K_M as a starting choice for llama.cpp and describes Q4_K_M or NVFP4 as useful balances among throughput, accuracy and memory. For vLLM or PyTorch workflows, NVIDIA’s guidance prefers NVFP4.
| Choice | Where it fits | Trade-off to check |
|---|---|---|
| Q4_K_M | Recommended starting quantization for llama.cpp | Lower memory than higher-bit formats; compare quality on your own tasks and leave room for context. |
| NVFP4 | Preferred in NVIDIA guidance for vLLM or PyTorch workflows | Requires a compatible software and hardware stack; verify support in the chosen release. |
| Higher-bit or FP16 | When quality and available memory justify the larger file | Weight memory rises quickly and leaves less space for KV cache and other applications. |
Do not pick a quantization by its label alone. Compare the resulting file size, supported context, generation speed at your target context and the quality of the tasks you actually perform. A smaller quantized model with ample headroom can be more useful than a larger model that constantly offloads.
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The context window is the amount of conversation, document text and instructions the model can consider at once. Its KV cache grows as context grows, so a model that fits at 4k tokens may fail at 16k. The cache also persists while you generate, which is why a session can start normally and run out of memory later.
Set context deliberately instead of accepting the runtime’s maximum. For chat, start with the length your prompts require and increase it only when you need to include larger documents. For retrieval or coding workflows, measure the longest realistic prompt and add modest headroom rather than selecting an extreme maximum by default.
What should I do when a model fits at startup but crashes later?
- Lower the context window and restart the session so the old KV cache is released.
- Close GPU-accelerated browsers, games, recording tools and other applications, then check free VRAM again.
- Use a smaller or more aggressively quantized checkpoint if the workload still exceeds the margin.
- Disable optional features that allocate additional GPU buffers when your runtime exposes them.
- If the runtime offers CPU or system-RAM offload, use it as a fallback, understanding that token generation may slow substantially.
Which runtime should I use?
Ollama, llama.cpp and LM Studio are separate projects with different interfaces. All can work with GGUF models, but acceleration, supported tasks and hardware compatibility depend on the specific build and release.
| Runtime | Best fit | What to verify |
|---|---|---|
| Ollama | Model management and a local API | NVIDIA compute capability, driver version and supported RTX families on the current hardware-compatibility page. |
| llama.cpp | Lightweight execution, direct controls and an optional OpenAI-compatible server | That your build includes the intended GPU backend and that the GGUF quantization is supported. |
| LM Studio | Desktop downloading and interactive model testing | The release’s acceleration support, context settings and the model format it can load. |
Check the current NVIDIA and runtime compatibility documentation before downloading a large checkpoint. Ollama’s hardware documentation, for example, lists required NVIDIA compute capabilities, drivers and supported RTX families; an older or newer card should not be assumed compatible merely because it has enough VRAM.
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How should I compare two models that both fit?
Once both candidates fit with a real safety margin, memory capacity is no longer the only decision. Compare them on the workload you care about, using the same context and runtime settings.
- Quality on your tasks: test representative coding, summarization, extraction or chat prompts rather than relying on a general benchmark reputation.
- Quantization and file size: record the exact format and size; two releases with the same parameter count can have different memory footprints.
- Context cost: compare the context length you can sustain without exhausting the KV-cache budget.
- Generation speed: measure tokens per second at your target context, not only on an empty prompt.
- Backend support: confirm that your chosen runtime and GPU build provide acceleration for the model format and tasks you need.
- Dense versus MoE behavior: account for total resident weights as well as active parameters, because memory and compute can scale differently.
A practical test is to load each candidate with the same prompt length, generate a representative response, and watch whether free VRAM remains stable. The fastest model at a short context may not be the best choice for long documents if its KV cache removes the margin.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the most common VRAM mistakes?
Counting the card’s full capacity
Physical VRAM is not an application budget. The desktop, display compositor, browser and other GPU programs consume memory before the model starts. Use free VRAM under normal conditions and leave headroom for peaks.
Using a training estimate for inference
NVIDIA’s rough GPU-memory article estimates that a 7-billion-parameter model in FP16 needs approximately 28GB because it multiplies two bytes per parameter and then doubles for optimizer and other overhead. That estimate is useful for illustrating training-style overhead, but inference usually has a different profile. For local inference, start with the exact quantized file size, KV-cache requirement and runtime overhead.
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Weights are static; the KV cache grows with the conversation. Set and test the context you actually intend to use, especially when a model is close to your VRAM limit.
Assuming every “4-bit” file is equivalent
Quantization families, formats and runtime implementations differ. Confirm whether the file is Q4_K_M, NVFP4 or another format, and whether your backend accelerates it.
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Downloading before checking compatibility
A large file wastes time and disk space if your driver, compute capability or runtime build cannot use it. Verify the current compatibility requirements first, then choose the checkpoint.
Accepting offload without measuring the slowdown
System-RAM offload can prevent an out-of-memory error, but it may make generation much slower. If interactive speed matters, a smaller model with complete GPU residency is often the more practical choice.
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What is a reliable selection workflow for an 8GB, 12GB, 16GB or 24GB card?
Use this repeatable process whenever you change a model, context or workload.
- Define the workload: write down whether you need chat, coding, long-document summarization, image or tool tasks, and the longest prompt you expect.
- Measure the normal baseline: with your monitor, desktop and usual applications open, note free VRAM.
- Choose a starting tier: use NVIDIA’s 4B, 9B–12B and 27B-plus examples as an initial shortlist for 6–8GB, 12–16GB and 24GB-plus cards.
- Select a compatible quantization: start with Q4_K_M for llama.cpp or NVFP4 for vLLM/PyTorch when the hardware and software support it.
- Budget context: include KV-cache memory for the target context; use the 30B Q4_K_M and 16k-context example as a reminder that a long window can add 1–2GB.
- Load a small test: run a representative prompt, then a long prompt, while watching VRAM and speed.
- Adjust one variable: reduce context, close another GPU application, change quantization or move down a model tier. Changing one item at a time reveals what caused the failure.
- Document the working setup: record model file, quantization, context length, runtime version, driver and observed free VRAM so you can reproduce it after updates.
When should I choose a smaller model instead of offloading?
Choose a smaller, fully GPU-resident model when you need responsive interactive generation, stable latency or a long context. Offloading is reasonable for occasional batch jobs or when the larger model’s quality is worth substantially slower output. Make that decision using measured speed at your real context, not a startup test with an empty conversation.
Similarly, reducing context is often preferable to dropping model quality when your prompts are naturally short. If your work requires long documents, preserve context and select a model whose weights leave enough room for the KV cache.
Frequently Asked Questions
Does adding system RAM compensate for insufficient VRAM?
It can let a runtime offload some work and avoid an immediate out-of-memory failure, but system RAM is much slower than VRAM. Treat offload as a compatibility fallback and measure whether the resulting token speed is acceptable for your workload.
Is a 4B model always better for an 8GB graphics card?
It is the conservative starting tier in NVIDIA’s current guidance, not a universal rule. A larger model may fit with a very short context and minimal background use, while a 4B model can still fail if other applications consume most of the card’s memory.
Why did a model download successfully but refuse to load?
Downloading checks storage, not GPU memory or acceleration support. The exact quantization, context allocation, runtime build, driver and compute capability all have to be compatible when the model is initialized.
Should I maximize the context window because my model supports it?
No. The advertised maximum is not the same as the context you can sustain in your VRAM budget. Set the window to the longest prompt you actually need and increase it only after confirming that the KV cache leaves a safety margin.
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