proxmox/docs/11-references/XE9680_VS_R750_DECISION.md

2× XE9680 vs 3× R750 — GPU/AI tier decision

Last updated: 2026-03-03
Context: The 3× R750 were planned for GPU/AI workloads. This doc compares that plan to using 2× Dell PowerEdge XE9680 (8× NVIDIA A100 80GB SXM4 per node) instead.

Reference: Dell PowerEdge XE9680 — 8× NVIDIA A100 80GB SXM4

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Opinion summary

• For a dedicated GPU/AI tier: 2× XE9680 is the stronger choice than 3× R750 (GPU-equipped) if you need serious capacity: 16× A100 80GB, purpose-built cooling and power, NVLink, and optional 200G InfiniBand. Pricing (vendor example): $94,999 per node on sale (list $179,000) → $189,998 for 2 nodes; vs ~$30k–80k for 3× R750 with GPUs.
• 3× R750 (GPU) still makes sense if you prefer lower cost, fewer GPUs (e.g. 6–12 total across 3 nodes), PCIe-based GPUs (e.g. A6000, L40S), and more flexibility to mix GPU and CPU workloads per node.
• Recommendation: Prefer 2× XE9680 for heavy AI/ML (training, large models, 80GB VRAM); prefer 3× R750 + GPUs for cost-sensitive or lighter/mixed GPU workloads.

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3× R750 as GPU/AI nodes

Aspect	R750 (2U, GPU-equipped)
Form factor	2U per node; 6U total for 3
GPU capacity	Typically 2–4 PCIe GPUs per node (e.g. NVIDIA A6000, L40S, A40) → 6–12 GPUs total; depends on TDP and slot layout
GPU memory	PCIe cards: often 24–48GB per GPU; no 80GB SXM4 in 2U
Interconnect	PCIe; no NVLink across GPUs; 10G/25G typical unless you add high-speed NICs
Use case	Lighter training, inference, mixed workloads; good for dev/staging GPU pools
Cost (approx)	~$10k–25k per node (server + GPUs) → ~$30k–75k for 3 nodes
Power/cooling	Moderate; 2U-appropriate

R750 is a general-purpose 2U that can hold GPUs; it is not an 8-GPU dense AI chassis.

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2× XE9680 as GPU/AI nodes

Aspect	XE9680 (6U, 8× A100 SXM4)
Form factor	6U per node; 12U total for 2
GPU capacity	8× NVIDIA A100 80GB SXM4 per node → 16× A100 80GB total
GPU memory	80GB per A100; NVLink within node for fast multi-GPU training
Interconnect	100GbE + 200GbE InfiniBand (Mellanox ConnectX-6); ideal for multi-node scaling
Use case	Large model training, HPC, generative AI, serious ML/DL workloads
Cost (approx)	$94,999/node on sale (list $179,000) → $189,998 for 2 nodes
Power/cooling	High; 6U chassis and power designed for 8× A100

XE9680 is purpose-built for dense GPU AI; no 2U server matches this density and memory per node.

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Side-by-side (GPU/AI)

Factor	3× R750 (GPU)	2× XE9680
Total GPUs	6–12 (PCIe, config-dependent)	16× A100 80GB
VRAM per GPU	Typically 24–48GB	80GB
Multi-GPU	PCIe only	NVLink within node
Multi-node	10G/25G typical	200G InfiniBand (option)
Rack space	6U	12U
Cost	Lower (~$30k–75k ballpark)	$189,998 for 2× (2 × $94,999 sale)
Best for	Lighter AI, mixed workloads, budget	Heavy training, large models, max throughput

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Recommendation (GPU/AI tier)

Goal	Recommendation
Maximum GPU capacity and large-model training	2× XE9680 — 16× A100 80GB, NVLink, InfiniBand; accept higher cost and power.
Lower cost, flexible GPU pool (dev/staging/inference)	3× R750 + GPUs — 6–12 GPUs, PCIe-based; good value and spread across 3 nodes.
Start small, expand later	3× R750 now; add XE9680 (or similar) later when workload justifies it.

If you replace the planned 3× R750 (GPU) with 2× XE9680: document the GPU tier as “2× XE9680” in the inventory and assign IPs (e.g. .24–.25 for the two nodes); the R750 IP block (.24–.26) can be repurposed or left for future use.

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Inventory and docs

• HARDWARE_INVENTORY_MASTER.md — R750 row updated to “GPU/AI tier”; optional XE9680 alternative.
• 13_NODE_NETWORK_AND_CABLING_CHECKLIST.md — If using XE9680, cable 200G/100G to your fabric (or 10G to existing XG backbone for management).
• 13_NODE_AND_ASSETS_BRING_ONLINE_CHECKLIST.md — Phase 3: treat as “GPU tier (R750 or XE9680)” and document which platform is chosen.

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References

• HARDWARE_INVENTORY_MASTER.md — GPU/AI tier role and IP plan.
• Dell XE9680 — 8× A100 80GB (The Server Store) — specs and pricing (refurb).