What Can 2x Workstations built on NVIDIA DGX Station B300 Do for Agentic AI?

Agentic AI systems rarely run a single model. A typical pipeline pairs a large reasoning model that plans and delegates with smaller models that retrieve documents, call tools, write code, and check results, and every one of them competes for GPU memory at the same time.

A single Exxact Valence built on NVIDIA DGX Station B300 handles a lot of that from a desk. But what can you do with two?

What One Exxact Valence with B300 Brings to Agentic AI

Each unit is built around the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, pairing one Blackwell Ultra GPU with a 72-core Grace CPU over NVLink-C2C. Per NVIDIA's development guide, a single system provides:

• 748GB of coherent memory: 252GB of HBM3e at up to 7.1TB/s, plus 496GB of LPDDR5X at up to 396GB/s
• Up to 20 petaFLOPS of sparse FP4 compute with native NVFP4 support
• Up to seven isolated MIG partitions
• 800Gb/s of combined networking through two ConnectX-8 400Gb/s ports

What we measured: One Valence with GB300 ran 256 concurrent Qwen3 agents with zero failed agents and a stable 0.824–0.897 pass-rate range. With DeepSeek V4 Flash, vLLM reported 11.1 million tokens of KV-cache capacity—enough for an estimated 10.6 simultaneous sessions configured for one million tokens each.

Those results reinforce the practical limit: agent count alone is not the bottleneck. Model architecture, context length, and KV-cache demand determine capacity. Weights that spill beyond the 252GB of HBM3e into LPDDR5X also run at much lower bandwidth, so repeated agent calls amplify the slowdown.

How to Connect Two NVIDIA DGX Station GB300 Systems

Two GB300 Systems link directly with no switch. NVIDIA's two-system playbook calls for two 400G QSFP cables match port 0s and port 1s on each Exxact Valence with GB300. Each cable acts as an independent 400Gb/s rail running RoCEv2 (RDMA over Converged Ethernet) which moves data between systems' memory with minimal CPU involvement.

There is no NVLink between the two systems. NVIDIA estimates 45 to 60 minutes for core fabric bring-up, including jumbo frames (MTU 9000) and a GPUDirect RDMA check before running NCCL, Ray, or vLLM.

A few practical details to plan for:

• Both QSFP ports go to the peer link. Each unit's regular LAN and storage traffic moves to its 10GbE RJ-45 port.
• Two is the documented ceiling. NVIDIA's documentation describes clustering two systems together and doesn't publish a switched configuration for more. However, if you had a networking switch, coupling multiple Exxact Valence with GB300 is possible.
• Keep the rails private. NVIDIA's dual-system vLLM guide notes the Ray, NCCL, and vLLM coordination traffic on those rails is unauthenticated, so treat both units and the cables between them as one trusted boundary.
• Budget power per unit. Each unit ships with a 1,600W power supply, so plan on a dedicated high wattage circuit. Two may need 220V outlet at least.
• Match the software stack. NVIDIA expects the same firmware, OS, driver, CUDA, and DOCA/OFED versions on both systems before setup.

What 2x GB300 Systems Unlocks for Agentic AI

Two units give you two GPUs, 504GB of HBM3e, 1,496GB of total coherent memory across two separate pools, and up to 40 petaFLOPS of sparse FP4. How you spend that depends on which of four deployment patterns fits your workload.

Split Agentic AI Workloads Across Two Systems

Unit A dedicates its 252GB of HBM3e to the orchestrator model and its KV cache—the stored attention state that grows with context. Unit B runs the execution tier, with tool models isolated in separate MIG partitions when appropriate. Plans and results move between the systems as lightweight text traffic.

• Best for: Agentic AI pipelines that use a large reasoning model alongside coding, retrieval, guardrail, vision, or speech models.
• Main advantage: Each model remains in fast local HBM, while the 800Gb/s connection carries only prompts and responses rather than per-token activations.
• Tradeoff: Workloads must be assigned and routed across two separate systems.

Shard a Large AI Model Across Two GPUs

The two systems provide 504GB of aggregate HBM3e. For example, a 550-billion-parameter model at 4-bit precision requires roughly 275GB for weights alone. NVIDIA's dual-system vLLM workflow targets this type of deployment with the 550B-parameter Nemotron 3 Ultra mixture-of-experts model.

• Best for: A reasoning or orchestrator model whose weights, and KV cache exceed one system's 252GB of HBM3e.
• Main advantage: Sharding provides enough fast-memory capacity to run models that cannot fit entirely within one GPU's HBM.
• Tradeoff: Activations cross the RoCE link on every token. Pipeline parallelism, which assigns each GPU a contiguous group of layers, is generally better suited to this connection than tensor parallelism. Using both systems for one model also eliminates node-level redundancy.

Configure High Availability and Redundancy

Mirror the complete agent stack on both systems and place a load balancer in front. An active-active configuration distributes normal traffic across both nodes, while active-standby keeps one system ready as a warm spare.

• Best for: Always-on agent services such as internal copilots, ticket-triage agents, and monitoring systems.
• Main advantage: Agent services can remain available during maintenance, driver updates, or a hardware failure on one node.
• Tradeoff: Each model must fit on a single system, and duplicated capacity cannot be combined to host a larger model.

Run Parallel Long-Context AI Sessions

Sessions are routed to either system instead of sharing one GPU. This distributes the growing KV-cache demand created by retrieved documents, tool results, and accumulated context.

• Best for: Teams running multiple independent agents with large contexts, extensive tool output, or long execution histories.
• Main advantage: Two systems double the available HBM for independent KV caches, reducing queues and supporting more simultaneous long-running sessions.
• Tradeoff: Memory remains divided between two separate pools, so one session cannot automatically use the combined capacity without model sharding.

Where 2x GB300 Workstations Still Falls Short of NVIDIA HGX and Rack Clusters

Two linked units extend what a desk can do, but they remain two separate computers joined by Ethernet. The limits show up in four places:

• Interconnect bandwidth: ~100GB/s between units versus 1.8TB/s of NVLink per GPU inside an HGX B300, an 18x gap that rules out efficient tensor parallelism across the pair.
• Scale ceiling: Two units is the end of the line. An HGX node plugs into InfiniBand or Spectrum-X Ethernet fabrics that scale to many nodes.
• Training: Fine-tuning a model that fits on one unit works well. Training anything that has to span both is bound by the link.
• Operations: Two deskside systems are managed individually. HGX servers slot directly into existing cluster schedulers, monitoring, and shared storage

Frequently Asked Questions

Do I need a network switch to connect two units?

No. Two QSFP cables connect the units directly, one per 400Gb/s rail.

Can I connect more than two units?

NVIDIA documents clustering two systems and doesn't publish a configuration beyond that. Teams that need more nodes should look at rack-scale HGX infrastructure.

Do the two units behave like one large GPU?

No. They operate as two nodes over RoCEv2, and frameworks such as vLLM with Ray, or NCCL-based training, handle distributing work between them.

Can one unit really run a 1-trillion-parameter model?

NVIDIA rates it for models up to 1 trillion parameters within the 748GB coherent pool. Anything beyond the 252GB of HBM3e runs from slower LPDDR5X, so expect lower tokens per second than a model that fits entirely in GPU memory.

Which pattern should I start with?

Split roles. It keeps every model in fast local memory, puts almost no load on the link, and still leaves the option to reconfigure for sharding or high availability later.

Conclusion

Two linked units make the most sense when an agent system has outgrown one GPU's fast memory but doesn't justify a rack. Size your orchestrator model and KV cache against 252GB of HBM3e first. If it fits, the second unit is best spent on tool models or redundancy. If it doesn't, sharding across the pair buys headroom at the cost of per-token speed, and a workload that needs more than that is the signal to plan for an HGX B300 deployment.

Scaling Agentic AI at the Desk with Exxact Valence

The Exxact Valence with GB300 is available at Exxact for teams running agentic AI locally and can configure a matched pair with the cabling needed for a two-node setup. For workloads that have outgrown deskside hardware, Exxact also builds HGX B300 servers and full rack deployments.