Quick Summary:
- AMD Strix Halo mini PCs ($800-1,200): Best value for large model inference. 128GB unified memory at ~$9/GB. Linux-first. Slower bandwidth than Apple silicon.
- Mac Studio M4 Ultra ($3,999-9,999): Best single-device throughput per watt, up to 192GB unified, polished software. Premium pricing per GB.
- NVIDIA DGX Spark (~$3,000-5,000): Highest raw AI compute (1 petaFLOP FP4), GB10 Grace Blackwell superchip, full CUDA ecosystem, 128GB. Enterprise-class capability in workstation form.
Three platforms. Three different answers to the question "what should I run AI inference on at my desk?"
AMD Strix Halo mini PCs are the value play — commodity pricing for unified memory in the 64-128GB range, Linux-native, good enough for 70B models. Mac Studio M4 Ultra is the polished professional workstation — Apple's best hardware in a compact aluminum box, exceptional memory bandwidth, immaculate software integration. NVIDIA DGX Spark is the developer appliance — GB10 Grace Blackwell, 1 petaFLOP of FP4 compute, and the full weight of the CUDA ecosystem behind it.
They overlap in the "serious local AI" category. They don't overlap in price, software ecosystem, or intended audience.
The Hardware: Specs That Matter
NVIDIA DGX Spark
The DGX Spark is the compact member of NVIDIA's DGX personal AI computing line, announced at GTC 2025. It's built around the GB10 Grace Blackwell Superchip: a tightly integrated Grace CPU (Arm Neoverse) and Blackwell GPU connected by a 900 GB/s NVLink-C2C interconnect — not PCIe.
Key specs:
- AI Compute: 1 petaFLOP at FP4, 100 TOPS at INT8
- Memory: 128GB LPDDR5x unified (CPU + GPU shared via NVLink)
- Storage: 4TB NVMe
- CPU: NVIDIA Grace (72-core Arm Neoverse V2)
- Connectivity: Thunderbolt 5, USB4, 10GbE
- OS: DGX OS (Ubuntu-based) + Windows support
- Price: ~$3,000-5,000
The architectural differentiator: that 900 GB/s NVLink-C2C interconnect between CPU and GPU. PCIe 5.0 maxes at ~128 GB/s. Apple's M4 Ultra achieves ~800 GB/s through die-to-die interconnect. NVLink brings NVIDIA to the same tier — enabling true unified memory at bandwidth that doesn't bottleneck inference.
Mac Studio M4 Ultra
Apple's most powerful Mac in the Mac Studio form factor. The M4 Ultra is two M4 Max dies connected via Apple's UltraFusion interconnect — same concept as NVLink, same goal.
Key specs:
- CPU: 28-core (20 performance + 8 efficiency)
- GPU: 60-core (or 80-core on max config)
- Neural Engine: 32-core, 38 TOPS
- Memory: 96GB or 192GB LPDDR5x unified
- Memory Bandwidth: ~800 GB/s (with 192GB/80-core GPU config)
- AI Compute: ~38 TOPS Neural Engine (FP16 GPU compute significantly higher)
- Price: $3,999 (96GB, 60-core GPU) to $9,999 (192GB, 80-core GPU)
The M4 Ultra's memory bandwidth is its headline number for LLM inference. At 800 GB/s, it delivers roughly 3x the bandwidth of AMD Strix Halo's 256 GB/s — which translates directly to 3x the tokens/sec at equivalent model sizes.
AMD Strix Halo Mini PCs (GMKtec EVO-X2 / Beelink GTR9 Pro)
The value tier. Ryzen AI Max+ 395 silicon in mini PC form factor:
- CPU: 16-core Zen 5
- GPU: 40-CU RDNA 3.5 iGPU
- Memory: Up to 128GB LPDDR5x (256-bit bus)
- Memory Bandwidth: ~256 GB/s
- AI Compute: AMD XDNA 2 NPU, 50 TOPS
- Price: ~$800-1,200 depending on RAM tier
Not in the same performance class as M4 Ultra or DGX Spark. The value proposition is price-per-GB and Linux flexibility, not throughput.
For a detailed review of the EVO-X2 specifically, see our GMKtec EVO-X2 review.
Memory Architecture Compared
This comparison is fundamentally about memory — how much, how fast, how accessible.
Price at Max Config
~900 GB/s (NVLink)
~$9,999
~$1,100-1,200 The DGX Spark and M4 Ultra have comparable memory bandwidth — both well above AMD. The M4 Ultra wins on total memory capacity (192GB vs 128GB). The DGX Spark wins on raw AI compute performance (FP4 Blackwell tensor cores vs Apple's ML accelerators).
AMD Strix Halo is significantly lower bandwidth — but also significantly lower cost.
AI Compute: What "1 Petaflop" Actually Means
The DGX Spark's headline number is "1 petaFLOP FP4 AI performance." Let's unpack this.
FP4 (4-bit floating point) is an inference precision format supported by Blackwell architecture. It's analogous to INT4 quantization but with floating-point representation. NVIDIA's tensor cores can execute FP4 matrix math at peak throughput.
For local LLM inference: most open-source models run in FP16 or 4-8 bit integer quantization (GGUF, GPTQ, AWQ). The DGX Spark's Blackwell GPU excels at:
- FP4/INT4 quantized inference with NVIDIA's TensorRT-LLM
- CUDA-accelerated fine-tuning and training (not just inference)
- Multi-model serving and batched inference at scale
The Mac Studio M4 Ultra uses its GPU (Metal) and Neural Engine (ANE) for inference. Peak FP16 GPU compute is lower than Blackwell's AI-specific tensor cores, but for typical GGUF-based inference workflows the gap may be smaller in practice than the theoretical numbers suggest.
Bottom line: For pure LLM inference throughput on standard quantized models (GGUF/GPTQ), the M4 Ultra and DGX Spark are likely comparable. For FP4 precision inference with TensorRT-LLM, DGX Spark has no peer at this price point.
Inference Speed Estimates (70B Model)
All figures are rough estimates:
Est. Tokens/Sec
~20-35 t/s
~12-20 t/s
~4-8 t/s The DGX Spark's Blackwell GPU's AI compute advantage should theoretically deliver 2-4x more tokens/sec than M4 Ultra at 70B. The M4 Ultra delivers roughly 3-5x more than AMD Strix Halo due to the bandwidth advantage.
Software Ecosystem
This is where the platforms diverge most sharply in ways that affect day-to-day usability.
NVIDIA DGX Spark: CUDA is the default. Every major AI framework — PyTorch, JAX, HuggingFace Transformers, vLLM, TensorRT-LLM, llama.cpp (CUDA), ExLlamaV2 — works natively and is actively tested on NVIDIA hardware. If an open-source model releases a new quantization format or inference optimization, CUDA support ships first.
DGX OS is Ubuntu-based, so standard Linux tooling applies. Docker, container orchestration, remote access — all standard.
Mac Studio M4 Ultra: macOS is polished but constraining for server-style AI work. Ollama and LM Studio work flawlessly. MLX-optimized models deliver some of the best inference quality available. Fine-tuning and custom training are possible via Metal but require MLX-native implementations.
The limitation: ROCm doesn't exist on Apple. vLLM doesn't support Metal. Custom CUDA code doesn't run. For pure inference with mainstream tools, it's excellent. For bleeding-edge research or custom inference code, CUDA remains the standard.
AMD Strix Halo (Linux): Full ROCm support on Linux enables PyTorch, vLLM (experimental), and the HuggingFace stack. llama.cpp runs via Vulkan or ROCm. Software compatibility is improving rapidly but still behind CUDA. Ollama and llama.cpp are the reliable inference choices; the broader framework ecosystem is functional but requires more troubleshooting.
Linux Compatibility
Notes
macOS only For teams deploying inference infrastructure — containerized services, CI/CD pipelines, remote access — Linux support is a practical requirement. Mac Studio requires macOS, which excludes it from these workflows.
Value Analysis by Use Case
Running 70B models for personal research:
AMD Strix Halo 128GB ($1,100) is the value answer. Slow but functional. If you need faster 70B inference, Mac Studio M4 Max 128GB ($1,999) is the step up. DGX Spark at $3-5K is overkill unless you also need CUDA.
Building a production inference endpoint: DGX Spark or a workstation-class NVIDIA GPU (RTX 6000 Ada, A6000). CUDA is the production standard for inference infrastructure. Mac Studio and AMD Strix Halo are single-user local tools.
Best creative/coding AI workstation on a budget: AMD Strix Halo mini PC at 64GB (~$750). Runs 35B models adequately, full Linux stack, fraction of Mac Studio pricing.
Best all-around AI workstation, budget no object: Mac Studio M4 Ultra 192GB for power efficiency and software polish, or DGX Spark for CUDA ecosystem and raw AI compute. Both are $3,999+ at the relevant configurations.
Inference capacity for large models (100B+): Only Mac Studio M4 Ultra at 192GB and NVIDIA DGX Spark at 128GB apply. AMD Strix Halo maxes at 128GB and would need Q3_K_M or smaller to run 100B+ models.
Verdict
AMD Strix Halo wins on value. $1,100 for 128GB unified memory with Linux support is a compelling proposition that no competitor matches at this price. The trade-off is significantly lower memory bandwidth and a less mature software ecosystem.
Mac Studio M4 Ultra wins on software polish and power efficiency. Apple's unified memory architecture is mature, MLX is excellent, and the thermal/power profile enables 24/7 operation at moderate noise levels. The price premium is steep.
NVIDIA DGX Spark wins on raw AI compute and ecosystem. For developers who need CUDA, TensorRT-LLM, fine-tuning capability, and maximum inference throughput in a compact form factor, Blackwell in a workstation box is the answer. The $3-5K price puts it in professional territory.
For most hobbyists and independent researchers, the choice is between AMD Strix Halo at 128GB ($1,100) and Mac Studio M4 Max at 128GB ($1,999). The AMD option is 45% cheaper, runs Linux, but delivers about 40-50% less tokens/sec. The Mac Studio is faster, quieter, more energy efficient, but macOS-only and nearly $1,000 more expensive.
Neither answer is wrong. They serve different people.
For a deeper dive on the AMD vs Apple comparison specifically at the mini PC tier, see our AMD Strix Halo Mini PC vs Mac Mini M4 comparison. For setting up the AMD platform's full memory potential under Linux, see the Ryzen AI Max GTT memory guide. For the best inference runtime to run on any of these platforms, see our Ollama vs LM Studio vs llama.cpp vs vLLM guide.