After releasing the spec for their first system that will be able to run 70B models locally, NVIDIA has officially unveiled the RTX PRO 6000 Blackwell Workstation Edition, a high-performance GPU that brings capabilities to professional AI workloads and large-scale model inference. With 96GB of GDDR7 memory, 1.79 TB/s memory bandwidth, and a cooling design reminiscent of the RTX 5090, this is the first single-card workstation GPU capable of fully loading an 8-bit quantized 70B model such as LLaMA 3.3 – and potentially LLaMA 4 – while leaving headroom for extended context sizes.
For AI researchers, ML engineers, and developers working on local LLM inference, this marks a significant milestone. Until now, running these massive models required multiple GPUs or resorting to cloud-based solutions. The RTX PRO 6000 Blackwell changes that, offering a single-GPU solution that balances memory capacity, bandwidth, and compute performance.
Specifications and Performance
| Specification | RTX PRO 6000 Blackwell |
|---|---|
| CUDA Cores | 24,064 |
| Tensor Cores | 752 |
| RT Cores | 188 |
| VRAM | 96GB GDDR7 ECC |
| Memory Bus | 512-bit |
| Memory Bandwidth | 1.79 TB/s |
| TDP | 600W (Workstation Edition) |
| Boost Clock | 2.6 GHz |
| Display Connectivity | DisplayPort 2.1b |
The standout feature here is the 96GB GDDR7 VRAM – triple the capacity of the RTX 5090 while maintaining the same memory bandwidth. This allows users to load and run massive LLMs without model weights offloading and multi GPU setups.
AI and LLM Workloads
With 96GB of VRAM, the RTX PRO 6000 is the first NVIDIA workstation GPU that can fully load an 8-bit quantized 70B LLaMA model (75GB) while still leaving 20GB for context expansion or additional runtime processes.
Previously, running models of this scale locally required at least two A6000 ADA GPUs with NVLink, often introducing synchronization inefficiencies and additional power draw. The RTX PRO 6000 eliminates those constraints, enabling developers to experiment with multi-turn conversations, extended context windows, and real-time inference on a single card.
Community reactions have been mixed – some praising the potential, others questioning whether the price justifies the upgrade over a 5090. As one Reddit user noted, “It’s not that it’s faster, but that now you can fit some huge LLM models in VRAM.” Others remain skeptical, arguing that the limited production volume and expected $10K–$14K price tag put it out of reach for most enthusiasts.
Cooling and Design
NVIDIA’s choice of cooling solution has raised some eyebrows. The Workstation Edition of the RTX PRO 6000 uses a dual-fan flow-through design, nearly identical to the RTX 5090 Founders Edition. This decision makes sense for single-GPU workstation setups but deviates from the traditional blower-style cooling seen in previous Quadro and workstation-class GPUs. A separate blower-cooled variant is available for server environments, targeting multi-GPU racks.
This move sparked debate in online communities, with some users arguing that blower cards are superior for workstation use. One Redditor noted, “I have stacked 5090FEs, and they keep nice and cool – can’t see any advantage with blower here.” Others countered, saying that axial cooling solutions require ample space for proper airflow, while blower-style cards perform better in cramped setups.
Pricing and Market Positioning
Pricing remains speculative, but estimates suggest a range of $10,000–$14,000. Given that the A6000 ADA launched at $6,800 and offered only 48GB of VRAM, NVIDIA’s pricing strategy seems aligned with professional markets rather than enthusiast consumers. This also suggests that NVIDIA is positioning the PRO 6000 as a gap filler between gaming GPUs and data center cards – a premium, high-memory option for LLM researchers who can’t justify the $30K+ cost of an H100.
Several users pointed out that the pricing premium is mostly for memory, with the core specs being similar to the RTX 5090. As one Redditor put it, “Would this just be a $10K 64GB upgrade over a 5090?” While that sentiment is understandable, the reality is that consumer gaming GPUs aren’t designed for sustained, high-memory AI workloads – a crucial distinction for AI researchers running fine-tuned LLMs or high-context chat models.
Conclusion
The NVIDIA RTX PRO 6000 Blackwell Workstation Edition represents a significant step forward for local LLM inference. For researchers and professionals who need to run 70B models with full context sizes, this GPU is the first single-card solution desktop/workstation that makes it possible. However, the high price tag and workstation positioning mean it’s not aimed at mainstream AI enthusiasts.
For those already using multi-GPU setups for LLMs, the PRO 6000 could simplify workflows and reduce system complexity. But for those waiting for a true consumer-grade high-memory GPU, the gap between workstation and gaming cards continues to grow.
NVIDIA has once again demonstrated its dominance in professional AI acceleration – but will the limited availability and high cost deter potential buyers? Only time will tell. One thing is clear: if you need maximum VRAM with, good prompt processing times and high bandwidth, this is the card to beat.