Tinklaraštis

Running advanced AI models locally requires a capable GPU with sufficient VRAM and compute throughput. This guide compares consumer-grade GPUs (e.g., NVIDIA GeForce RTX 30/40 series) and server-grade GPUs (like NVIDIA A100/H100 or AMD MI300) for popular downloadable AI models. We outline each model’s VRAM needs, the recommended GPUs to run it, and the availability of quantized versions for lower-end cards.

AI model GPU requirements scale dramatically with model size. Larger models with more parameters demand significantly more VRAM (video memory) and compute power​. Precision also matters: lower numerical precision (e.g., FP16 or int8) can cut memory needs versus full FP32 precision​.

• Inference (using a trained model) generally needs less VRAM than training, primarily just enough to hold the model weights (plus a bit of overhead for activations)​. For example, a 7B-parameter model in 16-bit inference requires on the order of ~14–15 GB VRAM​, whereas a 65B model can need well over 100 GB (meaning it must be split across multiple GPUs or heavily quantized)​.

• Training or fine-tuning models requires additional memory for gradients and optimizer states – roughly 2–4× the model size​. Thus, training pushes VRAM needs much higher than inference. For instance, fine-tuning a 13B model in FP16 can consume on the order of ~97 GB VRAM​, far beyond what a single consumer GPU holds. Techniques like gradient checkpointing, smaller batch sizes, or parameter-efficient tuning (LoRA, QLoRA) can mitigate this.

As model parameter counts increase, you need proportionally more powerful GPUs (especially GPUs with more VRAM). Small models (6B–7B params) can run on a single consumer GPU, but the largest models (40B–65B+) demand multi-GPU setups or high-end data center GPUs. Below, we map specific language and image models to suitable GPUs for inference and training, balancing performance vs. budget.

(In the tables, “FP32” and “FP16” refer to peak theoretical throughput. FP16 performance assumes use of Tensor Cores for GPUs that have them. “Consumer” GPUs are gaming/workstation cards, and “Data Center” GPUs are server-class cards.)

LLaMA (7B, 13B, 65B – Meta AI)

LLaMA is an open foundational language model available in sizes of 7B, 13B, 33B, and 65B parameters. We cover 7B, 13B, and 65B. These models require progressively more memory: roughly ~12–13 GB, ~24 GB, and ~>120 GB of VRAM respectively (for 16-bit inference). In practice, the 7B and 13B models can run on high-end consumer GPUs, while the 65B model is so large it exceeds any single GPU and must be split or quantized heavily.

• 7B: In FP16, LLaMA-7B occupies about 12.3 GB of VRAM just for inference​. This fits on consumer cards with ≥12GB. (8-bit or 4-bit quantization can reduce it to ~6 GB or ~3 GB​, allowing even 8GB cards to load it.) Training 7B from scratch would need ~50 GB (not typically feasible on a single GPU)​, but fine-tuning can be done with lower precision or gradient checkpointing on a 24GB card.

• 13B: LLaMA-13B in FP16 needs roughly 24 GB VRAM for inference​ – right at the limit of a 24GB GPU. 13B models “in bfloat16 take ~14–15 GB” (probably assuming weight offloading) just to load​, and closer to ~26 GB total at runtime​. Realistically, you’d want a 24GB–32GB GPU for smooth 13B inference. Full training would require an order of ~97 GB or more​ (so multi-GPU or out-of-core methods are needed). Most users instead fine-tune 13B models via low-rank adapters or 8-bit optimizers to stay within a single GPU’s memory.

• 65B: This model far exceeds typical GPU memory. FP16 inference for 65B parameters is estimated at >130 GB VRAM (65B * 2 bytes per weight ≈ 130 GB, plus overhead). In practice, Meta’s 65B model cannot run on any single GPU without compression. One discussion notes ~250 GB of GPU memory (across a cluster) is needed for 65B in full FP16, or about half that (~125 GB) if using int8 weights​. Even 4-bit quantization (~32 GB) is beyond any single consumer GPU (24 GB cards are still too small to hold 65B at 4-bit)​. Bottom line: 65B LLaMA requires either multiple GPUs or specialized 80GB data center GPUs (and often both). Training or fine-tuning 65B is out of reach for most single-node setups (it was initially trained on a cluster of 2048 GPUs)​.

For LLaMA-7B and 13B, high-VRAM consumer GPUs can be used; for LLaMA-65B, only multi-GPU or top-tier server GPUs suffice. The table below lists example GPUs:

An RTX 3090 24GB or 4090 24GB can comfortably host LLaMA-13B (24GB) for inference, and even 7B with headroom for longer prompt contexts. These cards hit around 70–165 TFLOPS in half-precision, providing good throughput for local inference. For 65B, the NVIDIA A100 (80GB) is a go-to solution: one A100-80 can hold roughly half the model in FP16. In practice, you’d distribute 65B across at least 2× A100-80 (or 4× 40GB GPUs). Newer H100 80GB GPUs (~2022) further triple the half-precision compute (H100 offers up to ~400 TFLOPS FP16 with FP8 support) – ideal for enterprise serving of such models. But 65B is infeasible for most individuals to run locally; sticking to 7B or 13B (or using 4-bit compression on 30B+ models with a 24GB card) is the practical limit. LLaMA weights can be obtained from Meta AI (with a research request) or via authorized conversions on Hugging Face​.

LLaMA (original) weights are available from Meta AI’s repository (by request)​. Community-converted checkpoints (for use with PyTorch/Transformers) can be found on Hugging Face: e.g., huggyllama/llama-7b (7B)​, huggyllama/llama-13b, and huggyllama/llama-65b.

Mistral 7B (Mistral AI)

Mistral 7B is a 7-billion-parameter open LLM released in 2023, known for its efficiency. Its memory requirements are similar to LLaMA-7B. In FP16, approximately 13.7 GB of VRAM is needed for inference​. Mistral’s authors suggest a 16 GB GPU is recommended for safe execution (it has an 8k token context, which adds some overhead). With 4-bit quantization, the model can run in around 3.4 GB of VRAM​ – meaning even a single 8 GB card (with int4) can handle it​. For fine-tuning, full 16-bit training would consume ~55 GB VRAM​. Still, in practice, Mistral 7B is often fine-tuned with memory-efficient methods (QLoRA, etc.), making it feasible on ~24 GB GPUs or even smaller with 8-bit Adam optimizers.

Mistral-7B is relatively small and can be run on affordable hardware. A mid-range consumer GPU with ≥12 GB memory will do for inference. For faster training/fine-tuning or larger batch sizes, a higher-end card or professional GPU helps.

An RTX 3060 12GB is a budget option that can fit Mistral 7B in FP16 (or easily in 8-bit) – developers have run Mistral on consumer cards with 8–12GB by using 4-bit quantization​. The model will generate text slower on a RTX 3060 (it has modest ~13 TFLOPS FP32), but it works. On the higher end, an RTX A5000 (Ampere workstation GPU, 24GB) easily handles Mistral 7B with ample VRAM headroom, allowing longer contexts or multiple concurrent inference streams. (The A5000 is analogous to a GeForce 3080 Ti/3090 in performance.) For data-center deployment, one could also use an NVIDIA T4 (16GB) for simple inference loads – though T4 has lower throughput (~8 TFLOPS FP32), it has enough memory. If fine-tuning Mistral 7B with full gradients, a 24GB GPU or larger is advised (e.g., NVIDIA A6000 48GB or A100) to avoid out-of-memory at 16-bit precision. Mistral 7B weights are available on Hugging Face: mistralai/Mistral-7B-v0.1​ (Apache 2.0 license).

Falcon (7B & 40B – TII UAE)

Falcon is an open-source language model released by TII, available in 7B and 40B parameter versions (plus refined instruct variants). The Falcon-7B requirements align with other 7B models (~15 GB VRAM for FP16 inference). The Falcon-40B model is significantly larger – around 40 billion params – and approaches the limits of single-GPU memory. Falcon-40B in FP16 needs roughly 77 GB of VRAM to load​, which means even an 80GB GPU is almost entirely consumed. However, Falcon is optimized for inference (flash attention, etc.), and many users run Falcon-40B by using 8-bit or 4-bit quantization to reduce memory to more practical levels. In int4, Falcon-40B can be about ~19 GB ​, making it feasible to load on a 24GB GPU (with some room to spare).

• Falcon 7B: Easily runs on a single consumer GPU. A 16 GB GPU is recommended for full FP16 inference to allow some overhead. 7B at 4-bit can even run on ~4 GB of memory. Fine-tuning Falcon-7B (if doing full gradient updates) would be similar to other 7B (40–50 GB or more needed), so usually one uses low-memory fine-tuning techniques.

• Falcon 40B: This model is challenging to run without high-end hardware. For inference, 8-bit compression (~40 GB) or 4-bit (~20 GB) is used to fit on one or two GPUs. For example, a single RTX 4090 (24GB) can run Falcon-40B in 4-bit mode (needs ~19 GB)​, albeit with slower generation speed. In full FP16, you must use multi-GPU (e.g., split across 2×48 GB GPUs or 4×24 GB GPUs) or a single 80GB GPU with memory-sparing execution. Training Falcon-40B from scratch is out of scope for most (it would require dozens of A100 GPUs); even fine-tuning likely requires model-parallel on multiple 24+ GB GPUs.

For Falcon-7B, the same class of GPUs suitable for LLaMA-7B applies (see above). For Falcon-40B, one needs either the largest modern GPUs or a multi-GPU rig. We list a couple of examples:

The RTX 4080 16GB is a viable lower-cost choice for Falcon-7B (it can handle 7B in FP16 with room for more extended sequences). It is somewhat insufficient for Falcon-40B unless using 4-bit quantization (where 16GB might be a little tight at 19GB requirement – in practice, one would offload some layers to CPU or use compression to make 40B run on 16GB). The RTX 4090 24GB is more ideal for Falcon-40B quantized inference – 24GB allows running the 40B model comfortably in 4-bit or even 8-bit with CPU offloading. A 4090 will also significantly speed up generation (its ~165 TFLOPS FP16 is over 2× the 4080’s raw throughput). For best performance or  training, the NVIDIA A100 is recommended​. An A100 80GB can almost fit Falcon-40B in full precision (77 GB)​ on a single card – in fact, TII’s reference implementation suggests 2× 40GB or 1× 80GB for running Falcon-40B. Multiple A100s or newer H100 GPUs would be used in enterprise settings to serve Falcon-40B with low latency. Falcon models can be downloaded from Hugging Face: tiiuae/falcon-7b for the 7B​, and tiiuae/falcon-40b for the 40B.

GPT-J 6B (EleutherAI)

GPT-J is a 6-billion-parameter transformer model (released in 2021 by EleutherAI). It is one of the largest models that can comfortably run on a single consumer GPU. VRAM requirements: roughly ~12 GB for FP16 inference (6B params * 2 bytes ≈ 12 GB). The full 32-bit weights are ~24 GB​, so loading in half precision reduces it to ~12 GB. Hugging Face’s analysis shows about 10.9 GB VRAM is needed for GPT-J in float16​. This means a 12GB card (like RTX 3060 or 3080 12GB) can barely host the model (with a small overhead margin). Using 8-bit quantization, GPT-J can fit in ~6 GB, and 4-bit in ~3 GB, so even an 8 GB GPU can run it with quantized weights. For training GPT-J, memory requirements are ~4× higher (full FP16 fine-tuning might need ~43–50 GB total)​, but again techniques like 8-bit optimizers can allow fine-tuning on a single 12–16 GB GPU with trade-offs​.

GPT-J represents an “entry level” large model – you don’t need the newest or most expensive GPU, just one with sufficient VRAM. Consumer GPUs around 12–16 GB are a sweet spot. Data-center GPUs with 16+ GB (Tesla T4, V100, etc.) can also deploy GPT-J easily.

An RTX 3060 12GB (or any similar 12 GB card, e.g. RTX 2060 12GB or RTX 3060 Ti 16GB if available) allows running GPT-J 6B in full 16-bit precision. It’s likely the minimum consumer GPU for out-of-the-box usage (some users have also loaded GPT-J on 8GB cards using 8-bit models). Performance-wise, GPT-J will generate text at a modest speed on a 3060 (~12 TFLOPS FP32); upgrading to an RTX 3080 or 4080 would increase throughput (and those have more memory bandwidth to handle faster inference). On the server side, NVIDIA T4 (16GB) is an affordable inference GPU often used for models like GPT-J in the cloud. It has lower FP32 throughput (~8 TFLOPS) but its Tensor Cores provide up to 65 TFLOPS for FP16 operations​, which is sufficient for inference batches in deployment. A single T4 can thus serve GPT-J with reasonable latency. Other suitable GPUs include older Tesla V100 16GB (which has ~15 TFLOPS FP32​) – one V100 can efficiently run GPT-J and even do training if needed. In summary, any >=12 GB VRAM GPU from recent generations will work for GPT-J. The model can be downloaded from Hugging Face: EleutherAI/gpt-j-6B.

GPT-NeoX 20B (EleutherAI)

GPT-NeoX-20B is a 20-billion-parameter open-source model (from EleutherAI, 2022). It is significantly larger than GPT-J and pushes the limits of single-GPU memory. VRAM requirements: around 40–45 GB for 16-bit inference​​. In practice, running the 20B model in full precision typically “requires more than 40 GB of VRAM”​, and users suggest at least 45–48 GB to be safe​. This means no single consumer GPU can load it entirely (the largest consumer cards are 24 GB). Deployment of GPT-NeoX-20B therefore usually relies on either multi-GPU splitting (e.g. 2×24GB GPUs each holding half the model)​, or weight compression. With 8-bit compression, the model might take ~20 GB, which can fit on a 24 GB card. Indeed, GPT-NeoX 20B has been run on single RTX 3090s by loading 8-bit weights (or using CPU offload for the remainder). The requirements for training are incredibly high – full FP16 training would be >160 GB (far beyond a single node), so any fine-tuning is done with multi-GPU setups or memory-optimized methods.

Recommended GPUs: To run GPT-NeoX-20B locally, you realistically need either a 24 GB consumer GPU (with 8-bit or 4-bit quantization) or a high-memory professional GPU. For example, two RTX 3090s (24GB each via NVLink) can jointly host the model in FP16. Data center solutions like the A100 were designed for this scale.

The RTX 3090 24GB (or equivalently RTX 4090 24GB) is the minimum for hosting GPT-NeoX-20B on a single card – and even then, only with reduced precision. Running NeoX-20B on a 3090 typically involves loading 8-bit weights, which use ~21 GB VRAM​ (fits in 24GB). The 3090’s strong compute (70 TFLOPS FP16) means it can handle the model decently fast for inference. A step up is the RTX A6000 48GB, an Ampere workstation GPU. With 48 GB, one A6000 can load the full 20B model in FP16 (no quantization needed) since ~45 GB is within its memory. Two A6000s combined (96 GB) were suggested for heavier loads or faster throughput​. For enterprise settings, NVIDIA A100 GPUs are ideal. An A100 40GB can load the model (just about ~40GB needed) and was explicitly recommended for GPT-NeoX by cloud providers​. In fact, “GPT-NeoX (20B)… recommended GPU: NVIDIA A100 or multiple RTX A6000 GPUs”​. An A100 will also outperform any GeForce on training tasks due to its Tensor Core capabilities (up to 156 TFLOPS FP16). Plan on ~2×24GB GPUs or a single 48 GB+ device for comfortable use. GPT-NeoX-20B is available from Hugging Face at EleutherAI/gpt-neox-20b.

Stable Diffusion (v1.5 and v2.1 – Text-to-Image Diffusion)

Stable Diffusion v1.5 and v2.1 are latent diffusion models for image generation (with ~0.98B parameters U-Net plus CLIP text encoder). They are relatively lightweight in terms of model size (~4GB of weights), but image generation has other constraints (each inference involves iterative passes). Still, these models were designed to run on consumer GPUs. VRAM requirements for inference: roughly 4–6 GB is recommended for generating standard 512×512 images​. Benchmarks show stable diffusion needs only about 5 GB of VRAM per image for default settings​. This means even a GTX 1660 (6GB) or RTX 2060 (6GB) can run it, albeit with some swapping overhead if exactly at the limit. With optimizations like half-precision and aggressive memory management, people have squeezed SD1.x on 4GB cards, but 6GB is a practical minimum for smooth operation​. For training/fine-tuning (e.g. DreamBooth or LoRA on SD), more VRAM is needed to hold activations. It’s possible to fine-tune Stable Diffusion on a GPU with 8GB VRAM​, though 12–16GB is more comfortable, especially for larger batch sizes or higher resolution. Official recommendations often cite 8GB as a minimum for training​.

• Inference Performance: Many consumer GPUs do fine – Stable Diffusion doesn’t require massive compute. For example, an RTX 3080 can generate an image in ~5 seconds, and even older consumer GPUs are only a bit slower​. The current high-end RTX 4090 can produce ~40+ images per minute at 512px​, which is dramatically faster. So more GPU power mainly affects speed (iterations per second), while memory affects whether certain features (like high-res or multiple ControlNets) fit.

• High-Resolution or Extensions: If you use Stable Diffusion with enhancements (like upscaling, or ControlNet, or text-guided image-to-image with long prompts), VRAM usage can spike beyond 6GB. Using multiple conditioning networks or generating at higher resolutions (e.g. 768px or more) might require 10–12 GB or more. For these advanced use cases, having a GPU with >10GB VRAM is recommended so you don’t run out of memory​.

• Training: For a reasonable batch size, Fine-tuning SD 1.5 on a custom dataset (e.g., DreamBooth) traditionally required a 16GB+ GPU. New optimizations (xFormers memory-efficient attention, etc.) have lowered the requirement—people report doing DreamBooth on 10 GB VRAM by using such tricks​​. Still, for full 512×512 training with decent batch sizes, an RTX 3090 (24GB) or A5000 (24GB) will give a lot more headroom, allowing batch sizes 2–4, which speeds up convergence.

Stable Diffusion v1.5/v2.1 can run on mid-tier gaming GPUs; one does not need a data-center GPU unless serving many concurrent requests or training at scale. We provide a couple of examples:

The RTX 3070 8GB represents a mainstream GPU that meets the minimum for Stable Diffusion (4–6GB). With 8GB, it can handle standard inference plus some additional overhead for things like a simple ControlNet or slightly larger images. It's 20 TFLOPS of FP32 compute, which means it can generate images reasonably fast (a few seconds per 512px image). If you plan to do a lot of SD work or fine-tune models, an upgrade to RTX 3090 24GB (or RTX 4090) is beneficial. The 3090’s 24GB VRAM lets you train and generate without worrying about memory, even at 768×768 or with multiple pipelines. It also more than doubles the raw throughput of the 3070, yielding notably faster iteration speeds. On the server side, an NVIDIA T4 GPU is a common choice for inference – it has 16GB which is enough for one or two simultaneous SD generations, and its tensor cores accelerate the diffusion U-Net (the T4 can perform ~65 TFLOPS in FP16, so it’s surprisingly competent for a low-power 70W card). For heavy training or multi-user inference, one might consider an A100 40GB (especially for training, where the A100’s bandwidth and bigger memory shine), but for most local developers, that would be overkill. In summary, any recent GPU with ≥6GB VRAM can run SD1.x, but 8–12GB is recommended for flexibility​, and higher TFLOPS will scale generation speed nearly linearly​. Stable Diffusion v1.5 and v2.1 weights are available via Hugging Face: e.g., runwayml/stable-diffusion-v1-5 and stabilityai/stable-diffusion-2-1.

Stable Diffusion XL (SDXL 1.0)

SDXL 1.0 is a newer, larger diffusion model (released in 2023 by Stability AI). It consists of a base model (~2.6B parameters U-Net plus larger text encoders) and an optional refiner model (~1B). SDXL delivers higher image quality and supports 1024×1024 resolution, but at the cost of higher compute and memory usage. VRAM requirements: Stability recommends 8 GB VRAM as a baseline for SDXL inference​. In practice, 8GB will allow running the SDXL base at 512px (or even 1024px with some optimization). The base alone can fit in ~7–8 GB fp16. If you also load the refiner simultaneously (for maximum quality), memory use can climb to ~12–16 GB total (since the refiner is another model applied after the base). Therefore, to use SDXL with refiner at full resolution, a GPU with 16GB or more is ideal (or you can run the base on GPU and do refiner on CPU sequentially if GPU memory is limited). The demands for training SDXL are pretty large – some reports indicate that even fine-tuning SDXL can require 24GB+ (batch size 1, mixed precision). However, things like LoRA training for SDXL have been demonstrated on 8–12 GB cards by reducing resolution or using optimized attention​.

Since SDXL is about 2.5× times larger than SD1.x, you should scale up your GPU if possible. For the best experience, we suggest a high-end consumer GPU or a workstation card if you use the refiner or do heavy fine-tuning.

The RTX 4080 16GB is a good choice for SDXL if you are budget-conscious but need enough memory. With 16GB, you can load the SDXL base and refiner together (approximately 10–12GB total for both in half precision) and generate 1024×1024 images in one pass. The 4080’s ample CUDA cores give it nearly 50 TFLOPS FP32, which translates to decent speed even for SDXL’s heavier computation. For even better performance, the RTX 4090 24GB is king – it can handle larger batches or higher outpainting resolutions thanks to 24GB, and its ~2× increase in throughput over 4080 means faster image generation. If one were deploying SDXL at scale or doing research training, the NVIDIA A100 80GB becomes relevant; it has massive memory to train the model (80GB could even fine-tune SDXL with batch size >1, which is difficult otherwise) and high memory bandwidth which helps with the large parameter shuffles of diffusion models. In fact, Stability AI’s initial training of SDXL presumably utilized A100 or H100 GPUs in a multi-GPU cluster. That said, for local use, most will stick to consumer GPUs. In summary, to comfortably use SDXL: >=8GB is needed (it will run the base model)​, 16GB is recommended (to use all features), and more GPUs mainly buy speed. You can download SDXL 1.0 from Hugging Face – e.g. stabilityai/stable-diffusion-xl-base-1.0 for the base model and the corresponding SDXL refiner.

StyleGAN2 and StyleGAN3 (GANs for Image Generation – NVIDIA)

StyleGAN2 and StyleGAN3 are generative adversarial network (GAN) models (released 2019–2021) for producing images, famously used for “this X does not exist” demos. They have trainable parameters of 30M–60M (significantly smaller than diffusion models). Still, training GANs is resource-intensive because of high-resolution convolution operations and the need to train two networks (generator and discriminator). The official NVIDIA implementations for StyleGAN2/3 note the requirements as “1–8 high-end NVIDIA GPUs with at least 12 GB of memory”​​. In fact, NVIDIA’s development was done on 8× Tesla V100 16GB in a DGX-1 server​. To train at the maximum 1024×1024 resolution, you ideally want multiple GPUs or one of the newer 40GB+ GPUs. However, the requirements for running inference (generating images from a trained model) are much lower – a single GPU with a few GB can sample images quickly, since the generator model is only ~30M parameters. For example, generating 1024px images with a StyleGAN2 generator can be done on even a 8GB card (especially with half-precision). Due to large activations and batch size, training pushes VRAM usage to 10–12GB or more.

• StyleGAN2 Training: Recommended to have ≥12 GB GPU memory​. A typical scenario: 1 GPU with 12GB can train 512×512 images (but will be slow, e.g. one V100 16GB took about a week for FFHQ 512px)​. With 8GB, it’s possible, but you must lower the batch size (perhaps batch 1 or 2), and training will be even slower​. Multi-GPU helps linearly speed up training – e.g. 2 GPUs with 12GB can roughly halve the time by doubling batch throughput. For 1024×1024 resolution, StyleGAN2’s memory use jumps: 12GB is borderline for batch size 1 at 1024px (people have used 11GB cards for 1024 with optimized training settings, but it’s tight)​. Generally, 1024px training is done with multiple GPUs or GPUs like 24GB RTX 3090 to fit bigger batches.

• StyleGAN3 Training: Similar memory needs as StyleGAN2 (StyleGAN3 adds equivariance but uses a comparable architecture size). NVIDIA’s StyleGAN3 code was tested on V100 and A100 GPUs​. The same rule of “≥12 GB VRAM per GPU” applies. So a single RTX 3080 10GB might struggle at full 1024px (due to falling slightly under 12GB), whereas a 3090 24GB can comfortably handle it.

• Inference: Both StyleGAN2 and 3 generators can run on modest GPUs. For example, generating an image at 1024px might take <1 second on a 3080 and use maybe ~4GB VRAM. So, even a gaming laptop GPU can suffice for deployment of a pre-trained generator. The discriminator network is only used in training.

For training StyleGAN models, consider GPUs with 12GB or more. More GPUs or more memory = faster and higher-resolution training. For inference, almost any recent GPU with a few GB will do; but we’ll focus on the training perspective since that’s where guidance is needed.

The RTX 3080 (10GB) is an entry point for StyleGAN training – at 10GB it’s just under the 12GB recommendation, but users have trained 512px models on such cards by tweaking settings (e.g., using gradient checkpointing or smaller batches). It has nearly 30 TFLOPS of FP32, which is excellent for the convolution-heavy workloads. However, the limited VRAM means you might be restricted to lower resolutions or split your training into smaller increments. The RTX 3090 (24GB), on the other hand, was somewhat of a game-changer for indie GAN researchers: with 24GB, you can train 1024×1024 StyleGANs on a single GPU (batch size 4 or 8 possible) or run two 512px training instances concurrently. Its ample memory and high bandwidth make it as good or better than the older V100. NVIDIA’s Tesla V100 (16GB) was the workhorse for StyleGAN2’s original paper​. One V100 16GB can handle the official configs (often batch size 32 split over 8 GPUs) if you scale down the batch size per GPU. Eight V100s together (as in a DGX-1) effectively give 8×16=128 GB total and massive parallelism, which is how NVIDIA achieved fast training times​. For ultimate training needs, the A100 40GB (or 80GB) is the current top-tier – an A100 40GB could likely train a 1024px StyleGAN with large batch on a single card thanks to its memory and would do so extremely fast (156 TFLOPS in FP16 means it can churn through many images per second). In summary, 12 GB consumer GPUs are the minimum for training StyleGAN2/3​, while 24 GB or multiple GPUs are preferred for high-res or faster turnaround. For inference only, a much smaller GPU (even 4–8GB, or CPU) can generate images; the GPUs listed above are mainly to meet training demands. StyleGAN2/3 code and pretrained models can be downloaded from NVIDIA’s official GitHub repositories: NVlabs/stylegan2-ada-pytorch​ and NVlabs/stylegan3​ (which also provide links to pretrained model weights).

Conclusion

Choosing the “right” GPU for an AI model depends on the model’s size and whether you are doing inference or training. As a rule of thumb, language models in the single-digit billions of parameters (6B–13B) can run on high-end consumer GPUs (12–24GB), whereas very large models (40B–70B) cross into data center GPU territory (A100/H100 with 40–80GB, or multi-GPU splits). Image models like Stable Diffusion are much smaller and run on mid-range GPUs (6–12GB is plenty for inference, 16GB+ if training or using larger variants). Consider both VRAM and compute throughput: enough memory to hold the model (especially for training) and enough TFLOPS to process it reasonably. By mapping model sizes to GPU capabilities, developers and researchers can plan deployments that balance performance vs. budget, ensuring they allocate just enough GPU for the task at hand without unnecessary overspending.