M4 Max Mac Studio Beats GB10 and Strix Halo in Local AI
Real-world local AI testing indicates that the M4 Max Mac Studio can deliver higher LLM decoding throughput than NVIDIA’s GB10 and AMD’s Strix Halo platforms. The primary advantage is Apple’s unusually high unified-memory bandwidth, which reaches 546 GB/s in the tested configuration.
The result is particularly relevant for local LLM inference, where token-generation performance can become heavily constrained by memory movement rather than raw compute throughput. However, memory bandwidth is only one component of end-to-end AI performance, and software optimization, model architecture, quantization, GPU utilization, and memory capacity also influence results.
For developers evaluating compact AI workstations, the M4 Max therefore presents an interesting alternative to dedicated NVIDIA and AMD platforms traditionally favored for local AI development.
🚀 M4 Max Shows Strong Local LLM Decoding Performance #
The test compares three compact workstation-class platforms commonly used for local AI workloads:
- NVIDIA GB10, used in systems such as the DGX Spark and Dell Pro Max
- AMD Ryzen AI Max+ 395, based on the Strix Halo platform
- Apple M4 Max, tested in a Mac Studio configuration with 128GB of unified memory
The GB10 platform has generally been attractive as a local AI development environment because of its broad software ecosystem and relatively mature AI tooling. Its limitation in this particular workload is LLM decoding throughput, where memory bandwidth becomes an important bottleneck.
Why LLM Decoding Is Memory-Bound #
LLM inference consists of two fundamentally different phases: prefill and decode.
During decoding, tokens are generated sequentially. Each newly generated token requires the model to execute through its layers in order, while the relevant model weights must be accessed repeatedly from memory.
For single-token decoding, the amount of arithmetic performed per layer can be relatively small compared with the volume of weight data that must be read. This produces a workload with a low arithmetic intensity, making memory bandwidth and data movement efficiency critical determinants of tokens-per-second performance.
As a result, a platform with a large unified memory pool and substantially higher memory bandwidth can outperform a system with greater theoretical compute capability in specific LLM decoding scenarios.
📊 M4 Max vs GB10 vs Strix Halo Specifications #
The tested M4 Max Mac Studio uses a 16-core CPU, 40-core GPU, and 128GB of unified memory. Its launch price was approximately $3,699, placing it in a similar price category to the roughly $3,999 DGX Spark.
| Platform | CPU | GPU | Memory | Memory Bus | Bandwidth |
|---|---|---|---|---|---|
| NVIDIA GB10 | 10P + 10E cores | 48 Blackwell SMs / 6,144 CUDA cores | 128GB LPDDR5X-8533 | 256-bit | 273 GB/s |
| AMD Strix Halo | 16 performance cores | 40 RDNA 3.5 CUs / 2,560 ALUs | 128GB LPDDR5X-8000 | 256-bit | 256 GB/s |
| Apple M4 Max | 12P + 4E cores | 40-core GPU | 128GB unified memory | Ultra-wide bus | 546 GB/s |
The M4 Max provides approximately twice the memory bandwidth of both GB10 and Strix Halo. That difference is particularly significant for bandwidth-sensitive LLM decoding workloads.
M4 Max GPU Architecture Remains Difficult to Compare Directly #
Apple does not publicly disclose the same level of GPU architectural detail provided by NVIDIA and AMD. Precise information about the M4 Max GPU’s clock frequency and execution-unit configuration is therefore unavailable.
Based on architectural extrapolation from previous Apple Silicon generations, the 40-core GPU could potentially contain a large number of execution units, but such estimates should not be treated as confirmed specifications.
Consequently, theoretical GPU compute comparisons between the M4 Max, GB10, and Strix Halo remain difficult. The measured LLM decoding results provide a more useful indication of performance for this particular workload.
Apple also offers a lower-tier M4 Max configuration with a 14-core CPU and 410 GB/s of memory bandwidth, providing another performance tier for users who do not require the maximum configuration.
🧠 Why Unified Memory Matters for Local AI #
Apple’s unified-memory architecture is particularly well suited to workloads where CPU and GPU components need access to a large shared model state.
Instead of maintaining separate CPU and GPU memory pools, Apple Silicon allows the processor’s compute engines to access the same unified memory subsystem. For local LLM workloads, this can simplify deployment of large models while avoiding explicit transfers between discrete system RAM and GPU VRAM.
Combined with the M4 Max’s 546 GB/s memory bandwidth, the architecture provides a strong foundation for bandwidth-sensitive inference.
This does not mean higher memory bandwidth automatically produces better AI performance across every workload. Large-batch inference, model training, prefill-heavy workloads, and workloads that depend heavily on specialized accelerator instructions can produce very different performance characteristics.
The M4 Max’s advantage is therefore best understood as workload-specific, with sequential LLM decoding being a particularly favorable case.
🖥️ Mac Studio Design and Connectivity #
The Mac Studio maintains Apple’s compact desktop workstation design, with a minimal exterior and a thermal system designed for sustained workloads.
Air is drawn through the intake system around the lower portion of the chassis and exhausted through the rear vents. This configuration allows the system to maintain high-performance operation while keeping acoustic output relatively controlled.
Configuration and Availability #
Availability of high-memory M4 Max Mac Studio configurations has become more limited. Some configurations have reportedly topped out at 64GB of memory, while inventory constraints and extended delivery windows have made the 128GB configuration harder to obtain.
For local AI developers, memory capacity is particularly important because it determines which model sizes and quantization formats can be loaded without relying on external accelerators or aggressive memory-management strategies.
I/O and Expansion #
The rear I/O configuration is well suited to workstation and cluster deployments. It includes:
- Four Thunderbolt 5 ports supporting up to 120Gbps
- Low-latency RDMA networking capabilities for specialized multi-Mac Studio deployments
- External Thunderbolt display connectivity
- DisplayPort 2.1 Alt Mode support
- Two USB 3 ports at up to 5Gbps
- 10Gb Ethernet
- HDMI 2.1
- 3.5mm headphone output
The front panel adds two 10Gbps USB Type-C ports and a UHS-II SD card reader, providing convenient access to peripherals and removable storage.
🔬 What the Results Mean for Local AI Workstations #
The M4 Max Mac Studio demonstrates that local AI performance cannot be evaluated solely through CUDA core counts, GPU compute throughput, or theoretical TOPS figures.
For autoregressive LLM decoding, the ability to move model weights through memory efficiently can have an outsized effect on tokens-per-second performance. In this test, the M4 Max’s 546 GB/s unified-memory bandwidth gives it a substantial advantage over the 273 GB/s GB10 and 256 GB/s Strix Halo configurations.
For developers building local inference systems, the comparison highlights an important trade-off:
NVIDIA remains attractive for software compatibility and mature AI acceleration tooling, while Apple Silicon can be exceptionally competitive for bandwidth-bound local LLM inference when sufficient unified memory is available.
The M4 Max Mac Studio is therefore not universally faster than GB10 or Strix Halo, but its real-world decoding performance demonstrates how an unusually wide memory subsystem can translate into a meaningful advantage for specific local AI workloads.