Exxact Valence with GB300 vs. 4x RTX PRO 6000 for Agentic AI: One Big Model or Many at Once

Agentic AI workloads rarely fail because a team picked the wrong GPU. They fail because a team picked the wrong shape of hardware for the workload. A single agent orchestrating a 200 billion-parameter model with long context needs a very different memory layout than a team running a dozen smaller agents in parallel, and the two most common local-AI answers today, the Exxact Valence with GB300 and a tower carrying four NVIDIA RTX PRO 6000 Blackwell GPUs, are optimized for exactly those two different shapes. Neither wins outright. The right pick depends on whether the workload goal is one large model or several independent ones running at the same time.

What Is the Exxact Valence with GB300?

The Exxact Valence with GB300 is built on NVIDIA's DGX™ Station architecture, centered on the GB300 Grace Blackwell Ultra Superchip: a 72-core Grace CPU and a Blackwell Ultra GPU joined by a 900GB/s NVLink-C2C interconnect. The CPU and GPU share a single coherent memory pool of up to 748GB, made up of 252GB of HBM3e GPU memory and 496GB of LPDDR5X system memory. Common use cases for this class of system include:

• Running very large open-weight LLMs (200B parameters and up) that don't fit in any single GPU's VRAM
• Long-context agent orchestration, where the context window itself consumes significant memory alongside the model weights
• Local fine-tuning of large models without offloading to cloud infrastructure

NVIDIA rates the platform at up to 20 petaflops of FP4 compute with sparsity, and NVFP4, the 4-bit floating point format native to Blackwell, is what makes fitting models of this size into the coherent pool practical at all.

What Is the Exxact Valence 4x Max-Q?

The Exxact Valence 4x Max-Q is our configurable workstation proudly featuring four non-thermally-throttling NVIDIA RTX PRO 6000 Max-Q GPUs each with 96GB of GDDR7 memory and 1.8TB/s of memory bandwidth. Four of them in one workstation total 384GB of VRAM, but that memory is not unified. Each card holds its own allocation, and any workload that needs to reach across cards does so over PCIe rather than a single coherent address space. Common use cases for a 4-card configuration include:

• Running several independent agents or model instances in parallel, each pinned to its own GPU
• Serving multiple smaller-to-mid-size models (up to roughly 80B parameters per instance) simultaneously without one workload waiting on another
• Mixed workloads, since each card can also handle rendering, simulation, or other GPU-accelerated tasks when it isn't running an agent

Exxact Valence with GB300 vs. Exxact Valence 4x Max-Q: Spec Comparison

Overhead, Coherence, and Splitting Models Across Cards

The two systems take very different approaches to running models that exceed a single GPU's memory capacity.

• Model splitting: Running a model too large for one RTX PRO 6000's 96GB across all four cards typically requires tensor or pipeline parallelism, with the model divided across separate GPUs.
• Communication overhead: Every layer boundary or tensor shard that crosses a GPU becomes a network hop over PCIe, managed in software by frameworks like NCCL. These transfers add latency and require additional engineering, and the four cards never appear as one unified memory space.
• Memory coherence: The Exxact Valence with GB300 combines CPU and GPU memory in one hardware-coherent address space. Models requiring more than 96GB of working memory do not need to be manually partitioned across devices, making very large models and long-context workloads easier to run.

Cost, Power, and Upgrade Path

The two systems also diverge on how they get bought and how they grow.

• Configuration: The Exxact Valence with GB300 ships as a single, fixed-configuration SKU. A 4x RTX PRO 6000 build is assembled from discrete components, which gives more control over CPU, storage, and chassis choice but puts the integration burden on the buyer or system builder.
• Power and cooling: NVIDIA rates the DGX Station platform at up to 1,600W. Four RTX PRO 6000 Workstation Edition cards at up to 600W each can draw up to 2,400W before the rest of the system is counted, which pushes multi-GPU towers toward higher-capacity PSUs and more deliberate airflow planning.
• Upgrade path: A multi-GPU tower can be upgraded incrementally, adding cards, swapping GPUs, or reallocating a card to a different workload. The Exxact Valence with GB300's coherent memory pool is fixed at purchase; scaling further means moving to a larger DGX platform rather than adding a component.

Use Case Flexibility

The two systems also differ in how broadly they can support workloads beyond agentic AI.

• Professional GPU versatility: An RTX PRO 6000 can run an agent today and support rendering, CFD simulation, CAD, or other GPU-accelerated workloads tomorrow.
• Workload allocation: A 4-card tower can assign individual GPUs to different roles, allowing teams to divide capacity among AI, visualization, and simulation as demand shifts.
• AI specialization: The Exxact Valence with GB300 is purpose-built for large-model training and inference. Its coherent memory architecture makes it stronger for that specialized role, but less suited to repurposing for unrelated GPU work.

Exxact Valence with GB300 vs Exxact Valence 4x Max-Q Head to Head

Frequently Asked Questions

Can the Exxact Valence with GB300 run multiple models at once, like the RTX PRO 6000 setup can?

It can host multiple models within its unified memory, but it doesn't isolate them onto separate physical GPUs the way four discrete cards do, so true hardware-level parallelism across independent workloads favors the multi-GPU build.

Is 4x RTX PRO 6000 cheaper than the Exxact Valence with GB300?

It depends on configuration, but a multi-GPU tower generally offers more control over cost by letting buyers choose CPU, storage, and card count independently, rather than buying a fixed platform.

Which system is better for fine-tuning?

The Exxact Valence with GB300's larger unified pool and coherent memory make it better suited to fine-tuning models that don't fit in 96GB. Smaller models in the 30-70B range can be fine-tuned effectively on a single RTX PRO 6000 card.

Can I mix the two approaches?

Yes. Some teams run day-to-day agent serving on a multi-GPU RTX PRO 6000 tower and reserve an Exxact Valence with GB300 for the largest models or heaviest fine-tuning runs.

Conclusion

The decision between an Exxact Valence with GB300 and a 4x RTX PRO 6000 build comes down to workload shape, not raw specs. If the goal is a single very large model or a long-context agent orchestrator, the coherent memory pool on the Exxact Valence with GB300 removes a real engineering problem. If the goal is running many independent agents or models in parallel, or keeping the hardware flexible for non-AI GPU work, four discrete cards give more parallelism and more day-to-day versatility.

Exxact offers both our Exxact Valence with GB300 workstation as well as the Exxact Valence 4x Max-Q workstation as configurable systems. Contact Us for more information!