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For local chat and coding, choose VRAM for the model, quantization, context length, and other GPU use together—not from the parameter count alone. As a practical starting point, 8GB can suit many 7B–8B models at 4-bit, 12GB many 14B models, 16GB many models around 20B, and 24GB many 30B–33B models. Those are planning bands, not guarantees: a longer context, a different model, or desktop and application use can push a workload past the limit.
The measured examples below come from one Ollama-based test, so treat them as evidence of what can fit in a particular setup, not universal requirements for every GPU or inference stack.
How much VRAM do local LLMs need?
The following figures are useful starting points for ordinary local inference. The measured VRAM amounts are from Arynwood / Lorelei Noble’s 2026 test, which used approximately 4-bit quantization and a 4,096-token context; they describe that setup, not every model of the same size.
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|---|---|---|---|
| 8GB | Many 7B–8B models at 4-bit | 7B–8B examples used 5.6–5.8GB | Keep context moderate and reserve room for the desktop. |
| 12GB | Many approximately 14B models at 4-bit | A 14B example used 10.4GB | Limited headroom; context or other GPU work may force offload. |
| 16GB | Many approximately 20B models at 4-bit | A 20.9B example used 14.9GB | Context and background applications can consume the remaining margin. |
| 24GB | Many approximately 30B–33B models at 4-bit | A 33B example used 21.1GB | Long context or application use can still make the fit too tight. |
| 48GB or more | Larger models, longer contexts, or more concurrent work | No universal measurement established | Capacity still depends on model, quantization, backend, and workload. |
Source for the measured examples: Arynwood / Lorelei Noble, 2026. The practical targets are not hard limits. Two models with the same parameter count can have different memory needs, and even the same model can use a different amount depending on its format and runtime settings.
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What uses VRAM during local inference?
Model weights are only one part of the allocation. A realistic estimate also accounts for the context cache, runtime and desktop overhead, and any other applications using the GPU.
Model weights and parameter count
More parameters generally mean more memory for weights. Hugging Face’s rule of thumb is about 2GB per billion parameters for bfloat16 or float16 weights; its example puts a 70B model at roughly 140GB for weights alone. That is not a complete inference budget: it excludes context-related allocations and other runtime needs. See Hugging Face’s memory optimization documentation.
That distinction explains why a model’s download size or parameter label does not by itself settle whether it will fit. The precision and quantization of the actual model file matter, as do the context and software used to run it.
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Quantization stores weights with lower precision to reduce memory use. Four-bit and eight-bit versions are common ways to fit larger models on consumer hardware; NVIDIA summarizes the principle as “Quantized models use lower-precision weights to fit in less VRAM” in its local-LLM overview.
Hugging Face gives an example where a model’s memory requirement falls from roughly 32GB to 15GB at 8-bit and slightly above 9GB at 4-bit. These are the documentation’s example figures, not guarantees for every model. Lower precision is a memory-quality trade-off, not a free upgrade: quality or speed can change, and the outcome depends on the model and format. When comparing estimates, check the quantization format of the specific file rather than assuming every “4-bit” model has identical memory use.
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Context length and the KV cache
Context length is the amount of token history the model can access during a conversation or task. As context grows, the runtime must reserve more memory for the key-value (KV) cache. That means a model that fits with a short prompt may fail to fit when asked to retain a much longer conversation, codebase, or document.
In Arynwood / Lorelei Noble’s Qwen2.5 7B example, increasing context from 2,048 to 32,768 tokens raised measured memory use from 5.4GB to 8.9GB. This is a specific model and setup, but it illustrates why a VRAM estimate without a context assumption is incomplete. Ollama defines context length as “the maximum number of tokens that the model has access to in memory”; see its context-length documentation.
Desktop, runtime, and other GPU applications
The operating system’s desktop, the inference runtime, and other GPU applications all compete for memory. The Vulkan Documentation Project recommends budgeting by subtracting desktop and operating-system use, peak application use, quantized model weights, and KV-cache needs from total VRAM; what remains is the safety margin. Its example shows why advertised capacity alone can mislead: a 24GB card with 6GB in application use and an approximately 18GB 30B Q4 model has no remaining margin for other inference allocations. Read its VRAM budgeting guide.
What should you expect from each VRAM capacity?
Use capacity bands to shortlist a target, then verify the specific model, quantization, context, and runtime. The trade-off is not simply “larger model versus smaller model”: keeping more context or running multiple GPU applications may require memory you otherwise expected to devote to weights.
Is 8GB enough for local LLM inference?
It can be enough for many 7B–8B models in 4-bit formats, particularly when context is moderate and the desktop is not using much of the GPU. It is not a promise that every 8B model, quantization, or context will fit. If your planned prompt history is long, check the model’s memory behavior at that context rather than relying on a short-context estimate.
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Is 12GB enough for a 14B model?
Often, but with little room to spare. If the model or context needs more memory than expected, or another GPU application is active, the runtime may split work between GPU and system RAM. That can make the model run without keeping all of it in VRAM, but running is not the same as running at a comfortable interactive speed.
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For a target around 20B at 4-bit, 16GB is a practical starting category, though limited headroom makes the exact context and desktop load important. If you want to target many 30B–33B Q4 models, 24GB is the more relevant consumer capacity band. Even then, a model that nearly fills the card leaves little room for the cache, runtime, and other applications, so fitting the weights alone is not enough.
When does 48GB or more make sense?
More capacity is useful for larger models, long contexts, or multiple concurrent workloads, but 48GB is not a universal minimum for local inference. The actual requirement depends on the model architecture, quantization, backend, context, and how much work must stay on the GPU. Treat a larger card as additional capacity to plan with, not a guarantee that a particular context or workload will fit.
Does full precision require more VRAM than a 4-bit model?
Yes. Full-precision bfloat16 or float16 weights take substantially more memory than lower-bit weights; Hugging Face’s rough estimate is 2 × the parameter count in billions, expressed in gigabytes, for weights alone. Its 70B illustration is about 140GB before other inference allocations, so a consumer-GPU plan commonly relies on quantization or a larger-memory system.
When considering an estimate, separate three things: the memory occupied by weights at the chosen precision, the additional runtime allocations such as the KV cache, and the space needed by the desktop and other applications. A headline figure for model weights alone should not be mistaken for the VRAM required to run a full workload.
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What happens if the model does not fit in VRAM?
The runtime may partially offload model work to system RAM or CPU rather than refusing to run outright. This can be a useful fallback, but it usually trades performance for capacity; Ollama advises avoiding CPU offload for best performance in its context-length guidance.
The dated Arynwood / Lorelei Noble test reported 6.5 tokens per second for a 20B model with 73% on the GPU, compared with 2.4 tokens per second on CPU alone. Its 33B example reached 2.7 tokens per second with 53% on the GPU. These figures describe that test’s setup, not a prediction for another computer, model, or backend. They do show why “it loads” and “it responds quickly enough for interactive use” are different criteria.
If memory is tight, try remedies in an order that preserves the intended workload where possible:
- Reduce context length. This reduces the cache requirement, though it also limits how much conversation or source material the model can retain.
- Choose a smaller model. This is the clearest way to lower weight demand if the target model is not essential.
- Use a less memory-intensive quantization. Confirm the format and its quality trade-off rather than assuming a lower-bit label has no cost.
- Close competing GPU applications. This can recover memory that is not being used for inference.
- Allow CPU or system-RAM offload if slower responses are acceptable. It can make a larger model usable, but may reduce interactive performance.
- Consider a second GPU or a system with more combined capacity. The Vulkan guide lists a second GPU as an option when VRAM is insufficient; software support and the workload determine whether that option is practical.
How should you compare GPUs for local LLM inference?
Do not rank cards by advertised VRAM alone. Compare the memory that remains available for the exact model and workload, and consider whether the model can stay on the GPU at a context length you will actually use.
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- Usable VRAM: leave room for desktop, runtime, KV cache, and concurrent applications.
- Model and quantization: compare the actual model file and format, not just parameter labels.
- Context length: check whether the model fits at your intended history size; a short-context fit may not carry over to a much longer one.
- Inference backend: Ollama, llama.cpp, LM Studio, and other stacks can expose different options and memory behavior.
- Concurrency: multiple loaded models or parallel requests require additional available memory.
- Performance after fit: if interactive speed matters, keeping the workload on the GPU is generally preferable to depending on system-RAM offload.
A useful purchase or upgrade decision starts with a target workload: name the model, its quantization, the context you need, and whether other GPU applications will be open. Then compare the estimated total allocation—not merely the model’s weight size—with usable VRAM. If the estimate leaves no margin, a smaller model or more memory may be a safer choice than assuming the runtime will make a tight fit comfortable.
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What are Ollama’s context defaults, and do they set a VRAM requirement?
Ollama documents different default context lengths by available memory: below 24 GiB, the default is 4k; from 24 GiB to below 48 GiB, it is 32k; at 48 GiB or more, it is 256k. These are runtime defaults, not universal laws of inference or minimum hardware requirements. Users can configure other context lengths when the model and backend permit them, and the memory demand still depends on the specific workload.
Frequently Asked Questions
Does a 48GB GPU guarantee that a 256k context will work?
No. Ollama’s 48 GiB threshold is a documented default, not a guarantee that every model and workload can sustain that context. Model architecture, quantization, and other runtime allocations still affect whether a configuration fits.
Why might the same model use different VRAM in different apps?
Inference backends can expose different settings and have different memory behavior. Context, quantization format, concurrent GPU use, and operating-system allocation also affect the total, so a figure from one runtime should not automatically be treated as the requirement in another.
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Is a model’s download size the same as its VRAM requirement?
Not necessarily. The file reflects its stored format, while inference also needs memory for context-related data and runtime overhead; the desktop and other GPU applications take space as well. A file-size figure alone therefore does not establish the full working-memory budget.
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