RTX 5090 vs Apple Silicon: Why Your Gaming PC Might Struggle with the Biggest Local LLMs
When you invest in top-tier hardware like an Nvidia RTX 5090 and a Ryzen 7 9800X3D, you expect it to handle everything—including running the largest local large language models (LLMs). However, many users are discovering that Apple Silicon, with its unified memory architecture, can actually outperform these behemoths on massive models. This Q&A explores the technical reasons behind this surprising reality, focusing on memory bandwidth, VRAM constraints, and the unique advantages of Apple's approach.
Why does the RTX 5090 struggle with the biggest local LLMs?
The RTX 5090 is a GPU powerhouse for gaming and many compute workloads, but it has a critical limitation: VRAM capacity. Most high-end consumer GPUs max out at 24GB of VRAM. Large LLMs like Llama 3.1 405B require several hundred gigabytes of memory just to load the model weights. Even quantized versions (e.g., 4-bit) demand around 50–70GB. The RTX 5090 simply doesn't have enough memory to hold these models, forcing the system to swap data via system RAM (typically DDR5), which is orders of magnitude slower than GPU memory. This swapping cripples performance, leading to extremely slow token generation. In contrast, Apple Silicon Macs offer unified memory configurations up to 192GB, with bandwidth of 800GB/s or more, allowing the entire model to fit in fast memory without swapping.
What is unified memory and why does it help Apple Silicon?
Unified memory is a design where the CPU, GPU, and other processors share a single pool of high-bandwidth memory. In Apple's M-series chips, this memory is directly accessible by both the CPU and GPU without copying data back and forth. For LLMs, this means the entire model can reside in a single, fast memory space. The bandwidth (e.g., 800 GB/s on the M3 Ultra) is comparable to the memory bandwidth of an RTX 5090 (around 1 TB/s), but the key difference is capacity. With up to 192GB, Apple Silicon can load models that are far too large for any consumer GPU. This eliminates the need for data paging or quantization that degrades model quality. The RTX 5090, despite its raw compute power (flops), is bottlenecked by its 24GB VRAM limit when handling the largest models.
Does the RTX 5090 have any advantages over Apple Silicon for smaller LLMs?
Yes, for smaller models that fit within its 24GB VRAM—such as Llama 3 8B or Mistral 7B—the RTX 5090 is extremely fast. Its tensor cores and CUDA ecosystem enable optimized inference using libraries like TensorRT-LLM. For these models, Nvidia's GPU can achieve much higher tokens per second than Apple Silicon. Additionally, the RTX 5090 excels at gaming, graphics rendering, and other parallel workloads where Apple Silicon lags. The trade-off becomes apparent only when you scale up to models requiring more than 24GB of memory. For developers or researchers who need to experiment with cutting-edge 100B+ parameter models locally, Apple Silicon offers a more practical solution, while the RTX 5090 remains a beast for everything else.
Can I run large LLMs on an RTX 5090 using CPU offloading?
Yes, it is possible to offload parts of the model to system RAM using frameworks like llama.cpp with GPU offloading. This technique splits the model layers between the GPU's VRAM and the CPU's DDR5 memory. However, performance suffers dramatically. The overhead of transferring data between GPU and CPU over PCIe 4.0 or 5.0 introduces latency, and the DDR5 bandwidth (typically 50–100 GB/s) is far lower than GPU memory. As a result, token generation speeds can drop to less than 1 token per second for large models, making real-time interaction impractical. In contrast, a unified memory Mac can often achieve 5-10 tokens per second or more. For batch processing or experimentation, CPU offloading might be acceptable, but for chat applications, Apple Silicon provides a much smoother experience.
Is memory bandwidth or capacity more important for LLMs?
Both matter, but capacity is the primary bottleneck for the largest models. The RTX 5090 has very high memory bandwidth (approx 1 TB/s) but only 24GB capacity. If a model exceeds that capacity, the system falls back to much slower system RAM, nullifying the bandwidth advantage. Apple Silicon provides both high bandwidth (up to 800 GB/s) and high capacity (up to 192GB), making it ideal for models that need 50-100GB. For smaller models, the RTX 5090's bandwidth and compute lead. Memory bandwidth determines how quickly the GPU can read and write parameters during inference; higher bandwidth leads to faster token generation. But if the model doesn't fit, bandwidth becomes irrelevant. So for the biggest LLMs, capacity is the deciding factor, and that's where Apple Silicon triumphs.
What about the Ryzen 7 9800X3D and system RAM?
The Ryzen 7 9800X3D is an excellent gaming CPU with 3D V-cache, but it doesn't directly affect LLM performance when a GPU is used. In a scenario where CPU offloading is needed, the system RAM (DDR5) becomes crucial. The 9800X3D supports up to 128GB of DDR5-6000, which offers decent bandwidth but still much less than unified memory. The CPU's performance per core is high, but inference on CPUs is generally slower than on GPUs because CPUs lack the massively parallel architecture optimized for matrix operations. In practice, even with a fast CPU, offloaded inference is painfully slow. For users who want to run the largest models locally, investing in a Mac with unified memory is currently a more effective path than even a top-tier AMD + Nvidia combo.