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For generating text with a local large language model, memory bandwidth can matter more than CPU core count: each decode step needs the model’s weights, and moving those weights quickly can limit how fast tokens appear. That is a workload-specific rule of thumb, not a universal ranking of computer specs. Memory capacity, GPU capability, and software support also affect whether a model fits and how quickly it runs.
Why bandwidth matters during LLM text generation
LLM text generation has two distinct phases. During prompt processing (often called prefill), the system processes the input prompt. During decode, it generates output one token at a time. Decode is sequential, and the model’s active weights must be accessed repeatedly as each next token is produced. That makes the rate at which data moves from memory relevant to generation speed.
CPU core count tells you how many CPU cores a processor has; it does not tell you how quickly the whole system can supply model data to the hardware doing inference. On a GPU-accelerated system, the GPU, memory subsystem, and the software that connects them all matter. A system with fewer CPU cores can therefore generate tokens faster than one with more CPU cores, if it has an advantage in the parts of the system that constrain that workload.
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Capacity and bandwidth solve different problems
- Memory capacity determines whether the model, its context, and runtime working data can fit in memory without unwanted offloading. A larger capacity does not, by itself, mean faster generation.
- Memory bandwidth describes how quickly data can be transferred. It can affect decode speed when moving weights is a bottleneck, but it does not guarantee a particular token rate.
Fit depends on more than a model’s advertised size: quantization, context length, model architecture, runtime overhead, and other active workloads all affect memory use. There is no universal model-size cutoff that can be inferred from a system’s capacity alone.
What current specification figures do—and do not—show
The figures below are published specifications or named test-system configurations, not a controlled, apples-to-apples performance ranking. “Up to” figures describe the vendor’s listed maximum configuration, not every model of a product.
| System or configuration | Memory capacity | Published bandwidth | CPU/GPU details stated in the cited source |
|---|---|---|---|
| MacBook Pro with M5 Max (Apple specification) | Up to 128GB unified memory | Up to 614GB/s | 18-core CPU; up to 40-core GPU |
| M5 Ultra (Apple, 2026 announcement) | Up to 512GB unified memory | 1.2TB/s unified memory bandwidth | Not stated in the cited figure |
| M6 Mac mini (Apple specification) | Not stated here | Up to 170GB/s unified memory bandwidth | Not stated in the cited figure |
| M5 Pro Mac mini (Apple specification) | Not stated here | 307GB/s | Not stated in the cited figure |
| M4 Max test system (Tom’s Hardware, 2026) | 128GB | 546GB/s rated | 16-core CPU; 40-core GPU |
| Nvidia GB10 test system (Tom’s Hardware, 2026) | 128GB unified memory | 273GB/s | Not stated in the comparison configuration |
| AMD Ryzen AI Max+ 395 test system (Tom’s Hardware, 2026) | 128GB unified memory | 256GB/s | Not stated in the comparison configuration |
Apple’s figures are vendor specifications for the listed configurations: MacBook Pro technical specifications, Apple’s M6 and M5 Ultra announcement, and Mac mini technical specifications. The other three rows reproduce the configurations described in Tom’s Hardware’s comparison. These numbers do not establish which system is faster: platforms differ in GPU architecture, supported precision, software, and other factors.
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On Apple Silicon, CPU and GPU can access the same unified memory pool. The MLX unified-memory documentation explains that MLX arrays live in unified memory. Apple’s MLX developer session describes using Metal GPU acceleration while taking advantage of that shared memory, so CPU and GPU operations can work on the same data. Apple’s WWDC25 MLX session provides the framework context.
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Shared memory can be useful for local inference, but it does not make all systems or frameworks interchangeable. Check that the runtime you intend to use supports the device and model, and that its implementation can use the relevant accelerator. Framework behavior and supported precisions can change practical performance even when two machines advertise similar bandwidth.
How to choose a computer for local AI
- Pick the workload first. Decide whether you care about generating LLM responses, processing long prompts, image generation, training, or CPU-only inference. A decode-focused bandwidth comparison should not be generalized to those other workloads.
- Check whether the model fits. Account for the model, quantization, context length, runtime, and memory needed by other applications. Capacity is a fit constraint, not a speed score.
- Check accelerator and framework compatibility. Confirm that your intended model and runtime support the system’s GPU or other accelerator, including the precision and features you plan to use.
- Use bandwidth as one clue, not a verdict. A higher published figure may be relevant to bandwidth-sensitive decode, but cannot by itself predict token throughput.
- Compare matched performance results if speed is decisive. Look for tests using the same model, quantization, prompt and context, runtime version, batch size, and power conditions. Without those controls, a headline token rate or hardware specification may not answer your use case.
When CPU cores still matter
CPU cores remain part of the system. They can matter for CPU-only inference and for work handled by the CPU around an accelerated workload. But the number of CPU cores alone is a poor shortcut for predicting GPU-accelerated LLM decode: it omits memory movement, GPU configuration, capacity, and runtime behavior. The useful comparison is the complete system under the workload you actually plan to run.
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