7 Best AMD Motherboards For Dual GPU LLM Builds (2025 Guide)

Local LLM inference is a GPU-intensive task. This is why many users begin exploring multi-GPU solutions, the simplest being a dual-GPU setup. Such configurations and builds require a compatible motherboard and some knowledge of LLM inference using multiple graphics cards. Read on to learn more!

Check out also: Best GPUs For Local LLMs This Year (My Top Picks)

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The Quick Reference Table

(On mobile you can scroll left & right to see the full table).

Does a Multi-GPU Setup Speed Up LLM Inference?

Adding a second GPU can make your local AI chatbot answer you faster, but the real gains from using more than one graphics card for inference lie in multi-user model interactions, batching workloads as well as model training and fine-tuning which are among the more advanced use cases.

The reality is that for a single user, the main benefit of a multi-GPU setup isn’t raw speed, but rather the combined VRAM capacity that lets you run much larger, more sophisticated models.

Let’s dive a little bit deeper into the multi-GPU large language model performance. When we talk about “speed” in LLM inference, we’re dealing with two different things: latency and throughput.

Latency & Throughput

Latency refers to the time elapsed from when an input is provided until the first token of the model’s response is generated and becomes visible. This latency includes both the computation time on GPUs and any necessary data transfer or synchronization overhead between GPUs. When a model is too large to fit on one GPU, its layers or partitions are distributed across multiple GPUs. These GPUs must frequently exchange data and synchronize during inference or training. This back-and-forth communication introduces synchronization delays and data transfer overhead, which can add to the total latency before the first token can be produced.

Throughput, by contrast, measures the total number of output tokens or words the system can generate per second. Using multiple GPUs in parallel can significantly increase throughput, especially when handling multiple requests simultaneously (batching). Frameworks and libraries like vLLM and Hugging Face Accelerate are designed to efficiently utilize multi-GPU setups, thereby improving throughput and overall system performance by splitting workloads and managing parallel execution effectively.

Real Speedups and Capacity

According to benchmarks conducted by Geronimo, performing parallel inference using the Accelerate package both with and without batch processing can substantially increase speed with the right software configuration for multi-GPU calculations. As mentioned in his article:

Using ‘generate’ with batches of prompts speeds up things quite a bit. Increasing the number of GPUs, (…), shows a plateau in performance at 4 GPUs.

https://medium.com/@geronimo7/llms-multi-gpu-inference-with-accelerate-5a8333e4c5db
(‘generate’ is a function within the transformers library)

It is important to note that the efficiency of multi-GPU inference depends heavily on quite a few factors: the inference software framework, model architecture, tokenizer behavior, prompt complexity, batching strategy, as well as underlying hardware characteristics such as memory bandwidth and interconnects. This is why performance improvements from adding more GPUs are not always guaranteed and vary widely depending on these variables.

For a home user, the primary advantage of using more than one GPU for LLM inference is increased capacity. This enables running very large models, such as those with 70B parameters or more, which would not be able to fit into the memory of a single GPU. In other words, a dual-GPU setup allows you to run larger LLMs than would be possible on a single GPU with limited VRAM.

16x, 8x, 4x PCIe Lanes – What’s The Deal With That?

Specs	GT/s	X1	X2	X4	X8	X16
PCIe 1.0	2.5 GT/s	0.5 GB/s	1 GB/s	2 GB/s	4 GB/s	8 GB/s
PCIe 2.0	5.0 GT/s	1 GB/s	2 GB/s	4 GB/s	8 GB/s	16 GB/s
PCIe 3.0	8.0 GT/s	2 GB/s	4 GB/s	8 GB/s	16 GB/s	32 GB/s
PCIe 4.0	16 GT/s	4 GB/s	8 GB/s	16 GB/s	32 GB/s	64 GB/s
PCIe 5.0	32 GT/s	8 GB/s	16 GB/s	32 GB/s	64 GB/s	128 GB/s
PCIe 6.0	64 GT/s	16 GB/s	32 GB/s	64 GB/s	128 GB/s	256 GB/s

When you look at motherboard specifications, you’ll often see terms like PCIe lanes and slot configurations such as x16, x8, or x4. PCIe lanes are the individual, bidirectional data channels, each composed of two pairs of wires that connect PCIe devices like GPUs and NVMe SSDs to either the CPU or the motherboard chipset (Platform Controller Hub, PCH).

The number indicated by the slot (for example, x16, x8, or x4) represents how many data transfer lanes are electrically wired to that slot, defining the data bus width available for communication. More lanes allow more data to be transmitted simultaneously, increasing the potential bandwidth.

PCIe lanes can originate directly from the CPU or from the chipset. Lanes coming directly from the CPU typically have lower latency and higher bandwidth because lanes from the chipset must pass through the link between the CPU and chipset, which adds a small amount of latency and can limit bandwidth.

The bandwidth of each PCIe lane depends on the PCIe generation in use. Each successive generation roughly doubles the bandwidth per lane compared to the previous one. For example, PCIe 3.0 provides about 985 MB/s per one lane, PCIe 4.0 about 1969 MB/s per lane, and PCIe 5.0 approximately doubles that to around 3938 MB/s per lane.

Real-World Implications & LLM Inference

During initial data-intensive operations, such as loading large models, considerable amounts of data are transferred from your HDD/SSD through system memory to the GPU’s VRAM. Having a PCIe slot with more lanes (a wider bus) can help speed up these transfers by providing higher data throughput, which is where significant real-world speed differences can occur.

As Rost Glukhov mentions in his write-up about LLM performance and PCIe lane connections:

PCIe lane count is not a major bottleneck after loading the model. x4 lanes are usually sufficient, though x8 or x16 will reduce loading times.

https://www.glukhov.org/post/2025/06/llm-performance-and-pci-lanes/

He also highlights that lower lane counts (such as x4) are more likely to cause bottlenecks during training or in large-scale deployments, rather than during typical home-use inference scenarios.

In small-scale single-GPU inference tasks, especially with modern PCIe 4.0 and 5.0 standards, using an x8 instead of an x16 slot rarely introduces noticeable performance degradation. That’s because most LLM inference workloads are compute-bound and occur almost entirely within the GPU VRAM, with minimal ongoing data transfer across the PCIe bus.

In multi-GPU inference configurations without high-bandwidth interconnects like NVLink, the PCIe lane count can potentially become more important. When a model is sharded/split across two or more GPUs, its parts must frequently exchange intermediate data. If these GPUs are limited to narrow PCIe connections (e.g., x4), especially on older PCIe generations, this inter-GPU communication could become a bottleneck in more bandwidth-intensive workflows, negatively affecting both inference speed and training performance.

So, Which Configuration Is The Best For Dual GPUs?

For a dual GPU LLM setup, it would be great to have a motherboard that can provide at least PCIe x8 lanes to each GPU, typically described as an “x8/x8” configuration. This arrangement means that the CPU’s PCIe lanes, commonly 16 for mainstream desktop processors, can be split evenly between the two PCIe slots dedicated to GPUs.

Many affordable or older motherboards make use of an “x16/x4” layout, where the second GPU slot receives only 4 PCIe lanes, often routed through the chipset rather than directly from the CPU. While running a GPU with only 4 lanes usually doesn’t cause significant performance degradation for many inference tasks, it can become a potential bottleneck for bandwidth-heavy workloads such as model training and fine-tuning.

Higher-end platforms like AMD Threadripper, Intel Xeon, or AMD EPYC, two of which are proposed below, often offer more PCIe lanes. This allows multiple GPUs to run at full x16 lanes each. Still, for most consumer Ryzen or Intel desktop CPUs, the common maximum configuration for dual GPUs is full x8/x8 bandwidth.

Every card listed below will have its effective main GPU PCIe slot lane configuration indicated underneath.

The List – Best AMD Motherboards For a 2x GPU Local LLM Build

Here are all the motherboards I’ve researched and compared, along with detailed additional information on each.

For some of the motherboards, I will also include the projects and builds where they have been successfully used.

Why this order? The only TRX50 Threadripper-based board on the list comes first. After that come the X870E/AM5 boards which offer most of the latest features and adequate PCIe connections available, followed by the X670E generation. Lastly, an X570/AM4 motherboard is mentioned as a reliable and still budget-friendly option. Let’s get started.

1. GIGABYTE TRX50 AERO D

• Form factor: E-ATX
• Chipset: AMD TRX50
• CPU Socket: sTR5 (for AMD Threadripper 7000 series)
• GPU PCIe Slots: 3x PCIe 5.0/4.0 x16 slots (capable of running at x16/x16/x16 depending on CPU and configuration)

If budget isn’t a concern and you’re after the absolute best performance, the TRX50 AERO D stands in a league of its own. This high-end HEDT motherboard built for AMD’s Threadripper CPUs, is one of the most popular solutions for multi-GPU workloads. It offers two full-speed PCIe 5.0 x16 slots and one PCIe 4.0 x16 slot, with a reasonable amount of space between them. This means you can easily fit three high-performance GPUs on it, knowing that none is held back by a lack of bandwidth.

As stated on the Gigabyte website, it’s designed for professional creators, data scientists, and AI enthusiasts who need maximum power and expandability. With support for up to 1TB of quad-channel DDR5 memory and server-grade connectivity, this board is one of the best foundations for an ultimate local AI rig. It also offers a dual LAN 10G+2.5G connection plus Wi-Fi 7, and dual USB4 Type-C ports. Here you can see an example build featuring the TRX50 AERO D.

2. ASUS ProArt X870E Creator Wi-Fi

• Form factor: ATX
• Chipset: AMD X870E
• CPU Socket: AM5 (for AMD Ryzen 9000, 8000 and 7000 series)
• GPU PCIe Slots: 2x PCIe 5.0 x16 slots (x16 single or x8/x8 dual), 1x PCIe 4.0 x16 slot (max. x4)

The ProArt X870E-Creator Wi-Fi is one of the ASUS’s best motherboard models for professionals and creators on the new X870E platform, and it’s a really good pick for dual-GPU AI setups. It features two PCIe 5.0 x16 slots that can operate in a x8/x8 configuration when both are populated, which is, as we’ve established, what you should be aiming for when attempting to use two cards for local model inference.

3x Gpu Asus proArt x870e
byu/PawelSalsa inLocalLLaMA

Aside from the optimal slot configuration, you get a 10Gbps Ethernet port for fast network access, a second 2.5Gbps port, dual 40Gbps USB4 ports, and 4x DDR5 DIMM slots for up to 192GB dual-channel memory support. This build showcased on r/LocalLLaMA shows a rather creative use of this motherboard in a multi-GPU inference setup project, which also showcases an experimental 5-GPU, 120GB VRAM setup based on this board.

3. GIGABYTE X870E AORUS Xtreme AI TOP

• Form factor: E-ATX
• Chipset: AMD X870E
• CPU Socket: AM5 (AMD Ryzen 9000, 8000 and 7000)
• GPU PCIe Slots: 2x PCIe 5.0 x16 slots (x16 single or x8/x8 dual), 1x PCIe 4.0 x16 slot (max. x2)

GIGABYTE is leaning heavily into the “AI” branding in its higher-tier product lines, and the X870E AORUS Xtreme AI TOP is a great example of this. This board is built with an E-ATX form factor and offering two PCIe 5.0 x16 slots with x8/x8 support nicely fit for a dual-GPU setup. The third PCIe slot, although physically a x16, is limited to x2 transfer speeds. You can check out this video to see how exactly the lanes are distributed.

With three more fan headers, a higher 256GB max. memory limit, and a larger amount of USB ports it’s arguably on the same level as our previous contender, at the same time being more expensive. Depending on the deal, in my opinion both of these are well worth considering.

4. MSI MPG X670E Carbon Wi-Fi

• Form factor: ATX
• Chipset: AMD X670E
• CPU Socket: AM5 (AMD Ryzen 9000, 8000 and 7000)
• GPU PCIe Slots: 2x PCIe 5.0 x16 slots (running at x8/x8), 1x PCIe 4.0 slot (at x4, chipset-based)

The MSI MPG X670E Carbon Wi-Fi is a good example of a high-end gaming motherboard that happens to be well fit for dual-GPU AI tasks. It features two PCIe 5.0 x16 slots running at x8/x8, and an additional PCIe 4.0 x16 slot running at x4 from the chipset. It’s also very reasonably priced, being the first real budget option on our list.

Speed metrics running DeepSeekV3 0324/Qwen3 235B and other models, on 128GB VRAM (5090+4090×2+A6000) + 192GB RAM on Consumer motherboard/CPU (llamacpp/ikllamacpp)
byu/panchovix inLocalLLaMA

While it doesn’t include some of the more recent features found in the ProArt series, such as 10GbE LAN or USB4, it still offers fast 2.5Gbps LAN, Wi-Fi 6E, and a high 256GB maximum memory capacity. For anyone building a dual-GPU rig suitable for both LLM inference and gaming, this board is another excellent choice. Here is a great example of a 4-GPU build based on the X670E from MSI.

5. ASUS ProArt X670E Creator Wi-Fi

• Form factor: ATX
• Chipset: AMD X670E
• CPU Socket: AM5 (AMD Ryzen 9000, 8000 and 7000)
• GPU PCIe Slots: 2x PCIe 5.0 x16 slots (x16 single or x8/x8 dual), 1x PCIe 4.0 x16 slot (max. x2)

Before the X870E Creator arrived, the ProArt X670E Creator was a noteworthy motherboard for many dual-GPU workstation builders on the AM5 platform, and for good reason. It offers nearly all the key benefits of its newer sibling, including almost identical storage and I/O options, as well as the crucial dual PCIe 5.0 x8/x8 slot configuration that ensures sufficient bandwidth for two GPUs.

This board set the standard for what a modern high-end AM5 motherboard should be, featuring a 10Gbps LAN port, a 2.5Gbps LAN port, dual USB4 ports, and Wi-Fi 6E. It still remains a feature-rich model worth considering, especially if you can find it at a discount now that its successor is available.

6. ASUS Pro WS X570 Ace

• Form factor: ATX
• Chipset: AMD X570
• CPU Socket: AM4
• GPU PCIe Slots: 3x PCIe 4.0 x16 slots (capable of running at x8/x8/x8)

For those looking to build a powerful dual-GPU system without breaking the bank, the ASUS Pro WS X570 ACE on the older AM4 platform is one of a few hidden gems. While it uses the previous-generation X570 chipset and relies on DDR4 memory (max 128GB), its key strength is a proper triple PCIe 4.0 x8/x8/x8 slot configuration, which is a feature that was relatively rare and highly sought-after on consumer boards of its era.

This board allows you to utilize a wide range of affordable AM4-compatible CPUs, including the high-performing Ryzen 5000 series, as well as use cheaper DDR4 RAM, while still providing both of your GPUs with a high-bandwidth connection. As a neat addition, it also supports 2-way SLI and 3-way Crossfire GPU configurations. Although it lacks modern features like PCIe 5.0 and USB4, for a pure inference machine where VRAM capacity and balanced GPU links are priorities, this board offers incredible value and has thus earned its place on our list.

7. ROMED8-2T/BCM ATX

• Form factor: ATX
• Chipset: System on Chip (SoC) integrated in the AMD EPYC platform, no separate chipset chip
• CPU Socket: SP3 (for AMD EPYC)
• GPU PCIe Slots: 7x PCIe 4.0 x16 slots

Taking a closer look at the world of server hardware can unlock possibilities that consumer boards simply can’t offer, and the ASRock Rack ROMED8-2T is a great example. Designed for AMD EPYC server CPUs (which are available on Amazon at reasonable prices), this motherboard is an absolute workhorse built to run multiple graphics cards for professional workloads. It boasts an incredible seven PCIe 4.0 x16 slots, making a dual-GPU setup really feel like an afterthought. Still, I thought it would be a good choice to finish this list with, to broaden your horizons.

While it’s not a plug-and-play consumer board per se, it offers unmatched expansion potential, which you should expect from server-grade boards. Features like the dedicated IPMI port for remote management and a dual 10GbE connection are very useful in an advanced homelab environment. However, remember: you’ll need ECC RDIMM DDR4 memory for this board, as well as some patience during setup—both expected with this kind of hardware.

Which Graphics Cards Are Best For a Dual GPU LLM Inference Setup?

When choosing GPUs, your main goal should be to maximize VRAM while focusing on relatively new hardware. NVIDIA graphics cards are generally the safest bet; however, if you’re interested in options from AMD or the newest Intel GPUs, many users successfully run these alongside compatible inference software.

When it comes to NVIDIA cards, the RTX 3090 and 3090 Ti 24GB remain the best price-to-performance picks, offering a massive 24 GB of VRAM at prices often much lower than newer cards. Two of these cards provide 48 GB of VRAM, which is enough for many of the larger models.

If your budget allows, the NVIDIA RTX 4090 24GB is also an excellent choice. While the RTX 5090 32GB would be even better, its prices at least for now make it much less reliable for any larger consumer multi-GPU builds.

You can learn much more about the best graphics cards for local LLMs here: Best GPUs For Local LLMs (My Top Picks)

What Are The Other Things You Should Know When Building This Kind of a Setup?

First, your power supply unit needs to be able to provide enough power to all of your GPUs at full load. For a dual RTX 3090 setup for instance, you would need at least a 1000/1200W PSU, depending on other system components in use.

The second important thing is airflow, especially if you’re using a case with limited space. Two powerful GPUs will generate a significant amount of heat even when not running at full power, so keep that in mind when planning out your setup to prevent thermal throttling. Also, consider purchasing additional case fans.

While many modern motherboards provide enough physical space for most dual GPU configurations, it is important to verify the actual clearance between PCIe slots before purchasing. The challenge is not only about ensuring adequate airflow but also whether the GPUs physically fit side-by-side, especially since high-end GPUs often occupy two or three expansion slots each.

Some motherboards have nearly ideal slot layouts that offer one or two empty slots between GPUs, which helps with cooling and fitting large cards. Most of the boards on this list in fact do. If spacing is tight, GPUs may be too close for effective airflow, causing higher temperatures. To address this, some users modify GPU shrouds or use liquid cooling to reduce the GPU’s physical profile.

Always check manufacturer specifications for both GPU slot width and motherboard slot spacing to avoid fitment issues. There are a few great resources online on how to check the card size compatibility yourself.

What If You Need More Than Two GPUs?

Once you go beyond two GPUs, you may want to consider moving beyond consumer-grade motherboards. While multi-GPU builds exceeding 2-3 cards are possible on some regular high-end and “gaming” motherboards (such as the previously mentioned 5x GPU setup on the ASUS ProArt X870E), they often require some hardware hacks to get going.

Depending on the consumer motherboard you choose, these setups usually are not able to provide the full data transfer speeds and bandwidth your cards can deliver. This may or may not be an issue, depending on the inference software you’re using, cards you have on hand or plan to purchase, and your exact project requirements.

Motherboards like the GIGABYTE TRX50 AERO D, ASRock Rack ROMED8-2T, and a few other market options that offer full-speed PCIe slots are what you should target if you want to build a 3+ GPU system without compromising your PCIe data transfer speeds.

Hope that this guide was helpful to you, and I hope to see you next time when we’ll be talking more about specific GPUs for parallel processing. Until then!