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Start with the workload, not a GPU tier
A model’s parameter count alone cannot tell you whether it will fit or how quickly it will run. Before comparing hardware, identify the actual model file, its quantization, the context length you intend to use, and the runtime settings. Then decide whether you need all model layers on the GPU or can accept partial GPU offload with the rest handled by the CPU.
- Full GPU placement: the model and the runtime’s additional memory needs must fit within usable GPU-addressable memory. This is the target if you want to avoid relying on CPU offload.
- Partial offload: llama.cpp can use CPU+GPU hybrid inference to partially accelerate models larger than total VRAM capacity. It can make an otherwise-too-large model usable, but it is not equivalent to placing the whole model on the GPU.
For either target, allow headroom beyond the file’s nominal size. Context and runtime buffers consume memory, and other processes may also use some of the GPU’s available memory.
Which GPU backends does llama.cpp support?
The llama.cpp project documents CUDA for NVIDIA GPUs, HIP for AMD GPUs, Metal for Apple silicon, SYCL for Intel GPUs, and Vulkan for GPUs. The project’s README describes Apple silicon as a “first-class citizen” optimized via ARM NEON, Accelerate, and Metal frameworks. This documents project support, not identical speed, feature parity, setup difficulty, or compatibility for every product and operating system. Check the backend and build instructions that apply to your specific system in the llama.cpp project.
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Backend fit is one selection factor, not a performance verdict. A supported backend does not show how a particular GPU will perform on your model, quantization, context, and runtime configuration.
Quantization changes the memory-versus-quality tradeoff
GGUF quantization reduces model memory use, but the actual file and quantization level matter. AMD’s July 2025 FAQ gives the following illustration for a 7B model; these are figures from that example, not universal sizes or quality measurements for all models:
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| Quantization in AMD’s 7B example | Stated model size | Stated perplexity increase |
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| Q5_K_M | 4.45G | +0.0142 |
| Q6_K | 5.15G | +0.0044 |
The example shows why choosing a smaller quantization can reduce the memory needed for weights while changing the measured quality metric. It does not establish a universal best quantization. Compare the specific GGUF files available for your model and decide whether the memory saving is worth the quality tradeoff for your use.
AMD’s figures and explanation are in its Ryzen AI FAQ.
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Context length and runtime settings need their own memory budget
Weights are only part of the memory calculation. AMD’s llama.cpp deployment guide explicitly notes that increasing context size incurs more memory use. Runtime settings, buffers, and memory used by other processes also reduce what remains available for the model. A configuration that works at a short context may therefore fail, or require more CPU offload, at a longer one.
AMD reports vendor test results for a particular Kimi K2.5/Ryzen AI Max+ configuration: for 128 decoded tokens, it measured 8.81 tokens per second with Flash Attention disabled and 9.45 with it enabled; at sequence length 8192, it reported 3.46 and 8.30 tokens per second, respectively. These results illustrate that settings and sequence length can affect performance in that configuration; they are not a general GPU comparison or a prediction for other hardware. See AMD’s llama.cpp deployment guide.
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Integrated graphics memory is not free extra system memory
AMD describes Variable Graphics Memory as a BIOS-level option that reallocates a percentage of system RAM to integrated graphics. Memory assigned this way is no longer available as CPU system RAM, so it should not be treated as interchangeable with discrete GPU VRAM.
AMD says Ryzen AI Max+ systems with 128GB of memory can allocate up to 96GB to Variable Graphics Memory. Its FAQ gives an example of total graphics-addressable memory up to 112GB for a particular 128GB configuration. Those figures describe AMD’s platform and configuration; they are not a general capacity claim for other systems or discrete graphics cards. Consult the AMD FAQ for the platform-specific explanation.
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A practical way to compare candidate GPUs
Compare candidate cards only after fixing the workload. For a meaningful speed comparison, use the same GGUF model file, quantization, context length, backend, and runtime settings. Include price geography and date when comparing cost. The evidence available here does not establish a neutral, current shortlist or speed-per-dollar ranking, so it cannot support naming a universal winner.
- Choose the exact GGUF file and quantization. Use the file you intend to run, rather than assuming that parameter count predicts memory needs.
- Set the context and workload. Include the context length and whether you expect concurrent workloads; both can change memory requirements.
- Pick the inference backend supported by your system. Check the relevant llama.cpp setup path for CUDA, HIP, Metal, SYCL, or Vulkan, as applicable.
- Decide whether full placement is required. If the weights plus runtime and context needs exceed usable GPU-addressable memory, decide whether CPU/GPU partial offload is acceptable.
- Compare usable memory, then verify performance under matched settings. Allow room for runtime buffers and other processes. Treat speed claims as specific to the tested configuration, not as transferable rankings.
- Compare dated, region-specific prices and availability. Without those details and matched workload measurements, a value recommendation is not grounded.
What this means when choosing a GPU
Buy for the workload you can describe, not for an unqualified “large model” label. If you need full GPU placement, memory headroom for the chosen GGUF file and context is central. If partial offload is acceptable, llama.cpp provides a documented hybrid path, with a different performance target. Backend support narrows the software options, but a supported GPU is not automatically the fastest or easiest choice.
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