Do You Really Need CUDA For Local LLMs? – Here Are The Alternatives

When you’re starting to think about preparing your hardware setup for setting up any local LLM inference software of your choice, the question of whether you need an NVIDIA card (and therefore CUDA), is likely one of the first you’ll face. For a long time, the answer was quite obvious. Today, the landscape is much more complex, and frankly, much more interesting for budget-minded users and AMD/Intel GPU owners. Here is all you should know.

This guide breaks down exactly what CUDA is, how it compares to its rivals (ROCm, oneAPI, Vulkan), and shows you how the choice can impact your performance, software selection, and overall workflow. Let’s get to it.

TL;DR for the impatient: For local LLMs on Windows and Linux, CUDA still delivers the smoothest setup, the widest software support, and the highest single-GPU performance in most contexts and use cases. But you don’t really need CUDA to run great local models. ROCm (AMD), Vulkan (cross-vendor), DirectML/ONNX Runtime (cross-vendor), and oneAPI/SYCL (Intel), now make non-CUDA paths reasonably good choices, if you know the trade-offs involved.

If your primary goal is the easiest setup and the fastest performance out-of-the-box, an NVIDIA CUDA-enabled card is still the top choice. If your goal is cost-efficiency for VRAM capacity, other options are now completely viable for many of the most popular use cases.

Quick Glossary – Key Terms Explained

The GPU Landscape in 2025 (NVIDIA vs. AMD vs. Intel)

While the market still remains skewed toward NVIDIA for almost all local AI related applications including local LLM inference, the competition from the AMD and Intel side is growing rather fast, both in terms of newly released hardware, and the driver/software-side compatibility.

NVIDIA (CUDA) offers the most robust software stack, Tensor Core acceleration for mixed-precision math, and is still the universal choice for both model training and inference. The ecosystem is fully mature, and in most cases lets you take the “plug-and-play” approach with most local AI software. The topic of extending the CUDA capabilities beyond the NVIDIA hardware is still an ongoing research problem.

AMD (ROCm) GPUs are in the majority of cases much more cost-effective for a given amount of VRAM (especially the 7900 series), which is a key metric for running larger, higher quality models. While historically a Linux-first platform, ROCm is steadily improving on Windows for consumer AMD cards, rather quickly closing the performance gap in memory-bound AI-related workloads.

Intel (oneAPI/SYCL) Arc GPUs are one of the newest and most interesting budget solutions on the GPU market. Their primary challenge is, software maturity, as many developers are still optimizing their software for the SYCL/oneAPI environment. If you want to know more about these, take a look over here: Intel Arc B580 & A770 For Local AI Software – A Closer Look

What “CUDA”, “ROCm”, “oneAPI/SYCL”, “Vulkan”, “DirectML”, and “Metal” Actually Are

All these are not LLM programs per se, although they are directly related to the local LLM software frameworks you’ll decide to use. They are the fundamental compute languages/GPU compute APIs that tell a graphics card how to handle general-purpose parallel computing tasks like for instance matrix multiplication, which is the core of LLM inference.

CUDA is NVIDIA’s proprietary, closed-source parallel computing platform that locks developers who decide to use it into the NVIDIA ecosystem, and provides them with great tools to handle low-level maths needed to create and develop inference software for various for AI workloads.

ROCm is AMD’s open-source alternative to CUDA, utilizing the HIP programming model (Heterogeneous-Compute Interface for Portability), which often allows developers to translate CUDA code to be compatible with the AMD hardware.

oneAPI / SYCL is a cross-architecture standard developed by Intel to enable developers to write code once and run it across various hardware accelerators (CPU, GPU, FPGA, NPU) from different vendors, with SYCL being the underlying programming model.

Vulkan is primarily a graphics API, but its powerful compute pipeline can be used for non-graphical tasks. Backends like llama.cpp leverage this to run LLM inference on any modern GPU (NVIDIA, AMD, or Intel) that supports the Vulkan standard, making it the one of the most popular universal GPU paths.

DirectML is Microsoft’s answer for accelerated machine learning on Windows, which works across NVIDIA, AMD, and Intel hardware, often providing the most stable path for non-NVIDIA cards on Windows.

Finally, Metal is Apple’s proprietary compute API, which provides outstanding performance due to the tight hardware/software integration of Apple Silicon (M-series chips), with unified memory.

While cross-platform solutions designed for hardware-agnostic compatibility with NVIDIA hardware and its CUDA ecosystem are widespread and they get better month-by-month, they still aren’t a perfect solution for the NVIDIA’s market dominance in the broad AI/ML fields.

“Cross-platform solutions like HIP and SYCL often require extensive code rewrites, and while they provide tools to transform CUDA code into their APIs to reduce developer effort, these tools are frequently incomplete and fail to ensure seamless compatibility.”

Manos Pavlidakis et al., “Cross-Vendor GPU Programming: Extending CUDA Beyond NVIDIA”, ACM, 2024.

A Quick Note – Inference vs. Training

For Training and Fine-Tuning (e.g., LoRA), CUDA is still the go-to choice. The ecosystem for training is built almost entirely on NVIDIA’s stack (PyTorch, TensorFlow, specialized libraries). While AMD has made strides with ROCm 6.x, getting consumer Radeon cards to work reliably for fine-tuning often involves significant OS-level tinkering, typically done on Linux-based systems.

For Inference (direct model use), CUDA’s lead is much less noticeable these days. Since LLM inference often uses aggressively quantized models (GGUF, GPTQ), the performance bottleneck often shifts from raw compute speed to memory bandwidth or simply the efficiency of the quantization runtime. This allows cross-platform solutions like llama.cpp’s Vulkan backend to deliver competitive speeds on many AMD/Intel graphics cards.

Different Backends/Runtimes Available

Here are some of the most popular backend/runtime options you have when it comes to local LLM inference and training. You will choose one of these depending on your hardware (your GPU model), and your software (the software support for one or more of the listed technologies).

The CUDA stack – cuBLAS/cuDNN & TensorRT-LLM

These are NVIDIA’s highly specialized, pre-optimized libraries. cuBLAS handles the core matrix math, while cuDNN accelerates deep learning primitives. TensorRT-LLM takes this further, compiling and optimizing models specifically for NVIDIA hardware, resulting in the highest attainable tokens-per-second throughput on their GPUs.

The AMD ROCm stack – HIP/hipBLAS, ROCm kernels & libraries

HIP is AMD’s development tool designed to be syntax-compatible with CUDA, allowing developers to target AMD GPUs with some minimal code changes. hipBLAS is the direct equivalent of cuBLAS. The open-source nature of the ROCm kernels allows for many ongoing community-driven optimizations, which differs from the way CUDA is developed because of its closed-source proprietary nature.

The Intel stack – oneAPI/SYCL & IPEX-LLM

Intel’s approach is to solve the cross-platform problem at the language level with SYCL, part of their larger oneAPI initiative. For LLMs, the IPEX-LLM library (Intel Extension for PyTorch) provides high-speed optimizations, focusing on the new Intel Arc dedicated cards and integrated graphics/NPUs. This is overall, a much younger solution than both ROCm, and CUDA.

Vulkan compute paths – a cross-vendor solution

Vulkan is a hardware-agnostic graphics and compute API. Projects like llama.cpp and KoboldCpp have successfully integrated Vulkan support, often replacing older OpenCL backends. Vulkan provides a true cross-vendor path for GPU-acceleration, meaning the same code can run on NVIDIA, AMD, and Intel, even if it requires slightly more performance overhead than a native CUDA path.

DirectML/ONNX Runtime on Windows – another cross-vendor solution

DirectML is yet another hardware-agnostic Windows GPU acceleration API that enables machine learning workloads on AMD and Intel graphics by translating ML operations into commands compatible with any Windows-supported GPU. Widely used through the ONNX Runtime, its role is to provide stable and broad GPU processing support on Windows without the need for vendor-specific libraries. While not as fast as NVIDIA’s or AMD’s specialized stacks (in most contexts), it’s a great option to have.

OpenVINO – CPU/iGPU/NPU focused

Another key Intel tool, OpenVINO, focuses on optimizing models for low-power inference, particularly on Intel CPUs, iGPUs, and the new NPUs (Neural Processing Units). While generally much slower than a dedicated high-end GPU, it is highly efficient for running models like 7B on lower-end hardware.

Local LLM Software Compatibility

Your choice of local LLM software will determine which hardware stacks you can actually utilize. The general local inference software landscape is broadly split into user-friendly “desktop-first” launchers and complex, performance-focused “library-first” engines.

By now, almost all major inference software projects have successfully built bridges to non-CUDA hardware, usually through the versatile Vulkan compute path. Here are some of the most popular options you have.

“Desktop-first” Inference Software

These applications let you easily get started with local LLM hosting, offering either a clear and simple user-friendly GUI, or CLI like in the case of Ollama. All of these will work both with NVIDIA cards via CUDA, with AMD cards via ROCm, and they have Vulkan support for other compatible hardware.

• LM Studio – This software, as of now, provides one of the best front-ends for NVIDIA & AMD users. It features native, selectable support for CUDA and ROCm on both Windows and Linux. This allows AMD users to choose the high-speed ROCm path, or fall back to the more universal Vulkan/OpenCL for broader compatibility, which may be slower but requires less setup.
• KoboldCpp – This one, frequently paired with the SillyTavern UI, is highly successful on all platforms because of its underlying llama.cpp engine. For acceleration, it offers native CUDA support for NVIDIA users and Vulkan for virtually any other GPU, be it AMD, Intel Arc, or integrated graphics. There is also an actively developed ROCm-compatible fork of the project available.
• GPT4All – This launcher focuses on hardware compatibility using the Vulkan backend offering accelerated inference for GGUF models across a huge array of devices, including AMD Radeon and Intel Arc/iGPUs.
• Ollama – While it’s primarily a command-line and server tool, Ollama prioritizes stability and native performance for all of the most popular model architectures. It features deep integration with CUDA and Metal, but also supports the ROCm stack for AMD cards.

You can learn much more about all of these (and more) here: Full List of Local LLM Inference Software

“Library/Engine-first” Inference Software

These are two main examples of the most popular LLM inference software libraries that require manual installation and environment setup but can deliver the fastest possible tokens-per-second with the right configuration, especially for larger or batch-processed models, or on multi-GPU setups. Most of the “desktop-first” user-friendly solutions listed above, rely either on base or forked versions of one of these as the main framework managing the inference process.

• vLLM – If you are dealing with advanced quantization formats like EXL2 or AWQ, or require specialized features like Tensor Parallelism for efficient multi-GPU use, vLLM is one of the best choices out there. vLLM offers the best support for both CUDA, ROCm, and Intel XPU devices, as well as Apple silicon.
• llama.cpp – This is the library that powers the GPU acceleration features in many tools like for instance the aforementioned KoboldCpp and GPT4All. Its commitment to portability via Vulkan and its advanced CPU+GPU offloading make it the foundational engine that allows non-NVIDIA cards to participate in the local LLM movement at all. It’s compatible with CUDA, ROCm, and Intel GPUs (via the SYCL backend).

Real-Life Performance

If you compare a top-tier NVIDIA card to an equivalent AMD card using their native frameworks (CUDA vs. ROCm) on specialized library inference, NVIDIA often maintains a performance lead, sometimes even by 10-30%, due to highly optimized kernels and Tensor Cores.

The real-world performance for the hobbyist, in the vast majority of the cases will depend on whether you’re using GGUF models via the Vulkan or DirectML backends, or AMD/Intel cards via their own compatible runtime environments supported by the software. In either case, depending on your workflow of course, it’s safe to assume that the performance will be just a little bit below what working with CUDA-enabled hardware would offer.

For many users, the slightly lower tokens/sec throughput on an AMD card is an acceptable tradeoff for the greater VRAM capacity per dollar. To estimate what throughput you can achieve on your hardware for different models and quantizations, you have to look at the broader picture, with not only the computational efficiency of your chosen software backend, and your GPU speed in mind, but also taking into account the amount of VRAM on your card, and your card’s memory bus bandwidth, as well as the actual models you want to run, and their particular requirements.

You can learn much more about that in my guide on LLM VRAM requirements, where I detail the video memory requirements for locally hosted large language models: LLMs & Their Size In VRAM Explained – Quantizations, Context, KV-Cache.

Which One To Choose – Some Examples of Practical Scenarios

Note that every situation is different in real life, but these simple theoretical setups/short roadmaps I prepared for you can help you at least start thinking about what hardware/software combination you need.

“I already own an NVIDIA card”
That’s the easiest path. Every piece of local LLM software should be supported, and you most likely will be able to access the fastest performance on both Windows and Linux systems, especially if you plan to explore model fine-tuning and have a card that can handle it.

“I have an AMD Radeon GPU (Linux/Windows)”
If you’re on Linux, set up ROCm and you will be highly competitive on speed for inference and even some basic fine-tuning. On Windows, your simplest and most stable path is to either focus on ROCm-compatible software like LM Studio, or on running GGUF models through a Vulkan-enabled runtime like llama.cpp or a DirectML-enabled tool.

“I’m on Intel Arc dedicated graphics”
For dedicated Intel Arc GPUs, set up an optimized framework like OpenVINO or IPEX-LLM if your software supports it, or fall back to the Vulkan backend in launchers like LM Studio. The performance will depend on the particular GPU from the Arc series you own.

“I need the simplest setup with least tinkering”
Choose NVIDIA on Windows. The hardware/software stack is the most mature and most likely to work right out of the box with any LLM software. You will spend less time playing around with drivers and software configuration, and more time chatting with your models.

“I care about running 70B+ locally”
Focus on maximizing VRAM capacity, which often means an AMD card for better VRAM-per-dollar, or a high-end 24GB+ NVIDIA card. Regardless of brand, your setup will most likely rely on aggressive quantization (GGUF) and sometimes on the CPU RAM offloading features available in llama.cpp runtimes.

“I want multi-GPU setup or mixed GPUs”
Your path is through the universal portability of GGUF models. You will need a runtime like llama.cpp that is designed for aggressive layer offloading, which can successfully distribute the model across various non-uniform GPUs and CPU RAM, regardless of the vendor. The most important thing here is the correct GPU choice, as well as a motherboard that can easily support the cards you choose.

Sources & Further Reading

• techtactician.com:
  • Beginner’s Guide To Local LLMs – How You Can Get Started Today
  • LLMs & Their Size In VRAM Explained – Quantizations, Context, KV-Cache
  • Quickest SillyTavern x KoboldCpp Starter Guide (Local AI Characters RP)
  • 6 Best GPUs for Dual & Multi-GPU Local LLM Setups
• External Sources:
  • Building Large Language Models (LLMs) from Scratch: The Role of CUDA and AVX
  • ROCm vs CUDA: A Performance Showdown for Modern AI Workloads
  • CUDA, ROCm, oneAPI? — Running Code on a GPU, Any GPU
  • oneAPI: A Viable Alternative To CUDA Lock-in
  • Performance of llama.cpp with Vulkan