CUDA vs ROCm vs Vulkan for Local Inference

Compatibility reviewed August 25, 2026. NVIDIA’s general CUDA documentation is currently on CUDA 13.3, while NVIDIA’s DGX Spark release notes list CUDA Toolkit 13.0.2 in the current DGX OS image. AMD’s fixed ROCm 7.14.0 compatibility matrix is dated July 16, 2026. These versions will change. Preserve the known-good combination for an operating service and recheck the linked primary documentation before any upgrade.

This page is a software/backend decision guide. It does not rank computers or repeat their complete specifications. Start with the local AI hardware architecture guide if memory capacity and system class are still unknown, use the Canadian buyer guide for the three D-Central purchasing paths, or read DGX Spark vs Strix Halo once the choice is specifically between those two 128 GB platforms.

Decision table: which backend should you validate first?

Starting requirement	First backend to validate	Why	Required evidence before approval
The application or vendor container explicitly requires NVIDIA CUDA	CUDA	It avoids an unsupported port and follows the application’s documented path	Supported NVIDIA GPU compute capability, driver/toolkit relationship, host architecture, container architecture, and application release
The framework publishes an AMD ROCm build for the exact target	ROCm/HIP	It uses AMD’s native compute stack and libraries	GPU or APU row, operating system and kernel, driver, ROCm version, Python/framework versions, and the runtime’s own support statement
The primary workload is GGUF through llama.cpp on an AMD or mixed-vendor fleet	Vulkan and HIP as separate candidates	llama.cpp maintains both backends, allowing a controlled comparison on the same model and build	Backend build succeeds, device is detected, required operations stay on the intended device, output is correct, memory remains stable, and the service test passes
The workload is GGUF through llama.cpp on NVIDIA	CUDA first; Vulkan as a separately tested alternative	llama.cpp has native CUDA and Vulkan backends, but they are distinct implementations	Pin the llama.cpp commit/build, model hash, launch flags, driver, and backend; never transfer a result from one backend to the other
The team needs vLLM, PyTorch extensions, or custom kernels	The backend named by the exact release documentation	Framework wheels, containers, kernels, and compiler targets are narrower than the platform brand	Official image or build matrix for the hardware, OS, Python, framework, and model architecture; representative API validation
No accelerator path is yet required	CPU baseline, then one GPU backend	A CPU run verifies model artefact and application logic before driver and offload variables are introduced	Same model, tokenizer, prompt set, context policy, and correctness criteria on both paths

A decision table cannot replace a pilot. It simply identifies the first supported path worth testing. “Supported” has several layers, and skipping any one of them creates a fragile deployment.

Five compatibility layers that must all agree

1. Hardware support: the accelerator or integrated GPU appears in the vendor’s supported-device information for the selected release.
2. Host support: the processor architecture, operating-system edition, kernel, compiler, and driver are qualified together. An x86-64 container is not automatically usable on an Arm64 host.
3. Backend support: the inference engine was built with CUDA, HIP/ROCm, or Vulkan enabled and discovers the intended device at runtime.
4. Model support: the engine implements the model architecture, tokenizer, quantization, and any vision, speech, mixture-of-experts, or custom-code components.
5. Service support: the complete application passes correctness, memory, context, concurrency, restart, authentication, and recovery tests.

A green hardware row establishes only the first layers. For example, AMD can list a Radeon device in ROCm while a specific inference server has not published a compatible wheel or container for that exact combination. Conversely, llama.cpp can expose a Vulkan device even though another application depends on CUDA-only kernels. Platform support, framework support, model support, and application support are separate claims.

CUDA: NVIDIA’s native compute path

NVIDIA describes CUDA as a parallel-computing platform made of hardware and software. Its CUDA platform documentation separates the NVIDIA driver, CUDA Toolkit, CUDA runtime, driver API, runtime API, and GPU compute capability. That separation matters operationally: seeing a CUDA version in a tool does not prove that every toolkit, library, application binary, and GPU target is aligned.

The current CUDA 13.3 Linux installation guide requires a CUDA-capable GPU, a qualified Linux distribution with compiler toolchain, and the CUDA Toolkit. It lists supported distributions and architectures for that release. The table is release-specific. A distribution being generally popular or an application running on another NVIDIA machine is not equivalent to qualification for this host and toolkit.

Driver compatibility also has boundaries. NVIDIA’s minor-version compatibility documentation says CUDA 11 and later can provide compatibility within a major release family when the driver meets the minimum, subject to limitations. It calls out newer-feature and PTX-related cases that can still require a newer driver. For deployment, capture the actual driver, toolkit or container runtime, GPU compute capability, compiled target, and application version rather than relying on the word CUDA alone.

CUDA is the natural first path when an application provides only CUDA kernels, an official NVIDIA container, or documented NVIDIA deployment instructions. It is also the native stack on DGX Spark at $9,449 CAD and on an NVIDIA-based custom GPU inference rig. That commercial fit still does not make every CUDA image Spark-compatible: DGX Spark is Arm64, so image architecture and dependencies must be checked.

For Spark specifically, use the system image as its own supported baseline. NVIDIA’s DGX Spark release notes list DGX OS 7.5.0, GPU driver 580.159.03, and CUDA Toolkit 13.0.2 as of this review. NVIDIA also says that the CUDA version shipped on Spark has been verified with that system hardware at the time of the software release. Follow the DGX Spark setup guide rather than replacing the appliance baseline merely because a newer general CUDA guide exists.

ROCm and HIP: AMD’s native compute path

ROCm is not one interchangeable package. AMD’s compatibility matrix describes a coordinated stack of firmware, driver, operating system, compiler/runtime components, libraries, and AI frameworks. The fixed ROCm 7.14.0 matrix lists device families, LLVM targets, supported operating systems and kernels, drivers, core components, and validated AI ecosystem versions. Use the selectors and exact row for the deployed hardware.

As of August 25, 2026, that matrix includes Ryzen AI Max 300-series APUs and Radeon 8060S graphics under the Ryzen APU information, with gfx1151 among the listed targets. It names qualified Ubuntu and Windows releases for that ROCm version. Those are dated compatibility facts, not a statement that every Radeon 8060S chassis, firmware build, Linux derivative, WSL path, Python wheel, or local-LLM application has been validated by AMD or D-Central.

HIP is the programming and runtime layer used by applications such as llama.cpp to target supported AMD GPUs through ROCm. The primary llama.cpp HIP build documentation requires ROCm and shows `GGML_HIP` builds, including optional GPU targets. A successful compiler invocation is only the start. The resulting binary must discover the expected device and execute the actual model without unsupported operations, memory failures, or silent CPU fallback.

Shared-memory AMD systems add another configuration boundary. AMD’s Strix Halo optimization guide explains GPU virtual memory, GTT mappings, and Linux memory limits for large GPU allocations. A machine containing 128 GB does not mean an application can map nearly all of it without the required kernel and memory settings. See the Strix Halo ROCm LLM guide for the platform workflow.

ROCm is the correct first candidate when the exact AMD hardware and application path is documented. It should not be selected merely because the processor is AMD. The D-Central Strix Halo 128 GB plan at $7,349 CAD is an x86 AMD platform, but the runtime still determines whether ROCm/HIP, Vulkan, CPU, or another supported path is appropriate.

Vulkan: a cross-vendor API and llama.cpp backend

The Khronos Group’s official Vulkan guide defines Vulkan as a cross-platform, cross-vendor graphics and compute API. For local LLM work, the relevant point is not graphics: an inference project can implement compute kernels through Vulkan and reach hardware through the installed Vulkan driver. Vulkan is therefore an application backend, not a replacement label for the CUDA Toolkit or the entire ROCm library collection.

The maintained llama.cpp repository lists CUDA for NVIDIA GPUs, HIP for AMD GPUs, and Vulkan for GPUs among its supported backends. Its Vulkan build guide documents Vulkan dependencies, `vulkaninfo`, the `GGML_VULKAN` build option, and device discovery. It also notes that multiple backends can be built and that available devices can be listed at runtime.

This makes Vulkan especially useful for a controlled llama.cpp/GGUF deployment where portability matters or the HIP path for a particular build is not ready. It does not prove that Ollama, vLLM, a Python extension, or a model with custom kernels can use Vulkan. It also does not prove a performance ranking. CUDA, HIP, and Vulkan use different kernels and integration paths; compare them only with the same source revision, model artefact, settings, prompt set, power mode, and measurement procedure.

Runtime support is narrower than backend support

An inference engine can support a backend while a packaged release supports only a subset of devices. Ollama bundles and selects components differently from a local llama.cpp build. vLLM has its own framework, Python, compiler, container, model, and accelerator requirements. A desktop UI may package one backend on Windows and another on Linux. Read the runtime’s documentation and release artefacts after checking the vendor platform matrix.

Model support is another independent dimension. A runtime may execute an ordinary GGUF text model while lacking a new tokenizer, multimodal projector, quantization type, speculative-decoding component, or custom operator needed by another model. If a model card asks users to trust remote code, compile an extension, or install a specific framework branch, add those dependencies to the compatibility review.

Use Ollama vs vLLM vs llama.cpp to choose the serving layer. The Ollama quick-start and first-boot Ollama and Open WebUI guide are evaluation paths, not proof of a production service. For NVIDIA’s compact platform, see Ollama on DGX Spark.

Backend validation checklist

1. Freeze identifiers. Record the GPU/APU model, device identifier, processor architecture, firmware, operating system build, kernel, and power profile.
2. Freeze the software stack. Record driver, CUDA or ROCm release, Vulkan driver, compiler, Python, framework, container digest, inference-engine commit or package, and launch configuration.
3. Verify official compatibility. Save the dated vendor matrix or guide covering the exact combination. Confirm that a general framework row is not being mistaken for support on every device.
4. Verify backend discovery. Use the vendor diagnostic and the runtime’s device listing. Confirm logs name the intended backend and device; do not infer GPU use from system responsiveness.
5. Verify the model. Hash the model files. Test tokenizer, prompt template, quantization, context, structured output, tools, embeddings, reranking, and multimodal components that the service will use.
6. Check placement and fallback. Confirm which layers or operations execute on the accelerator. Treat unexpected CPU fallback as a deployment finding, not a harmless detail.
7. Exercise memory limits. Use the maximum permitted prompt, response, batch, concurrent requests, retrieval workload, and any side models. Monitor both accelerator-visible and host memory.
8. Test correctness before speed. Compare deterministic prompts and application outputs. Then measure the service-level criteria using a documented method; do not import numbers from unrelated hardware or reviews.
9. Test operations. Restart the host and service, rotate credentials, restore a backup, interrupt a request, fill a queue, lose network access, and roll back an update.
10. Preserve the result. Store the bill of materials, configuration, hashes, test set, logs, acceptance criteria, and known limitations with an accountable owner.

Run this checklist again when any material layer changes. Updating only the model can introduce unsupported operators; updating only the driver can change backend behaviour; updating only a container can change the embedded runtime and libraries. “No hardware changed” is not a reason to skip validation.

Operational rules for a maintainable backend choice

Prefer a supported baseline over a constantly moving host. Keep development experimentation separate from the accepted service. Pin artefacts, stage changes, define a rollback window, and retain the previous working image. A newer CUDA Toolkit does not automatically belong on a DGX Spark image; a newer ROCm release does not automatically qualify an older kernel; a new llama.cpp commit can add model support while also changing backend behaviour.

Decide who owns each boundary. The hardware owner handles firmware, thermals, and physical access. The system owner handles drivers, kernel, accounts, patches, and recovery. The application owner handles runtime builds, model licences, model hashes, API behaviour, and user acceptance. The data owner approves documents, retention, and access. A local backend improves control over data flows, but it does not assign these responsibilities.

Do not expose the inference port broadly just because the backend works. Add authentication, network policy, secret management, input limits, logs appropriate to the data classification, and tested backups. Keep a CPU-safe diagnostic path where practical, so model or application errors can be separated from accelerator errors. For memory architecture decisions, read VRAM vs unified memory.

Frequently asked questions

Is CUDA always faster than ROCm or Vulkan for local LLMs?

No universal answer is defensible. Results depend on hardware, model, quantization, prompt and response lengths, batch, concurrency, kernels, runtime revision, drivers, power mode, and measurement method. Choose supported candidates first, verify correctness and stability, then benchmark the exact service. D-Central does not publish an invented backend multiplier.

Can an AMD GPU run CUDA?

CUDA targets NVIDIA GPUs. AMD’s native compute path is ROCm/HIP, while applications such as llama.cpp can also target AMD hardware through Vulkan. A source-porting tool or similarly named API does not turn an AMD GPU into a CUDA-supported device. Follow the runtime’s documented AMD backend.

Does ROCm listing my GPU mean vLLM will work?

Not by itself. The GPU row establishes a platform compatibility layer for a specific ROCm release. You must also verify operating system, kernel, driver, Python, PyTorch, vLLM release or image, model architecture, and application path. AMD’s ecosystem table and device table must be interpreted together with vLLM’s own documentation.

Is Vulkan only a fallback for llama.cpp?

No. It can be an intentional cross-vendor backend for a llama.cpp/GGUF deployment. Whether it is primary or fallback depends on tested correctness, operations, and service requirements. Its presence in llama.cpp does not give unrelated runtimes Vulkan support.

Why can a CUDA container fail on DGX Spark?

DGX Spark uses Arm64. A container or one of its wheels and binaries may have been built only for x86-64, even if its GPU code uses CUDA. The container must match host architecture, driver/toolkit constraints, GPU target, libraries, and application requirements. Start with NVIDIA’s Spark playbooks or confirmed multi-architecture images.

Should I install the newest CUDA or ROCm release immediately?

Not on an accepted service without staging. Review vendor release and compatibility documentation, reproduce the environment, test the exact models and application, and preserve rollback. Appliance baselines such as DGX OS may intentionally ship a toolkit version different from the newest general developer documentation.

How do I choose between CUDA and ROCm before buying hardware?

Inventory the required applications, model formats, extensions, containers, operating systems, processor architecture, and support expectations. If a non-portable dependency requires CUDA, that narrows the hardware path to NVIDIA. If the required stack publishes an exact AMD path, ROCm can qualify. If the workload is llama.cpp/GGUF, test its CUDA, HIP, or Vulkan candidate on representative hardware. Then use the Canadian local AI computer guide for the purchase decision.

Merchant routing after software validation: choose the NVIDIA DGX Spark at $9,449 CAD when its Arm64 CUDA/DGX environment is the supported fit; choose the AMD Strix Halo 128 GB plan at $7,349 CAD when its x86 ROCm/Vulkan path is validated; or request a custom GPU inference rig when a named discrete GPU and serviceable tower match the accepted stack. Hardware selection follows the compatibility record, not the other way around.