Memory is starting to matter as much as raw compute power for running artificial intelligence (AI) models on a computer. Unified memory, open models, and new inference engines are expanding what home and professional machines can do, to the point that several machines can now work together to run models too large for a single PC. Still, having more memory doesn’t guarantee higher speed on its own: bandwidth, processor architecture, and software optimization matter too.
Local AI in 20 seconds
- Unified memory lets the CPU and GPU share physical memory.
- Open models, such as Qwen, can run locally with compatible tools.
- AMD has documented a test using four 128GB machines to run Kimi K2.5.
- ArcLight aims to improve inference on many-core processors.
- Running AI locally cuts reliance on external services, but requires matching the model to the available hardware.
This shift responds to a familiar technical limitation: large language models need memory to store their parameters and keep track of each conversation’s context. When a model exceeds the capacity available in graphics memory, the system has to fall back on alternatives such as splitting the work between CPU and GPU, using quantized models, or spreading inference across multiple machines.
In that scenario, unified memory offers a different path than computers that rely on a graphics card with dedicated memory. Its usefulness, though, depends on the platform and on how well the software makes use of the available resources.
Unified Memory: More Capacity Available, Not Always More Speed
In a unified memory architecture, different processing units can access the same physical memory. This avoids necessarily having to keep separate copies of data in system memory and graphics memory, though the exact behavior depends on the implementation.
On traditional machines, a dedicated GPU’s RAM and VRAM are separate resources. If a model doesn’t fit in the graphics card’s memory, the system can split its components across different types of memory, but that split can reduce performance. Accessing RAM through the CPU doesn’t offer the same conditions as working directly with the GPU’s memory.
Unified memory lets certain machines with an integrated GPU devote a sizable share of system memory to AI tasks. That can be useful for loading larger models without installing a dedicated graphics card with an equivalent amount of VRAM.
But capacity isn’t the only factor. Memory speed, the number of channels, GPU architecture, and the performance of the compute cores all affect token generation. Context size matters too, since the memory needed to handle long conversations can grow significantly.
That’s why comparing two computers purely on RAM capacity can be misleading. A machine with a lot of memory may be able to load a model that won’t fit on another system, but that doesn’t necessarily mean it will respond faster. On top of that, resources used by the operating system and other applications reduce the memory actually available for inference.
AMD Shows Several PCs Can Run a Massive Model Together
One of the most striking technical examples comes from AMD. In an article published on February 25, 2026, the company documented how to build a small inference cluster using four Framework Desktop machines, each equipped with an AMD Ryzen AI Max+ 395 processor and 128GB of memory.
The test used Ubuntu 24.04.3 LTS, ROCm, and a version of llama.cpp with support for machine-to-machine communication via RPC. The model chosen was Kimi K2.5, from Moonshot AI, using a quantized version of roughly 375GB.
The setup split the work across the four systems and used a 5Gbps Ethernet connection. According to the documentation, the goal was to treat the machines as a logical accelerator to locally run a trillion-parameter-class model.
The test shows that a model too large for a single machine can run on a distributed architecture. It doesn’t mean, however, that any computer with 128GB of RAM can do this on its own, or that four machines of this kind are equivalent to a high-end professional GPU.
The setup requires specific memory, driver, and software tweaks. AMD also explains how to increase usable graphics memory through Linux system parameters. This is a technical demonstration published by the vendor, not an independent benchmark that can be extrapolated to every scenario.
For developers and research teams, this kind of setup makes it possible to experiment with large models without necessarily relying on dedicated GPU infrastructure. In exchange, it introduces new variables: communication between nodes, power consumption, network configuration, and the added complexity of keeping several machines running in a coordinated way.
Inference Engines and Open Models Matter Too
Hardware is only part of the equation. Advances in inference engines make it possible to get more out of conventional processors and shared-memory architectures.
One project working in this direction is ArcLight on GitHub, an inference engine written in C and C++ aimed at systems with unified memory and many-core processors. Its version 1.0 adds optimizations for multi-node memory access (NUMA) platforms, an architecture in which latency can vary depending on where data sits relative to the core that needs it.
ArcLight says its system can reach up to 50% higher generation throughput than llama.cpp with the same number of cores, and can double performance on certain machines. Those figures come from tests reported by its developers and shouldn’t be read as a guaranteed improvement on any processor or model.
The project uses models in the GGUF format, the same format popularized by llama.cpp, and supports both ARM and x86 processors. Its documentation recommends starting with small models, such as Qwen3-4B, to test the setup. The project is still under development and still lists areas it needs to improve, including model coverage and certain system optimizations.
At the same time, open models make it easier for users to choose the size and format that fit their machine. The official Qwen3 repository includes instructions for running models locally with tools like llama.cpp and LM Studio, along with documentation for larger-scale inference deployments.
Quantization also plays an important role. This technique reduces the precision used to represent a model’s parameters and, with it, its memory footprint. The savings can make it possible to run larger models on the same hardware, though the result depends on the method used and its effect on quality and performance.
Another approach involves splitting a model across several computers. A study posted on arXiv in August 2026, titled Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets, describes an architecture that divides a model into stages and distributes its execution across machines with Intel processors.
The authors say two machines were able to run a Llama 3.1 8B pipeline quantized to INT4 with two concurrent users and an aggregate throughput 1.79 times higher than a single-user reference setup. The paper also describes a four-machine setup running a 70-billion-parameter model at a speed the authors consider interactive. These are experimental results for one specific architecture, not a performance guarantee for any home network.
What This Means for Developers and Businesses
The combination of abundant memory, open models, and optimized engines widens the options for running AI without sending every query to an outside provider. That can be useful for software development, analyzing internal documents, or testing models that need to work with data organizations would rather not send elsewhere.
Running AI locally also allows working offline, as long as the model and the necessary tools are already installed. Even so, it doesn’t automatically eliminate every privacy risk: local software can still send telemetry or connect to external services if configured to do so, so it’s worth checking how each application actually behaves.
When choosing a machine, memory should be weighed alongside bandwidth, CPU and GPU performance, inference engine compatibility, and model size. It’s also worth checking the memory needed for the expected context length and for running multiple sessions at once.
The trend doesn’t mean every user needs a 128GB machine or a home cluster. Small, quantized models can handle plenty of tasks with far more modest requirements. Setups with abundant memory or multiple nodes make sense when model size, context length, or experimentation needs justify the added cost and complexity. For more on balancing memory and hardware choices for local AI, see our look at how the memory crisis is reshaping the PC for local AI.
Frequently Asked Questions
What’s the advantage of unified memory for running AI models?
It lets the CPU and GPU share physical memory, which can help load models that wouldn’t fit in a conventional machine’s dedicated graphics memory. Final performance also depends on bandwidth and hardware architecture.
Can Kimi K2.5 run on a home computer?
The demonstration documented by AMD uses four machines with 128GB of memory each and a specific software and network setup. It doesn’t show that the model can run under the same conditions on a single home computer.
What is ArcLight and what is it for?
ArcLight is an open-source inference engine aimed at systems with unified memory and many-core processors. Its developers publish performance gains over llama.cpp in certain configurations, though results depend on the hardware and the model.
What do I need to run Qwen locally?
It depends on the model chosen and the computer’s resources. Qwen3’s official documentation offers instructions for using tools like llama.cpp and LM Studio, plus options for larger-scale inference deployments.
