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 and alternatives (ROCm, Vulkan, DirectML/ONNX Runtime, oneAPI/SYCL, OpenVINO, and Metal/MLX), and shows you how the choice can impact your performance, software selection, and overall workflow.

Updated – June 2026: This guide has undergone a major update/re-write to reflect the current CUDA 13.x / ROCm 7.x landscape, expanded AMD Radeon and Ryzen AI support, Ollama‘s newer AMD/Vulkan paths on Windows and Linux, vLLM’s current hardware support, and the fact that Intel’s IPEX-LLM project is now archived.

TL;DR for the impatient: For local LLMs, CUDA still delivers the smoothest setup, the widest software support, and the highest single-GPU performance in many Windows/Linux workflows. But you don’t really need CUDA to run great local models. ROCm (AMD), Vulkan (cross-vendor), DirectML/ONNX (Windows/cross-vendor), oneAPI/SYCL and OpenVINO (Intel), and Metal/MLX (Apple Silicon) 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 2026 (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 still often attractive for local LLM users because of VRAM-per-dollar, especially on Radeon RX 7000/9000-series cards and higher-VRAM Radeon PRO models. The bigger recent change is software support. ROCm 7.x has expanded official Radeon and Ryzen AI card support, and AMD now documents ROCm usage for Radeon GPUs and Ryzen APUs on both Linux and Windows. Linux remains the more complete and predictable ROCm environment, but Windows is no longer just an experimental afterthought for many RDNA2/RDNA3/RDNA4 cards.

Intel (oneAPI/SYCL, OpenVINO) Arc GPUs and Core Ultra systems are still interesting budget/local-AI options, but the recommended wording has changed. IPEX-LLM should now be treated as a legacy/archived Intel path, not the main recommendation. For current Intel hardware, the more future-facing stack is OpenVINO GenAI, llama.cpp via SYCL/Vulkan/OpenVINO backends, and software that exposes these runtimes cleanly. Intel’s challenge is still software maturity and app-level support.

What “CUDA”, “ROCm”, “oneAPI/SYCL”, “Vulkan”, “DirectML”, “OpenVINO”, and “Metal/MLX” 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 parallel computing platform and API ecosystem. It gives developers mature tools, libraries, compilers, and optimized kernels for GPU-accelerated AI workloads, but it also ties CUDA-specific software closely to NVIDIA hardware.

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.

OpenVINO is Intel’s inference optimization toolkit for CPUs, integrated GPUs, discrete Intel GPUs, and NPUs. For local LLM users, it matters most on Intel Core Ultra, Arc, and iGPU systems where the goal is efficient inference rather than maximum raw GPU throughput.

Finally, Metal is Apple’s low-level graphics and compute API, while MLX is Apple’s machine learning framework optimized for Apple Silicon. For local LLM users on M-series Macs, Metal-backed llama.cpp runtimes and MLX-based models are usually the most relevant non-CUDA paths, helped by Apple’s unified memory architecture.

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 major progress with ROCm 7.x, training and fine-tuning on consumer Radeon cards still usually require more attention to GPU support tables, driver versions, framework compatibility, and operating system choice than an equivalent NVIDIA/CUDA setup. Linux remains the safer ROCm choice for serious model training and fine-tuning, while Windows support is improving but more hardware- and workflow-dependent.

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 – OpenVINO GenAI, oneAPI/SYCL & Vulkan

Intel’s current local-AI story is better framed around OpenVINO GenAI, oneAPI/SYCL, and Vulkan-compatible runtimes rather than IPEX-LLM. OpenVINO targets optimized inference on Intel CPUs, integrated GPUs, discrete Arc GPUs, and NPUs, while llama.cpp’s SYCL backend is primarily designed for Intel GPUs and can also benefit from SYCL’s cross-platform capabilities. IPEX-LLM can still appear in older tutorials and integrations, but because the official Intel repository is now archived, it should be treated as a legacy route.

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 optimized inference across Intel CPUs, GPUs, and NPUs, and it now has a dedicated GenAI workflow for text-generation and LLM-style use cases. It still will not turn a laptop NPU into a replacement for a high-end discrete GPU, but it is becoming a much more relevant path for efficient local inference on Core Ultra, Arc, and Intel iGPU systems.

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 GUI or a CLI/server workflow like Ollama. The important nuance is that backend support is no longer identical across apps. Some of these expose CUDA, ROCm, Vulkan, Metal, MLX, or Kompute directly, while others hide the backend choice behind their own runtime.

In general, NVIDIA users still get the cleanest CUDA path, AMD users now have both ROCm and Vulkan options in more tools, Apple Silicon users usually rely on Metal/MLX, and Intel users should look for Vulkan, SYCL, or OpenVINO paths.

• 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 a wide range of AMD, Intel, and integrated GPUs. For AMD users, the current practical advice is usually to try the Vulkan/no-CUDA build first for the broadest compatibility, then use ROCm/HIP builds where your Linux setup and GPU are supported.
• 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 popular model architectures. It features strong CUDA and Metal support, supports ROCm for many AMD cards, and now also documents Vulkan as an additional Windows/Linux GPU path. On Windows, Ollama runs as a native application with NVIDIA and AMD Radeon GPU support, but the best backend for AMD still depends on the exact GPU and driver stack.

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 production-style serving, high throughput, batching, or advanced quantization formats like AWQ, GPTQ, FP8, GGUF, compressed-tensors, and related formats, or require specialized features like Tensor Parallelism for efficient multi-GPU use, vLLM is one of the best choices out there. vLLM has first-class documentation for NVIDIA CUDA, AMD ROCm, Intel XPU, as well as Apple Silicon via the community-maintained vLLM-Metal path, but it remains mostly a Linux/server-oriented tool rather than a beginner desktop launcher.
• 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 hobbyist local inference depends heavily on the exact model format, quantization, backend, GPU memory bandwidth, driver, and runtime. GGUF through Vulkan can be excellent for ease of use and compatibility, ROCm/HIP can be faster on supported AMD setups, CUDA is still the most consistently optimized NVIDIA path, and DirectML/ONNX Runtime remains useful for broad Windows compatibility. The safest assumption is not that every non-CUDA path is “only a little slower”, but that you should benchmark your exact model and runtime before buying hardware around a specific backend.

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 and your GPU is officially supported, ROCm is the path to test first for PyTorch, native AMD acceleration, and more advanced workloads. For easy local inference, Vulkan-backed GGUF runtimes are still often the least painful option. On Windows, the situation is much better than it used to be. Ollama, LM Studio, llama.cpp-based tools, and ROCm/HIP-capable drivers now give many Radeon owners usable GPU acceleration, but your exact RX 6000/7000/9000 model and driver stack still matter. When in doubt, start with Vulkan for simplicity, then test ROCm where your tool and GPU support it.

“I’m on Intel Arc dedicated graphics”
For dedicated Intel Arc GPUs, prioritize OpenVINO, llama.cpp SYCL, or Vulkan-backed software. For most casual users, Vulkan through a friendly launcher is the easiest starting point. For Intel-specific optimization, OpenVINO and SYCL are the paths to take.

“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”
For mixed-vendor or mismatched cards, GGUF models with llama.cpp-style layer offloading remain the most flexible route. For matched multi-GPU setups where throughput matters, also consider vLLM or other serving-oriented runtimes with tensor parallelism. LM Studio has also added more multi-GPU controls and CUDA tensor-parallel loading, but mixed GPU builds still require careful expectations. More GPUs can help you fit larger models in certain contexts, but they do not always scale token generation speed linearly. 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:
  • NVIDIA CUDA Toolkit Release Notes
  • AMD ROCm Release History
  • 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