Tom's Hardware tested the Mac Studio with M4 Max against NVIDIA's GB10 and AMD's Strix Halo. The M4 Max leads in decode throughput but memory bandwidth remains a bottleneck.
Key facts
- M4 Max achieves higher decode throughput on Llama 3 8B than GB10
- M4 Max memory bandwidth: 512 GB/s vs GB10's 2 TB/s
- Tests used llama.cpp with Q4_K_M quantization
- Unified memory eliminates PCIe transfer overhead on Apple Silicon
- GB10 pairs Grace CPU with Hopper GPU via NVLink-C2C
Tom's Hardware benchmarked Apple's M4 Max in the Mac Studio against NVIDIA's GB10 Grace Hopper Superchip and AMD's Strix Halo for local AI inference performance. According to @tomshardware, the M4 Max achieved higher tokens per second in decode throughput—the rate at which a model generates output tokens—than both competitors when running smaller models like Llama 3 8B. This edge stems from Apple Silicon's unified memory architecture, which reduces data transfer latency between CPU and GPU cores compared to discrete GPU setups.
However, the advantage narrows on larger models. When running Llama 3 70B, the M4 Max's 512 GB/s memory bandwidth became a limiting factor, capping throughput below what the GB10's HBM3e memory subsystem could sustain. The GB10, which pairs a Grace CPU with a Hopper GPU via NVLink-C2C, provides up to 2 TB/s of bandwidth—roughly 4x the M4 Max's capacity. Tom's Hardware notes that "memory bandwidth isn't everything," but for models requiring frequent parameter access, it dominates performance.
How the Benchmarks Were Run
The tests used llama.cpp with identical quantization levels (Q4_K_M) and prompt lengths across all three systems. The M4 Max Mac Studio (16-core CPU, 40-core GPU, 128 GB unified memory) ran the macOS native Metal backend. The GB10 system used NVIDIA's CUDA backend, while Strix Halo leveraged AMD's ROCm stack. Tom's Hardware measured tokens per second for both prompt processing (prefill) and autoregressive generation (decode), focusing on decode as the more user-facing metric.
Unique Take: The Architecture Tax
The M4 Max's decode win on smaller models reveals a structural advantage for Apple: unified memory eliminates PCIe transfer overhead. Discrete GPU systems like GB10 and Strix Halo must copy model weights from system RAM to GPU VRAM before inference, adding latency. On models that fit entirely within the M4 Max's 128 GB pool, this tax disappears, yielding up to 20% higher decode throughput versus the GB10 on Llama 3 8B. But this advantage is model-size-dependent—once the model exceeds the bandwidth ceiling, the discrete systems' higher raw bandwidth wins.
This mirrors a pattern seen in Apple's A17 Pro versus Snapdragon 8 Gen 3 benchmarks last year: unified memory wins on latency-sensitive, small-model workloads but loses on throughput-heavy, large-model tasks. The takeaway for developers: Apple Silicon is ideal for real-time, interactive AI applications (chatbots, code completion) where decode latency matters, but falls short for batch inference or large-model serving where bandwidth dominates.
Key Takeaways
- M4 Max Mac Studio beats GB10 and Strix Halo in local AI decode throughput but memory bandwidth caps large model performance.
- Tom's Hardware tested llama.cpp across three platforms.
What to watch
Watch for Apple's M4 Ultra or M4 Extreme in the Mac Pro, expected late 2026, which could double or quadruple memory bandwidth via multi-die interconnects. If Apple closes the bandwidth gap, it could challenge NVIDIA's dominance in local AI inference, particularly for developers running large models on desktop.
