Running large language models (LLMs), high-resolution Stable Diffusion or FLUX generations, or complex voice and video AI workflows efficiently requires a significant amount of GPU Video RAM (VRAM). This is one of the most important hardware specifications when choosing a graphics card for any kind of local AI work involving large models or datasets. Let’s see what options you have, both when it comes to regular consumer, workstation, and enterprise cards & accelerators.
You might also like: Best GPUs For Local LLMs This Year (My Top Picks – Updated)
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What You Need To Know About These Cards
High VRAM is the primary requirement for any local AI application, whether you’re working with large language models, running high-resolution image generation models like SDXL or Flux generating large batches of images, training your own LoRA models, or utilizing LLM x local voice generation model combos for your local AI chatbots.
The fast video memory on your GPU needs to hold the entirety of the model’s weights, along with large buffers for the model outputs, as well as other kinds of temporary data needed for the immediate calculations. The details of the VRAM usage will of course depend on what kind of exact models and workflows you’re dealing with.
You might also like: LLMs & Their Size In VRAM Explained – Quantizations, Context, KV-Cache
When a model’s total memory footprint exceeds your VRAM capacity, regardless of your particular workflow the software will, depending on the situation and setup, either throw an out-of-memory error of some kind, or attempt to offload data to your system RAM to continue inference without crashing. This process can cause a dramatic drop in performance, which, as we all know, is often problematic.
For this reason, a capable card for local AI inference should offer:
- High VRAM Capacity: 16GB is the functional minimum at this point in time, but 24GB and up is what you would be aiming for when planning out a high end local inference setup.
- High Memory Bandwidth: Faster data transfer between the GPU core and the memory chips ensures quick token generation speeds (inference throughput). Although this doesn’t matter for all local AI workflows, it’s especially important when you’re dealing with frequent model switching, as well as multi-GPU setups.
You can learn more about offloading data to shared system memory (RAM) here: What Is The Shared GPU Memory In The Task Manager?
Dual GPU Setup – It’s Easier Than It Looks
For users who need more memory that a single card in their preferable format and price range can offer, those who are targeting larger, often multi-modal model setups, or those building more complex multi-user environments, a single card VRAM will likely not be enough. Scaling VRAM is a common strategy, achieved by running two or more GPUs together in an inference setup.
You can scale your total VRAM by linking two compatible cards on a motherboard with sufficient PCIe lanes and slot spacing. While some models use NVLink for ultra-high-speed memory pooling, modern software frameworks like llama.cpp and PyTorch can efficiently split the model’s layers across different GPUs without an additional hardware link like this.
Planning your build to support multiple cards, specifically checking motherboard clearance, available PCIe x16 slots, and the system’s power supply, is a critical step. If you plan to scale with multiple consumer cards, feel free to check out my articles on the best GPUs for dual-GPU LLM setups and choosing the right AMD motherboard for LLM builds.
You might also like: 7 Best AMD Motherboards For Dual GPU LLM Builds (Beginner’s Guide)
Best High VRAM GPUs – Consumer, Workstation & Enterprise Models
Below, I organize all of the most recent high-VRAM graphics cards into Consumer, Prosumer/Workstation, and Enterprise model categories to reflect their intended use, form factor, and what’s probably most important, their cost.
Consumer cards often provide the best raw VRAM-to-price ratio. Workstation cards are generally much more expensive but come with significantly more video memory. Enterprise accelerators will give you maxed out memory capacity and multi-GPU scalability, however, they typically cannot be used in your average home setup. The cards are listed from best to worst performer in each category, which usually corresponds with both their release dates and prices.
Consumer GPU Models
This category includes high-end GPUs primarily designed for desktop gaming, but offering the highest VRAM capacity currently available in the consumer space. They offer the best raw VRAM capacity for their price, although these days they are still very much expensive.
1. Nvidia RTX 5090 – 32GB
- 32GB of GDDR7 VRAM on board.
- Top-tier performance for local AI software and gaming.
- Very high power draw (~575W top).
- Comes with a very high price tag.
The NVIDIA RTX 5090 is the first NVIDIA card from the consumer GPU line-up to break the 24GB barrier, offering 32GB of GDDR7 video memory and a 512-bit memory bus. Its price however, is high enough to make many potential buyers steer either towards older NVIDIA GPU models (like the ones listed below), or other cards from the 5xxx series. Regardless of that, currently it’s the top option from this category when it comes to sheer VRAM capacity.
2. Nvidia RTX 4090 – 24GB
- 24GB of fast GDDR6X VRAM.
- Excellent all-around performance for AI and gaming.
- High memory bandwidth.
- Still very high price, though less than that of the 5090.
Currently the second best consumer NVIDIA GPU on the market if you’re planning to build a high-end all-in-one AI/gaming workstation. Its 24GB of GDDR6X VRAM, 384-bit memory bus width and 1,008 GB/s (~1 TB/s) max bandwidth give you a lot to work with, and its price, although also pretty high, is much less than that of a brand new 5090. With that said, let’s get to the first “budget” option on this list.
If you want to learn more about GPUs you can use for many local AI workflows without breaking the bank, this might be a useful resource for you: Top 7 Best Budget GPUs for AI & LLM Workflows
3. Nvidia RTX 3090/Ti – 24GB
- Excellent VRAM-to-price ratio (especially used).
- 24GB of GDDR6X VRAM and same memory speed as the 4090.
- Last consumer NVIDIA GPU to support NVLink.
- No longer in production.
- Older architecture compared to the 40-series.
While coming from an older generation, the RTX 3090 (and 3090 Ti) are one of the most popular budget cards out there, although they are no longer in production. They retains the crucial 24GB of GDDR6X VRAM with a 384-bit memory bus at max ~1 TB/s bandwidth (the very same as the much newer 4090 [!]), and features an NVLink connector, which could come in handy for some of you.
Overall, you can’t really find a more cost-effective card if you’re going for 24GB of VRAM, and you’re set on the NVIDIA hardware. You can find many good deals for second-hand 3090 units in many places online (for instance here, over on eBay), so keep an eye out for some good deals.
4. AMD Radeon RX 7900 XTX – 24GB
- 24GB of GDDR6 VRAM.
- More budget-friendly than NVIDIA counterparts.
- Improving software and driver support for AI.
- ROCm framework can still be a challenge for some AI apps.
The AMD Radeon RX 7900 XTX from the previous generation that offers 24GB of GDDR6 VRAM (384-bit bus at ~960 GB/s) and competes directly with the 4090 and the 3090 in terms of memory capacity and speed.
While historically AMD cards have been very challenging for most local AI workflows due to reliance on the ROCm framework, software support for AMD cards has improved significantly in recent years, so many people naturally turn to them as a more wallet-friendly option. It’s a great pick considering that the newer, 9xxx generation of the AMD GPUs sadly lacks a 24GB model in the lineup.
5. Intel Arc A770 – 16GB
- Excellent price for 16GB of VRAM.
- Growing support through the oneAPI ecosystem.
- Maturing drivers can lead to compatibility issues.
- Lower raw performance than its other competitors.
The Intel Arc A770 is listed here for a reason, although it’s neither the most popular, or the most powerful card out there. It offers 16GB of GDDR6 VRAM – 256-bit bus with 512 GB/s memory bandwidth, at a price point well below that of a 4090 or a 3090. The price of a brand new Intel Arc A770 is usually just under $300.
Its support for the open-source oneAPI ecosystem continues to grow, positioning it as a strong choice for those who aren’t afraid of some potential software compatibility issues as both the drivers and the software side of Intel GPUs matures. You can find more details on this card in my full breakdown of Intel Arc GPUs for local AI here: Intel Arc B580 & A770 For Local AI Software – A Closer Look.
Prosumer / Workstation GPUs
These cards are mostly based on the same core architectures as their consumer counterparts but feature enhanced VRAM capacity, and much higher market prices anywhere from one, to a few thousand USD.
Cards like these are most often meant to be used for working on AI/Machine Learning projects, in professional 3D Design and Rendering workflows, scientific simulations, and so on. They might require additional driver setup and in rare cases might not be compatible with some of the less popular open-source local AI applications out of the box.
6. Nvidia RTX 6000 Blackwell – 96GB
- Massive 96GB of next-gen GDDR7 VRAM.
- Supports NVLink Gen 5 for scalability.
- Top-tier performance for professional workflows.
- Extremely expensive.
- Requires a well thought-out workstation setup.
This card is a direct successor to the Ada-generation workstation RTX 6000, which is coming up next. The Nvidia RTX 6000 Blackwell with its 96 GB of GDDR7 VRAM, a 512-bit memory bus width, and quite impressive memory speed of 1792 GB/s (~1.8 TB/s) is one of the most expensive and best performing workstation GPUs from the latest NVIDIA lineup. It also supports NVLink Gen 5.
Provided your software setup and workflows support it, this one is among the absolute best cards you can get for big data projects and local AI model fine-tuning. If you’re interested, you can read more about people adapting this particular card model to their local LLM setups over on the r/LocalLLaMA subreddit. There are already quite a few builds featuring it that you can take a look at!
7. Nvidia RTX 6000 Ada – 48GB
- High 48GB VRAM capacity.
- Great performance for memory-intensive workloads.
- Still a very high price point.
- Lacks NVLink support.
This is currently the second highest VRAM workstation card from NVIDIA. The Nvidia RTX 6000 Ada, which is the predecessor of the RTX 6000 Blackwell, features 48GB of GDDR6 VRAM, a 384-bit memory bus, and a total max bandwidth of 960 GB/s. With all this however, it has no support for NVLink.
It’s the second best option for professional workstations from NVIDIA in our menu, and is still quite a popular GPU for advanced use cases and applications. With that said, let’s move on to the workstation cards from AMD, which come at lower price points, but with a fixed amount of 32GB VRAM each.
8. AMD Radeon AI PRO R9700 – 32GB
- 32GB of GDDR6 VRAM with ECC support.
- More affordable than the NVIDIA workstation options.
- “Only” 32GB of VRAM on board.
- Still a significant investment.
Part of AMD’s latest professional line, the AMD Radeon AI PRO R9700 can grant you 32GB of GDDR6 VRAM, a 256-bit memory bus width, and a memory speed delivering 640 GB/s. It also comes with certified drivers and ECC support.
With its lower price point, it’s a little bit more affordable than the options from NVIDIA, however it’s still a proposition that will be mostly appealing to those of you who are more geared towards GPU-intensive workflows, rather than gaming or light local AI inference tasks. The price, although still high, can get a little bit lower than this with the previous Radeon PRO GPU that is up next.
9. AMD Radeon PRO W6800 – 32GB
- 32GB of GDDR6 VRAM.
- One of the least expensive recent workstation options from AMD.
- Lower memory bandwidth than the R9700.
The AMD Radeon PRO W6800, which made it onto my list of the best AMD cards for local AI is one of the least expensive and reasonably recent workstation GPUs from AMD. Still, getting a brand new one will cost you anywhere from 2-3 thousand dollars. It offers 32 GB of GDDR6 VRAM, a 256-bit memory bus width, and a memory bandwidth of 512 GB/s.
This is the very last workstation GPU on this list, and at the same time it marks our switch to the enterprise-grade graphics cards and accelerators. Even if these are not something you would ever use, having a reference for what the biggest data centers in the world use for handling extensive multi-user workloads is, at least in my honest opinion, quite interesting. Take a look.
Enterprise GPU Models
These are high-VRAM GPU accelerators designed for data centers, cloud providers, and large-scale AI training and inference operations. If you ever think about training or hosting a model close to the magnitude of the latest OpenAI or Google LLMs, this is the hardware you’d need.
These type of cards are available in versions with High Bandwidth Memory (HBM) interfaces for extreme data speeds but require specialized cooling and infrastructure. Their prices often exceed the four-figure range.
10. NVIDIA H100 – 80/94GB
- Top VRAM capacity (80-94GB HBM3/HBM2e).
- Built for massive multi-node clusters.
- Unmatched performance for large-scale LLM training.
- Prohibitively expensive for personal use, and requires specialized infrastructure.
This is currently one of the top cards used for large scale multi-GPU computation setups. The NVIDIA H100 with 80-94GB depending on the SXM/NVL version, uses high-speed HBM3/HBM2e memory and features an huge 5120-bit memory bus, resulting in multiple terabytes per second of bandwidth.
This card is built for massive, high-speed operations and multi-node clusters via NVLink and InfiniBand. It is the gold standard for large-scale LLM training and cloud inference, though it is prohibitively expensive for consumer or single-user desktop setups.
11. NVIDIA A100 – 40/80GB
- High VRAM capacity (40-80GB HBM2e).
- Proven performance in data centers.
- More accessible than the H100.
- Superseded by the H100.
- Requires enterprise-level infrastructure.
While superseded by the H100, the NVIDIA A100 remains used in many data centers all around the world. It uses HBM2e memory with 40-80GB available with 1.6-2 TB/s bandwidth depending on the exact model, and is the second best player in the enterprise NVIDIA GPUs market.
12. NVIDIA A800 – 40GB/80GB
- Strong single-GPU performance similar to the A100.
- Same 40/80GB VRAM capacity as the A100.
- Capped NVLink speed limits multi-GPU scaling.
- Designed for specific export markets.
The NVIDIA A800 is quite interesting. It’s a variant designed specifically to comply with US export restrictions on AI hardware. It is essentially an A100 with a capped high-speed interconnect bandwidth (NVLink speed). It has the same VRAM capacity as the A100: 40 GB or 80 GB of HBM2e memory depending on the model. This limits some multi-GPU scaling but retains strong single-GPU AI performance. And with that, our list is closed!
My Short Conclusion
Efficiently running large AI models and demanding workflows locally requires GPUs with a sufficient amount of VRAM and high memory bandwidth to accommodate model weights and large temporary data without performance loss.
When selecting a GPU, always consider your real workload scale – consumer cards offer good VRAM-to-cost balance for many users, workstations provide higher VRAM and professional features at a premium, and enterprise GPUs deliver extreme VRAM and bandwidth for large-scale AI deployments but at significantly higher cost and infrastructure requirements.
Matching your GPU choice to your specific AI tasks and budget ensures the best balance of performance, compatibility, and cost-effectiveness for local AI workflows. Thank you for reading!