Distributed LLM Inference on NVIDIA Blackwell and Apple Silicon via 10GbE

The quest for massive GPU VRAM often leads developers to a crossroads: invest in prohibitively expensive enterprise clusters or find creative ways to bridge heterogeneous hardware. In this technical exploration, we examine a hybrid setup that combines the raw compute power of the NVIDIA DGX Spark (featuring the GB10 Blackwell architecture) with the unified memory efficiency of an Apple Mac Studio M2 Ultra. By linking these two distinct ecosystems over a direct 10-Gigabit Ethernet (10GbE) connection, we can unlock a combined 248 GB of GPU-accessible memory—enough to run models exceeding 200 billion parameters that neither machine could handle in isolation.

While hardware enthusiasts often look to cloud providers like n1n.ai for instant access to high-performance LLM APIs, building a local distributed inference rig offers unique insights into the bottlenecks of modern AI networking. This guide details the implementation, the failures encountered with the Exo framework, and the successful deployment using llama.cpp's RPC backend.

The Hardware Landscape

Our testbed consists of two machines that excel in fundamentally different areas of the compute spectrum:

1. NVIDIA DGX Spark: Powered by the GB10 Blackwell GPU with 120 GB of unified memory. This machine utilizes cutting-edge Tensor Cores and CUDA 13, offering unparalleled throughput for matrix multiplications.
2. Mac Studio (M2 Ultra): Equipped with 128 GB of unified memory. While its raw TFLOPS might trail the Blackwell, its memory bandwidth and Metal optimization make it a formidable inference node.

By combining these, we reach 248 GB of total VRAM. This capacity is critical for running high-quantization versions of DeepSeek-R1, Qwen3-235B, or MiniMax M2.5, which typically require more memory than a single consumer or prosumer GPU can provide. For developers who need this level of power without the physical hardware maintenance, n1n.ai provides a seamless alternative through their aggregated API access.

Networking: The 10GbE Direct Link

To minimize latency, we bypassed traditional network switches. A direct CAT6A cable was used to connect the DGX's Realtek 10GbE NIC (enP7s7) to the Mac Studio's native 10GbE port (en0).

• DGX IP: 192.168.100.2/24
• Mac Studio IP: 192.168.100.1/24

Measured throughput reached 9.41 Gbps. In a distributed inference scenario, the network isn't just for data transfer; it becomes the backplane for the model's KV cache and weight synchronization. Every millisecond of jitter counts when you are splitting a model across 80+ layers.

The Software Struggle: Exo and the MLX Wall

Initially, we attempted to use Exo, a distributed inference framework designed to leverage MLX on both Metal and CUDA backends. Exo's promise is peer discovery and automatic model partitioning. However, we encountered a significant roadblock: the mx.distributed.init(backend="ring") function hangs indefinitely on CUDA environments as of MLX version 0.31.1.

Despite submitting PRs to fix edge oscillation and Linux interface detection, the core distributed path remained blocked. This highlights a critical reality in the AI world: while high-level APIs like those found on n1n.ai offer stability, DIY heterogeneous distributed computing is still on the bleeding edge of software compatibility.

The Solution: llama.cpp RPC Backend

Llama.cpp takes a more robust approach via its Remote Procedure Call (RPC) backend. Instead of requiring a unified ML framework across all nodes, it allows one machine to act as the primary host while offloading specific computational layers to remote servers.

1. Building the Environment

Both machines must be built from the same commit to ensure protocol compatibility.

On the DGX (Linux/CUDA):

# Build with CUDA and RPC support
cmake -B build -DGGML_CUDA=ON -DGGML_RPC=ON
cmake --build build --config Release

# Start the RPC server
LD_LIBRARY_PATH=build/bin build/bin/rpc-server -H 192.168.100.2 -p 50052

On the Mac Studio (macOS/Metal):

# Build with Metal and RPC support
cmake -B build -DGGML_METAL=ON -DGGML_RPC=ON
cmake --build build --config Release

# Start llama-server and offload layers to the DGX
build/bin/llama-server \
  -m /models/minimax-m2.5-q4_k_m.gguf \
  --rpc 192.168.100.2:50052 \
  -ngl 99 \
  --host 0.0.0.0 --port 9999 \
  -c 4096

Performance Benchmarks and Analysis

We tested two models: a lightweight Llama 3 8B and a heavy-duty Llama 3 70B. The results reveal the inherent trade-offs of network-based inference.

The Prefill Advantage

Prompt processing (prefill) is "embarrassingly parallel." The DGX Blackwell's Tensor Cores significantly accelerated the matrix multiplications required for input tokens. For the 8B model, we saw a 4.2x speedup in prefill. Even the 70B model saw a slight gain, though it was largely bottlenecked by the 10GbE link's ability to move data between the nodes.

The Decode Bottleneck

Token generation (decode) is sequential. Each token generated requires a round-trip across the network to synchronize the Key-Value (KV) cache states. At 10 Gbps, this adds approximately 0.2ms per layer per token. With a model like Llama 3 70B having 80 layers, that equates to 16ms of network overhead per token—effectively halving the generation speed.

When is Distributed Inference Worth It?

If a model fits on a single machine, local is always faster. The overhead of the network stack outweighs any compute gains from additional GPUs. However, for models that cannot fit on one machine, distributed inference is a game-changer.

With our 248 GB pool, we can run:

• MiniMax M2.5 Q4_K_M (138 GB): A 230B parameter MoE model.
• Qwen3-235B Q4_K_M (132 GB): A massive 22B active parameter model.
• DeepSeek-R1: At high quantization for complex reasoning tasks.

At Q4 quantization, a 200B+ MoE model achieves roughly 4–8 tokens per second. While not suitable for real-time chat, it is highly effective for background tasks like code review, complex data extraction, or long-form reasoning.

Technical Pro-Tips for Heterogeneous Setups

1. GGUF Consistency: Not all GGUF files are created equal. Ollama-generated GGUFs often contain custom metadata (like rope.dimension_sections) that upstream llama.cpp cannot parse correctly. Always source your models from reputable community contributors like bartowski on Hugging Face.
2. Disaggregated Architecture: The benchmark results suggest a future where we use high-compute nodes (Blackwell) for the prefill phase and memory-bandwidth-optimized nodes (Apple Silicon) for the decode phase. This "disaggregated" approach could maximize the strengths of both platforms.
3. Network Optimization: Ensure your MTU (Maximum Transmission Unit) is set to 9000 (Jumbo Frames) on both ends of the 10GbE link to reduce packet overhead.

Conclusion

Bridging NVIDIA Blackwell and Apple Silicon is no longer a theoretical exercise—it is a functional reality. While frameworks like Exo are still maturing, llama.cpp RPC provides a stable path for developers to pool resources and tackle the largest models in the open-source ecosystem.

For those who prefer to skip the hardware configuration and jump straight to development, n1n.ai offers a high-speed, reliable API gateway to the world's most powerful LLMs, ensuring you have the compute you need without the 10GbE cable mess.

Get a free API key at n1n.ai