Unified Architecture Showdown: AMD Strix Halo vs Apple M3/M4/M5 Max for LLM Inference

Introduction

When running large language models (LLMs) locally, the two most critical performance factors are memory bandwidth and raw compute throughput. Memory bandwidth dictates how fast tokens can be generated (decoding), while compute throughput (TFLOPS) determines how quickly the model can process the initial input prompt (prefill). Both AMD and Apple have embraced unified memory architectures, where the CPU, GPU, and Neural Engine share a single pool of high-bandwidth memory, eliminating the need to copy data between separate VRAM and system RAM. This architecture is ideal for LLM inference because large models can be loaded entirely into the unified memory, enabling the use of massive models that would not fit on traditional discrete GPUs like the RTX 4090 with its 24 GB VRAM. In this article, we compare the latest AMD Strix Halo (Ryzen AI Max 300 series) with up to 128 GB of LPDDR5X-8000 memory against Apple's M3 Pro/Max, M4 Pro/Max, and the newly enhanced M5 Pro/Max series, focusing on how memory bandwidth affects prompt processing and decoding speeds.

Architectural Differences

The AMD Strix Halo represents a major push into high-performance integrated graphics for the x86 ecosystem. It features a unified memory architecture with up to 128 GB of LPDDR5X memory running at 8000 MT/s, providing approximately 256 GB/s of memory bandwidth. The integrated RDNA 3.5 GPU can scale up to 40 Compute Units, delivering substantial TFLOPS for compute-bound workloads. In contrast, Apple's M-series chips use a custom unified memory on a system-in-package design. The M3 Max achieves around 400 GB/s, the M4 Max ranges from 410 to 546 GB/s depending on configuration, and the latest M5 Max tops out at an industry-leading 614 GB/s. Apple also includes dedicated Neural Engine cores integrated into each GPU cluster, which they claim improve AI performance up to 4x over the M4 generation. These architectural choices lead to distinct strengths in different phases of LLM inference.

Memory Bandwidth Comparison

Prompt Processing (Prefill) Speed

Prompt processing is the phase where the model reads the entire input sequence (prompt) and computes the initial key-value cache and hidden states. This operation is heavily compute-bound because the model must perform numerous matrix multiplications simultaneously over all input tokens. The AMD Strix Halo, with its RDNA 3.5 GPU, can deliver high TFLOPS that rival many discrete GPUs. In benchmarks, the Strix Halo can process prompts of several thousand tokens in under a second, making it a strong competitor for applications like document analysis or retrieval-augmented generation where long prompts are common. Apple's M5 Max has improved its Neural Engine and GPU compute capabilities, but the raw TFLOPS of the Strix Halo still gives it a slight edge in pure prefill speed. However, the gap narrows when using optimized inference frameworks like MLX that exploit Apple's unified memory efficiently.

Decoding (Token Generation) Speed

Decoding is the autoregressive generation of new tokens one by one. Each step requires fetching the entire model's weights from memory into the compute units. This process is almost entirely memory-bandwidth bound. The token generation speed (tokens per second) is directly proportional to the memory bandwidth divided by the model size in bytes. For example, a 70-billion-parameter model in 4-bit quantized form uses roughly 35 GB of memory. With AMD's 256 GB/s bandwidth, the theoretical maximum decoding speed is about 7.3 tokens per second. In contrast, Apple's M5 Max with 614 GB/s can achieve about 17.5 tokens per second under the same conditions. Real-world measurements confirm this gap: the M5 Max consistently delivers 2-3x higher tokens per second compared to the Strix Halo for models like Llama 3.1 70B Q4. This makes the M5 Max the clear winner for chat applications or text generation where low latency per token is critical.

Capacity and Practical Considerations

Both platforms can be configured with up to 128 GB of unified memory, enabling them to run very large models that would not fit on consumer discrete GPUs. For instance, a 120-billion-parameter model in 8-bit precision or a 70B model in 4-bit can run comfortably on either system. AMD's Strix Halo has the advantage of running on x86 architecture, which may be preferable for users who need Linux compatibility or want to use software that is not optimized for Apple Silicon. Apple's ecosystem, on the other hand, offers the MLX framework and tight integration with macOS, which can further accelerate inference. The M5 Max's massive bandwidth also benefits other bandwidth-sensitive tasks like image generation with Stable Diffusion or video processing.

Conclusion

In the battle of unified architectures for local LLM inference, there is no single winner. AMD Strix Halo offers excellent compute power for prompt processing and a more open platform, while Apple's M5 Max dominates in decoding speed thanks to its superior memory bandwidth. The choice depends on the user's primary workload: if you process long documents or run compute-heavy AI pipelines, the Strix Halo is a compelling option; if you generate long sequences of text or chat interactively, the M5 Max provides a noticeably smoother experience. Both systems represent a new era of high-performance unified memory computing, bringing the power of large models to personal computers without the limitations of discrete GPU VRAM.