Learn · NVIDIA Hardware & DGX · DGX Spark Clustering & MikroTik Switches
Clustering DGX Sparks over RoCEv2 & NCCL
In plain English
When two DGX Sparks team up, they pool their 128GB memory into one giant 256GB brain. Together, they can easily run 405B-parameter models that neither could run alone! 🤝
What is actually going on
Using NVIDIA ConnectX NICs and NCCL (NVIDIA Collective Communications Library), multiple DGX Sparks split large model weights across nodes using Tensor Parallelism (TP) and Pipeline Parallelism (PP). A 2-node cluster yields 256GB unified RAM (hosting Llama 3 405B at 4-bit), while a 4-node cluster provides 512GB (hosting DeepSeek-R1 671B MoE locally).
Why it matters
Local clustering breaks the single-machine memory ceiling. Instead of spending $50k on a server rack, users link compact desk-side nodes as their model needs grow.
Another way to think about it
Two musicians playing a four-hand piano piece in perfect synchronization across a high-speed audio link. 🎹
Check yourself
How much total unified memory does a 2x DGX Spark cluster provide?
- 64 GB
- 128 GB
- 256 GB
- 512 GB
Show answer
256 GB — Each GB10 carries 128GB, pooling into 256GB across a 2-node cluster.
Which communication library coordinates multi-GPU tensor parallelism on NVIDIA hardware?
- NCCL
- DirectX
- OpenGL
- HTTP/2
Show answer
NCCL — NCCL (NVIDIA Collective Communications Library) provides optimized multi-GPU and multi-node collective communication primitives.