For most general-purpose local LLM and AI inference work, NVIDIA RTX is the safest default. CUDA, TensorRT, TensorRT-LLM and consumer-focused TensorRT-RTX give it the broadest documented path from a model to a working application. AMD Radeon 9000-series cards are credible choices when your software is supported by ROCm, particularly on Linux or when a card’s memory configuration is attractive. Intel Arc can work well for applications built around oneAPI, oneDNN, OpenVINO or Intel GPU execution providers, but compatibility is more application-specific.
There is no universal winner. The right accelerator is the one that fits your model and context in VRAM, runs on your operating system, supports your chosen backend and stays within your power, cooling and troubleshooting budget.
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What the three platforms actually provide
Local inference means running a trained model on your own workstation rather than sending prompts to a hosted API. The graphics card does the matrix and tensor operations; the software stack determines whether those operations are available to your application and whether they are fast enough to be useful.
That stack is more important than a peak-compute number on a product page. A card can have ample theoretical throughput yet fail to run a required kernel, lack a compatible binary, or force a model onto the CPU. Conversely, a slightly slower card with a mature execution provider can deliver a better real-world experience.
#1 Best Overall
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| Platform | Primary software path | Where it is strongest | Main qualification |
|---|---|---|---|
| NVIDIA RTX | CUDA, TensorRT, TensorRT-LLM, TensorRT-RTX; broad support in common frameworks and runtimes | General-purpose consumer deployment, LLM serving, image generation and applications that publish CUDA instructions | Confirm matching CUDA, driver, framework and GPU-generation requirements |
| AMD Radeon | ROCm, PyTorch, TensorFlow, JAX, ONNX; integrations including Quark, AITER, vLLM and SGLang | Linux workstations and supported open tooling; buyers prioritising Radeon memory options | The cited Radeon matrix is Linux-focused and support varies by GPU, kernel and application |
| Intel Arc | oneAPI, oneDNN, OpenVINO, SYCL and Intel execution providers | Software already designed for Intel GPUs or OpenVINO/oneDNN workflows | Model, operating-system and backend support must be checked before purchase |
These are not interchangeable drop-in environments. A CUDA tutorial may require code changes or a different runtime on ROCm, and an Intel-optimised OpenVINO workflow does not imply that every CUDA-oriented package will run on Arc.
NVIDIA: the lowest-friction general-purpose choice
Why CUDA and TensorRT matter
NVIDIA’s software advantage is the breadth of documented deployment options. TensorRT converts TensorFlow, PyTorch, ONNX and other models into optimised runtime engines for low-latency, high-throughput inference across datacenter, edge, embedded and consumer hardware. TensorRT-LLM adds LLM-specific features such as in-flight batching, paged key-value caching, multi-GPU and multi-node support, plus FP8, INT8 and INT4 quantisation.
For a desktop RTX card, TensorRT-RTX is the consumer-oriented path. NVIDIA describes it as an optimised inference runtime for RTX GPUs. Its stated runtime footprint is under 200 MB, and NVIDIA says engine builds typically complete in 15–30 seconds. Those figures are vendor specifications, not a guarantee for every model or build system; the documentation also requires compatible CUDA and driver versions.
Model coverage and hardware generations
The documented TensorRT-RTX path spans RTX generations from Turing through Blackwell, but an application can still impose a narrower requirement. Check the project’s supported compute capability, CUDA version and driver range before installing.
NVIDIA architecture material identifies the GeForce RTX 5090 and RTX 4090 as capable of supporting a cited FLUX.dev example in FP16 when that model requires more than 23 GB of VRAM. This is an architecture claim for that workload, not a universal statement about LLM speed or every image model. NVIDIA also describes FP4 support in RTX 50-series Tensor Cores; whether a particular runtime and model can use FP4 is a separate software-support question.
Rank #2
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Who should choose NVIDIA
- Choose RTX when a project’s installation guide says CUDA, TensorRT or TensorRT-LLM.
- Choose it when you want the fewest compatibility surprises across PyTorch, ONNX Runtime, image-generation tools and commercial desktop applications.
- Choose it when you may later serve several models or use features such as paged KV caching, in-flight batching or multi-GPU execution.
NVIDIA is not automatically the best value. A cheaper card with insufficient VRAM will fail to load a model, and a card that fits only after aggressive quantisation may be less useful than a slower, higher-memory alternative.
AMD: a serious ROCm option when the workload matches
What ROCm supports
AMD’s ROCm 7.2 Radeon/Ryzen documentation states that the release supports the latest Radeon 9000 Series (RDNA 4) and selected 7000 Series (RDNA 3) GPUs for local development and inference. The documented Linux Radeon configurations list PyTorch, TensorFlow, JAX and ONNX support.
AMD’s current ROCm material also highlights Quark, AITER, vLLM and SGLang integrations and covers Radeon, Ryzen and Instinct platforms. That gives AMD a credible modern stack rather than a purely experimental alternative. The important word is supported: a package may support ROCm in general while excluding your exact Radeon model, operating-system release or kernel.
When AMD makes sense
- You run Linux and are prepared to follow the exact ROCm, kernel, driver and framework matrix.
- Your target application publishes ROCm instructions or has verified support for your Radeon generation.
- You value a card’s available VRAM or open development tooling more than the widest possible turnkey compatibility.
- You are willing to test the specific model, quantisation and backend instead of assuming CUDA parity.
AMD’s advantage cannot be reduced to a single vendor benchmark. AMD has published MI355X-versus-NVIDIA B200 inference measurements from AMD Performance Labs, dated May 25, 2025, with particular hardware, software versions and model settings. Those results are useful evidence for those configurations, but they are not an independent ranking of Radeon versus GeForce cards and should not be transferred to a desktop purchase without a matching test.
Common AMD failure modes
A ROCm installation can appear healthy while a framework silently falls back to the CPU, because the installed wheel, GPU architecture or kernel is not supported. Verify that the framework reports the GPU, run a small inference, and monitor utilisation rather than assuming that a successful import means acceleration is active. If a project supplies a ROCm-specific wheel or container, use that rather than substituting a generic CUDA package.
Rank #3
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Intel Arc: effective inside an Intel-first software path
oneAPI, oneDNN and OpenVINO
Intel’s oneDNN system requirements list Arc A-Series and Arc B-Series graphics, Iris families, Data Center GPU Max, Data Center GPU Flex and newer Intel UHD graphics among supported accelerator families. Intel’s Xe guidance describes XMX matrix engines in Arc GPUs and explains that oneDNN and OpenVINO can accelerate inference workflows in TensorFlow and PyTorch.
Intel’s strongest case is therefore application-led. If the program offers an OpenVINO backend, a oneDNN build, SYCL support or an Intel execution provider, Arc can be an efficient local accelerator. If the program only documents CUDA kernels, assume that a compatibility project or conversion step is required until you have verified otherwise.
Questions to answer before buying Arc
- Does the exact application list Arc A-Series or B-Series, rather than only “Intel GPU”?
- Which operating systems and driver versions are supported?
- Is the required model architecture implemented by OpenVINO, oneDNN or the application’s Intel backend?
- Are quantised formats, custom operators and long context windows supported?
- Does the application expose GPU execution, or will unsupported layers run on the CPU?
Arc can be the right choice for an Intel-optimised workflow, but it is a risky blind purchase for a CUDA-first LLM toolchain.
VRAM decides which models you can run
Before comparing brands, calculate whether the model and its context window fit in the card’s memory. The weights consume the largest block; the runtime also needs space for activations, temporary workspaces and the key-value cache used by autoregressive language models. Longer context and larger batches increase that working set.
Precision changes the requirement substantially. FP16 stores more information per weight than INT8 or INT4, so quantisation can make a model that does not fit at full precision usable on a smaller card. It can also alter quality, operator support and speed. A quantised model is not automatically faster if the backend has to dequantise unsupported layers or move data between GPU and CPU.
Rank #4
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A practical sizing method
- Identify the exact parameter count and weight format of the model file you intend to use.
- Estimate weight memory from the format, then reserve additional capacity for runtime workspaces and the context KV cache.
- Add headroom for the context length and batch size you actually plan to run; do not size only for a short demonstration prompt.
- Check whether the backend supports the model’s operators at that precision on the chosen GPU.
- Leave enough capacity for the operating system and other GPU applications if the workstation will display a desktop or run more than one model.
The cited NVIDIA FLUX.dev example illustrates why this matters: NVIDIA says more than 23 GB of VRAM is required for that model at FP16, making cards such as the RTX 4090 and RTX 5090 relevant to that particular configuration. It does not establish a universal VRAM requirement for LLMs.
Operating-system and backend compatibility
| Use case | NVIDIA RTX | AMD Radeon | Intel Arc |
|---|---|---|---|
| CUDA-native desktop application | Documented, usually the most direct path | Not a drop-in replacement; verify a ROCm port | Not a drop-in replacement; verify an Intel backend |
| PyTorch | CUDA builds and TensorRT integrations are broadly documented | Supported on the cited Linux ROCm configurations | Use oneDNN, OpenVINO or another documented Intel path |
| ONNX Runtime | CUDA and TensorRT execution providers are common | Check the ROCm provider and exact GPU support | Check OpenVINO or Intel GPU execution-provider support |
| vLLM or SGLang | Commonly targeted by CUDA deployments | AMD highlights integrations, but verify your Radeon and release | Do not assume support without an Intel-specific implementation |
| Windows | NVIDIA documents consumer Windows and Linux paths | The cited Radeon ROCm matrix is Linux-focused | Support depends on the oneAPI/OpenVINO component and GPU generation |
| Linux | Broad support, with driver and CUDA matching required | Primary environment for the cited Radeon ROCm support | Supported where the selected oneAPI and OpenVINO components list the GPU |
“Supports PyTorch” is not precise enough for a purchase decision. Record the operating system, framework version, execution provider, GPU model, model architecture, quantisation and context length that you intend to use.
A repeatable buying and setup procedure
- Name the workload. Write down the exact model family, parameter count, quantisation, context length, batch size and application. “Local AI” is too broad to map to hardware.
- Read the application’s hardware matrix. Prefer an explicit GPU model and backend over a generic claim such as “CUDA-compatible” or “GPU acceleration.”
- Choose the operating system. Linux generally gives the clearest path for the cited ROCm Radeon configurations; NVIDIA has documented consumer Windows and Linux routes; Intel support follows the selected oneAPI/OpenVINO component.
- Check memory before compute. Reject any card that cannot hold the model, cache and runtime headroom at your chosen precision.
- Install the matching driver and runtime. For NVIDIA, align the driver and CUDA versions required by the TensorRT or framework documentation. For AMD, align ROCm, kernel and framework versions. For Intel, install the oneAPI/OpenVINO components listed for the GPU.
- Run a minimal GPU test. Load a small model, confirm that the intended device is selected, generate tokens or an inference result, and watch GPU utilisation and memory.
- Measure your real workload. Record prompt processing time, generation speed, peak VRAM, power behaviour and failures at the context length you care about.
- Only then tune. Change quantisation, batch size, context length or engine-building options one at a time so you know which change produced an improvement or a regression.
How to compare performance without being misled
Inference speed is conditional. A result measured with one model, batch size, precision, context length, software release and power limit cannot rank every card. Throughput tests with large batches may favour a different architecture than single-user interactive generation, where time to first token and memory latency matter more.
- Match the model architecture and exact file format.
- Match precision and quantisation, including whether operators remain on the GPU.
- Use the same prompt length, generated-token count, context window and batch size.
- Record framework, runtime, driver and operating-system versions.
- Report power limits, cooling and whether the display is connected to the test card.
- Separate prompt processing from generation speed and report peak VRAM.
Vendor-run measurements, including AMD Performance Labs results, should be labelled as configuration-specific vendor evidence. They are not substitutes for a controlled cross-vendor test using your model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Power, cooling and total ownership cost
The purchase price is only one part of local inference cost. A high-power card may require a larger power supply, additional case airflow and a quieter cooling solution. Multi-GPU systems add motherboard spacing, power-delivery and software complexity. A lower-cost card that requires repeated driver workarounds can also cost more in time than a supported alternative.
Best Value
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Include these checks in the build plan:
- Power-supply capacity and the card’s connectors.
- Case clearance, slot thickness and airflow around the GPU.
- Linux kernel and driver maintenance if using ROCm or oneAPI.
- Storage for model files, engines and multiple quantised variants.
- Whether the card must also drive a monitor while serving a model.
- Replacement or rollback procedures when a runtime update breaks a previously working stack.
Decision guide by buyer type
General-purpose enthusiast
Buy an NVIDIA RTX card with enough VRAM for the models you actually plan to run. The CUDA and TensorRT ecosystem offers the broadest documented consumer path and the least need to redesign an application around a vendor-specific backend.
Linux user with a ROCm-supported workload
AMD Radeon 9000-series, or a selected 7000-series model listed by ROCm 7.2, is reasonable when the exact application and framework are validated. Prioritise memory capacity and confirmed software support over a theoretical benchmark advantage.
OpenVINO or Intel software developer
Intel Arc is compelling when your application already uses oneAPI, oneDNN, OpenVINO or SYCL. Validate model operators and quantisation in that backend before committing to a larger deployment.
CUDA-first application with no alternative backend
Use NVIDIA unless the application maintainers explicitly document a working ROCm or Intel path. Porting assumptions are not a compatibility plan.
Large model that barely fits
Choose the platform and card with enough VRAM headroom at the precision and context length you need. A nominally faster accelerator that repeatedly spills to system memory will usually provide a worse interactive experience than a slower card that keeps the workload resident.
Troubleshooting checklist
The application sees only the CPU
- Confirm the GPU driver is installed and the operating system detects the card.
- Verify that the installed framework build targets your vendor backend, not a CPU-only package.
- Check that your GPU generation appears in the runtime’s supported list.
- Inspect logs for unsupported operators that trigger fallback.
The model runs out of memory
- Reduce context length or batch size.
- Use a supported lower-precision or quantised model.
- Close other GPU applications and reserve runtime workspace.
- Do not assume multi-GPU pooling is available; the application must explicitly support it.
Performance is much lower than expected
- Check whether layers or the KV cache have moved to the CPU.
- Confirm that the selected precision is accelerated by the backend.
- Compare prompt processing and token generation separately.
- Check power, temperature and clock limits before changing software.
An update broke the installation
Record working driver, runtime and framework versions before upgrading. If the new combination fails, roll back the component named in the project’s compatibility matrix rather than changing several packages at once.
Bottom line
NVIDIA RTX is the safest general-purpose recommendation because CUDA, TensorRT and TensorRT-LLM cover the widest range of documented consumer inference deployments. AMD Radeon is a serious alternative when ROCm supports the exact Linux workload and its memory configuration fits your models. Intel Arc is best treated as a targeted choice for oneAPI, oneDNN, OpenVINO or Intel-specific applications.
Make the final decision from a complete specification: GPU model and VRAM, operating system, backend, quantisation, context length, target model and acceptable maintenance effort. That checklist predicts local-inference success more reliably than a brand name or an isolated benchmark number.
Quick Recap
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