Open models have made it much easier to build products around language, image, audio and video generation. Running those models still involves a less straightforward decision: choosing the GPU that can handle the workload without making every test or inference request unnecessarily expensive.
This matters whether you are testing a RAG assistant, serving an LLM through an API, generating images in ComfyUI or building an AI video workflow. A GPU with too little memory may fail to load the model or force you to reduce the context length, batch size, resolution or number of video frames. A GPU with far more memory than the workload needs may run it successfully, but at a cost that is difficult to justify.
There is no single GPU requirement for “an LLM” or “an image model.” Hardware needs depend on the model, its precision, the inference framework, the size of the inputs and outputs, and the number of requests processed at once. This guide explains how those factors affect VRAM and provides a practical starting point for choosing between the GPUs available on Nosana.
What VRAM tells you about an AI workload
VRAM is the high-speed memory available directly to the GPU. During inference, it holds model weights, input data, intermediate calculations, output data and caches. When everything fits in VRAM, the GPU can process the workload without repeatedly moving data through slower system memory.
A model’s parameter count provides a useful starting estimate. A seven-billion-parameter language model stored at 16-bit precision requires roughly 14GB for its weights alone. An 8-bit version uses approximately half as much memory for the weights, while a 4-bit version can reduce the requirement further. These figures are not complete fit calculations because the inference server, KV cache, runtime libraries and temporary tensors also consume memory.
Language models need additional VRAM as the context and number of simultaneous requests grow. Image and video models have a different memory profile: resolution, batch size, frame count and the number of pipeline components all matter. A basic image workflow may fit on a modest GPU, while a ComfyUI graph with ControlNets, upscalers, VAEs or additional text encoders can require much more.
VRAM should therefore be treated as a capacity limit, not a complete performance score. Two GPUs with 24GB of memory may both fit the same workload but deliver different generation speeds because their architectures, memory bandwidth and compute capabilities differ. The first question is whether the job fits; speed and price should be compared after that.
A practical VRAM guide
The following ranges provide a useful starting point. They are not universal compatibility guarantees, so the exact model, container and settings should always be tested before committing to a production configuration.
| VRAM | Suitable starting point | GPUs listed on Nosana |
|---|---|---|
| 8–10GB | Transcription, embeddings, classification, compact quantized LLMs and basic image generation | RTX 3060, RTX 3070, RTX 4060, RTX 3080 |
| 12GB | Quantized 7B-class LLMs, smaller RAG applications and moderate image workflows | RTX 4070, RTX 5070 |
| 16GB | 7B–8B models with more context headroom and larger image pipelines | RTX 4080, RTX 5080, RTX 4000/A4000 |
| 24GB | 7B–8B FP16 inference, larger quantized LLMs and advanced image generation | RTX 3090, RTX 4090, RTX 5000/A5000 |
| 32GB | AI video, complex ComfyUI pipelines and medium-sized quantized LLMs | RTX 5090 |
| 40–48GB | Larger-model inference, long contexts, bigger batches and demanding media workflows | A100 40GB, A6000, RTX 6000 Ada, A40 |
| 80–96GB | Large-model inference, high concurrency, multimodal systems and enterprise workloads | A100 80GB, H100, RTX PRO 6000 |
The GPUs shown in the Nosana interface and their availability can change as capacity joins or leaves the network. The useful approach is to identify the likely memory tier first and then compare the price and expected performance of the GPUs currently available within it.
8–10GB: focused workloads and affordable testing
GPUs with 8GB or 10GB of VRAM are practical for speech transcription, embeddings, classification, compact quantized language models and straightforward image generation. They can also be useful for validating a container or checking that a deployment works before moving to more expensive hardware.
Their limitations become visible when the scope grows. An application that combines embeddings with an LLM or adds higher resolution and multiple components to an image workflow may need to move beyond this tier.
12–16GB: everyday LLM and RAG development
The 12GB and 16GB tiers are a useful middle ground for developers building smaller AI products. They can support many quantized 7B-class language models, RAG pipelines and moderate image-generation workflows without the cost of a 24GB or 48GB GPU.
The distinction between a demo and a real application matters here. A quantized model may fit easily with one short prompt, while longer documents and multiple active users expand the KV cache. Sixteen gigabytes provides more flexibility for context, concurrency and additional image components.
For example, consider a company knowledge assistant using a 4-bit Mistral 7B Instruct model, an 8,000-token context and one or two simultaneous requests. An RTX 4070 or RTX 5070 with 12GB is a reasonable starting point because the model can fit while leaving space for the inference server and cache. If the application later needs longer documents or several concurrent users, moving to a 16GB GPU provides safer headroom.
24GB: serious prototyping and more demanding inference
Twenty-four gigabytes is one of the most versatile tiers for AI development. It can support many 7B–8B models at 16-bit precision, larger quantized models and advanced image pipelines without immediately moving to professional 48GB hardware.
The Mistral example shows how the job definition changes the recommendation. Loading the same 7B model at 16-bit precision requires roughly 14GB for the weights alone, immediately ruling out a 12GB GPU. A 24GB card leaves more room for the runtime, useful context and concurrent requests. The model name is unchanged, but its precision and usage create a different hardware requirement.
Nosana lists the RTX 3090, RTX 4090 and RTX 5000/A5000 in this category. The RTX 3090 and RTX 4090 both provide 24GB, but their performance can differ significantly, so runtime and price still matter once the workload fits.
32GB: AI video and complex generation pipelines
At 32GB, developers gain additional room for demanding image workflows, complex ComfyUI graphs and current open video-generation models. Video pipelines often need more memory because they process temporal information across many frames while also loading text encoders, VAEs, audio models or enhancement components.
Nosana’s MiniMax H3 image-to-video deployment is a concrete example. The workload turns a source image and prompt into a 1344 × 768 video with 124 frames at 24 frames per second, including native audio. Nosana pairs it with a 32GB RTX 5090, giving the complete pipeline enough capacity without moving directly to a 48GB professional GPU.
That recommendation applies to this workload rather than every video model. A lighter model might fit on 24GB, while a longer clip, higher resolution or additional enhancement stages could require more memory.
40–48GB: larger models and heavier production use
The 40GB and 48GB tiers are suited to larger quantized LLMs, longer contexts, bigger inference batches and demanding generative-media pipelines. Depending on the model and runtime, this range can support some 70B-class models at 4-bit precision, although the weights may leave limited room for context and concurrent requests.
Nosana lists the A100 40GB together with 48GB options including the A6000, RTX 6000 Ada and A40. These GPUs span different generations and performance profiles, so equal memory capacity should not be mistaken for equal throughput.
80–96GB: large models, concurrency and multi-model systems
High-memory GPUs become relevant when a deployment needs to serve a large model, retain long contexts, support substantial concurrency or keep several models in one pipeline. An 80GB GPU gives a large quantized LLM considerably more room for caches than a 48GB card, while 96GB expands the available configurations further.
Nosana lists the A100 80GB and H100 in the 80GB category, with RTX PRO 6000 options at 96GB. These GPUs support workloads that are impractical on consumer cards, but a compact application that uses only a fraction of their capacity will rarely justify the cost.
How to choose without overpaying
Start by defining the workload rather than selecting a GPU by model name. For an LLM, record the exact model, precision, context length, batch size and expected concurrency. For image or video generation, include the resolution, frame count, duration and every component loaded by the workflow.
Use those details to estimate the required VRAM tier, then compare the price, performance and availability of GPUs within it. Test the deployment with representative inputs: realistic prompt lengths for an LLM, or the intended resolution and frame count for a media workflow. Monitoring peak memory during these tests provides a far better basis for the final choice than a model’s minimum requirement.
If the workload comes close to the memory limit, move up a tier or reduce the setting responsible for the increase. If substantial VRAM remains unused, test a lower-cost option. The right GPU is not the largest one available; it is the most cost-effective option that can run the complete workload reliably under the conditions in which it will actually be used.
Nosana displays VRAM alongside pricing and current availability, making it easier to compare suitable GPUs before testing the workload.
Ready to find the right GPU for your workload? Explore the available options and deploy on Nosana here.
Technical note: The examples in this article are practical starting points rather than guaranteed compatibility specifications. Actual VRAM consumption depends on the model version, precision, framework, optimization settings, context length, batch size, resolution and other runtime variables. GPU listings, prices and availability on Nosana may change over time.
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