CUDA, Vulkan, ROCm, and OpenVINO: The Local AI Backend Guide

When running local language models with engines like llama.cpp, vLLM, or Ollama, your choice of compute backend dictates stability, hardware compatibility, and raw generation throughput.

Two years ago, NVIDIA’s proprietary CUDA framework was effectively mandatory. In 2026, the ecosystem has diversified dramatically across cross-platform Vulkan, AMD ROCm, and Intel OpenVINO. However, choosing the wrong backend can slash your token generation rate in half or produce garbage output.

This guide provides the architectural breakdown of each backend and the empirical recommendations from our benchmark lab.

1. NVIDIA CUDA: The mature ecosystem baseline

CUDA remains the industry benchmark against which all other compute runtimes are measured.

• How it works: Directly addresses NVIDIA Streaming Multiprocessors (SMs) and specialized Tensor Cores via compiled PTX assembly and optimized cuBLAS / cuBLASLt libraries.
• Strengths: Maximum memory bandwidth saturation. Flawless support for cutting-edge attention kernels (FlashAttention-2, Flash-Decoding, PagedAttention). Native support in every major serving framework (vLLM, SGLang, TensorRT-LLM, llama.cpp).
• Limitations: Proprietary lock-in. Only runs on NVIDIA hardware.
• When to use: Always, if you own an NVIDIA graphics card.

2. Vulkan: The universal open cross-vendor path

Vulkan is a low-overhead, cross-platform graphics and compute API maintained by the Khronos Group. In llama.cpp, the ggml-vulkan backend compiles matrix multiplication into SPIR-V compute shaders.

• How it works: Executes across any GPU with modern compute shaders—including AMD Radeon, Intel Arc, Apple Silicon (via MoltenVK), Qualcomm Adreno, and NVIDIA.
• Strengths: Exceptional portability. Zero proprietary SDK or gigabyte-sized toolkit installations required. On Linux, open-source Mesa drivers (RADV for AMD, ANV for Intel) provide extraordinary stability and fast shader compilation.
• Performance reality: While historically 15–20% behind native CUDA on identical silicon, modern Mesa Vulkan drivers achieve 85% to 92% of theoretical memory bandwidth saturation.
• When to use:
  • AMD Radeon users on Windows (where ROCm is difficult or unsupported).
  • Intel Arc users seeking rock-solid out-of-the-box compatibility in llama.cpp.
  • Heterogeneous multi-GPU systems mixing different vendor graphics cards.

3. AMD ROCm & HIP: High raw compute with platform friction

ROCm (Radeon Open Compute) is AMD’s open-source compute stack, designed as a direct architectural counterpart to CUDA via the HIP (Heterogeneous-computing Interface for Portability) translation layer.

• How it works: Compiles HIP kernels into native AMD RDNA machine code using hipBLAS and rocBLAS.
• Strengths: When properly tuned on supported hardware, ROCm delivers near-CUDA performance and enables full integration with Python-centric frameworks like PyTorch and vLLM. In our lab benchmarks (bm-007), an RX 7900 XT achieves a blazing 269.01 tokens/sec on 8B models.
• Limitations: Driver installation friction. Historically Linux-only for consumer cards. Hardware support across consumer RDNA architectures can be inconsistent, occasionally requiring environmental overrides (e.g., HSA_OVERRIDE_GFX_VERSION).
• When to use: Dedicated Linux homelabs and production workstations running high-end AMD RDNA3/RDNA4 GPUs (RX 7900 XT, 7900 XTX) where maximum throughput in vLLM is required.

4. Intel OpenVINO: The Battlemage breakthrough

Intel OpenVINO (Open Visual Inference and Neural Network Optimization) is Intel’s specialized toolkit for maximizing inference performance across Intel CPUs, integrated graphics, and discrete Arc GPUs.

• How it works: OpenVINO uses Intel’s Level Zero driver interface to execute deep learning graphs directly on Intel Xe Matrix Extensions (XMX) cores.
• What our lab measured (bm-012): On our Intel Arc B60 Pro 24GB testbed running 30B models, OpenVINO Model Server (OVMS) delivered a stunning ~1.76× speedup over Vulkan on identical silicon. OpenVINO fully saturated the card’s memory subsystem, achieving significantly higher compute efficiency than generic SPIR-V shaders.
• When to use: Any system running discrete Intel Arc GPUs (A770, B580, B60 Pro) or Intel Core Ultra processors when running dedicated inference containers.

5. The SYCL cautionary tale

SYCL is an open, multi-target C++ programming model that Intel uses extensively for oneAPI. However, in consumer local AI, SYCL in llama.cpp has a troubled history:

• In our earlier lab tests, ad-hoc community SYCL builds produced corrupted outputs and collapsed to an unoptimized fallback path, crawling at 5.97 to 8.41 tokens/sec on models that should run at 38 tok/s (documented upstream in llama.cpp issue #22413).
• The rule: Do not attempt experimental ad-hoc SYCL builds for discrete Arc cards. If you want maximum performance on Intel Arc, deploy OpenVINO Model Server. If you want instant zero-configuration simplicity, use Vulkan via Mesa ANV.

Backend Decision Matrix by Hardware and OS

For detailed benchmark comparisons across these backends, see CUDA, ROCm, Vulkan, and OpenVINO Compared and OpenVINO beats Vulkan on Arc B60.