Choose a GPU by first deciding which model you want to run, at what quantization and context length, and whether you need to load it fully into GPU memory. VRAM is a capacity screen, not a guarantee of model fit or speed: weights are only one part of memory use, and runtime compatibility determines whether the GPU does the work at all.
Start with the model and workload, not the graphics card
Before comparing GPUs, identify the specific model, its quantized file, your target context length, and the way you plan to use it. A chat session with a short context has different memory needs from long-context prompting, and concurrent requests or other GPU workloads can add further demand.
Do not equate a model’s parameter count or advertised size with the full amount of GPU memory it will need. The weights must fit, but inference also needs memory for the context or KV cache, runtime allocations, and other active workloads. Quantization can reduce the space used by weights, but does not eliminate those additional needs. The practical question is whether the chosen runtime can allocate the model and its working memory at your intended settings.
Make a fit checklist
- Choose the exact model and quantization you intend to load.
- Set a realistic context length and account for any concurrent requests.
- Leave headroom for runtime overhead and other GPU use rather than treating the card’s full advertised capacity as available to model weights.
- Check whether your application can split work between GPU and system memory; if it can, establish the performance trade-off for your own workload instead of assuming it will behave like a fully GPU-resident model.
Use VRAM as a screening number, not a promise
Published specifications show the scale of two relevant examples, but do not establish that one is faster or better value. NVIDIA lists the GeForce RTX 5090 with a standard 32 GB GDDR7 configuration. AMD’s ROCm 10.0.0 GPU specification table lists the Radeon RX 9070 XT at 16 GiB VRAM. These are manufacturer-published capacity figures; what an application can allocate depends on the system and workload.
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| GPU example | Published memory figure | What the figure tells you |
|---|---|---|
| NVIDIA GeForce RTX 5090 | 32 GB GDDR7 standard configuration, per NVIDIA’s product specifications | A larger published memory capacity than the RX 9070 XT figure below; it does not establish inference speed, model fit at a particular context, price, or value. |
| AMD Radeon RX 9070 XT | 16 GiB VRAM, per AMD ROCm 10.0.0 GPU specifications | A published hardware capacity. AMD’s separate llama.cpp documentation example reports 16,304 MiB total and 15,770 MiB free, but that is a software-reported documentation example, not an independent test or a guarantee for every system. |
Sources: NVIDIA GeForce RTX 5090 specifications; AMD ROCm 10.0.0 GPU specifications; AMD’s llama.cpp ROCm guide.
AMD’s older ROCm 6.4.1 Radeon guide recommends a 40GB GPU for 70B use cases. Treat this as dated vendor guidance, not a universal minimum: whether a 70B model fits depends on quantization, context length, runtime, and other memory use. It is not a substitute for checking the exact model file and allocation in your intended setup.
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Confirm that the GPU works with your runtime and operating system
A card’s specifications alone do not establish that your inference application supports it. Check the exact GPU, runtime or backend, driver requirements, and operating system together. Support can differ by software release and platform; for AMD ROCm on Linux, AMD’s requirements page lists supported Radeon GPUs and distributions, and the applicable release matrix should be checked before purchase or installation.
For the Radeon RX 9070 XT, AMD’s ROCm 10.0.0 GPU table supplies the VRAM figure, while the rolling Linux requirements page is the place to verify supported distributions and release-specific compatibility. These answer different questions: a GPU appearing in a specification table does not by itself confirm that a particular runtime setup is supported.
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Compatibility sources: AMD ROCm Linux system requirements; AMD ROCm 10.0.0 GPU specifications.
Verify actual allocation and GPU offload
Installation or device detection is not proof that inference runs on the GPU. AMD’s llama.cpp guide cautions: “Listing the devices confirms that the ROCm libraries were found, but it does not confirm that computation runs on the GPU.” Use the verification tools and logs for your chosen runtime while loading and generating with the model; confirm that GPU memory is allocated and that the inference workload is actually offloaded. A device list alone only verifies library discovery.
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AMD’s guide includes an RX 9070 XT example reporting 16,304 MiB total and 15,770 MiB free. That reading illustrates why reported usable capacity can differ from a simple specification number, but it is a documentation example rather than a result measured on your machine. Check the live allocation under your own model, context, and application settings.
Verification guidance: AMD llama.cpp inference on ROCm.
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Compare performance, cost, and physical fit only after the model fits
Once candidate cards pass the model-fit and compatibility checks, compare the factors that determine whether they make sense in your system:
- Speed: Look for inference measurements using the same model, quantization, context, runtime, and settings. The published capacity figures above do not provide a comparable tokens-per-second benchmark, so they cannot identify a speed winner.
- Cost and availability: Check current local pricing and stock when you are ready to buy. The cited specifications do not establish current street prices or value per dollar.
- Power and physical compatibility: Confirm the candidate card’s power requirements, case clearance, power-supply capacity, and connectors against the exact card and your PC. The cited sources do not provide a comparative power or system-fit assessment.
- Runtime support: Recheck the specific backend and driver path you plan to use, especially if support depends on a particular operating system or ROCm release.
Do not use more VRAM as a proxy for higher inference speed. Capacity helps determine what can fit; speed, compatibility, cost, and system requirements need their own evidence and checks.
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