NVIDIA · Blackwell · Layer 2
NVIDIA RTX PRO 6000 Blackwell
Local and on-premise AI. The honest way into this layer for anyone who is not a hyperscaler — you can actually buy one.
buy You can purchase this outright.
Specifications
- Memory
- 96 GB GDDR7
- Bandwidth
- 1.8 TB/s
- Compute
- Blackwell-generation FP4/FP8
- Power
- 600 W
- Interconnect
- PCIe
- Software
- CUDA
- Form factor
- workstation
- Workload
- both
Verified 2026-09-09. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.
The catch
GDDR7, not HBM, so bandwidth is a fraction of a datacentre part. Fine for local inference and fine-tuning; not a training cluster.
The economics
96 GB in a workstation runs models that used to need a rented multi-GPU node. For sustained local work it repays quickly against GPU-hour rental.
Cost per million tokens is the metric that decides most real purchases, and there is no neutral benchmark for it — every published figure comes from a company selling one side of the comparison. Read all of them, ours included, as claims rather than measurements.
The power question underneath this
A rack of frontier accelerators draws 120–200 kW against a 2026 average of about 27 kW, and the US grid interconnection queue exceeds 2,600 GW with waits approaching five years. Whether you can energise this part is now a harder question than whether you can buy it.
Layer 1 — Energy · The interconnection queue · Rack power density · Tokens per watt
Where you actually rent this
Choosing the part is the easy half. The same accelerator rents for wildly different money depending only on who you rent it from — the cheapest and dearest published on-demand price for an H100 sit 5.6× apart — and the provider, not the chip, is what decides whether you can leave later.
Compared against
- NVIDIA RTX PRO 6000 Blackwell vs NVIDIA DGX Spark — Both buyable, both local. Bandwidth against unified memory capacity.Compare with NVIDIA DGX Spark
- Tenstorrent Blackhole vs NVIDIA RTX PRO 6000 Blackwell — The only fully open stack against the one everything is written for.Compare with Tenstorrent Blackhole
Related silicon
- NVIDIA B200 — 192 GB HBM3E, rent
- NVIDIA DGX Spark — 128 GB unified LPDDR5X, buy
- NVIDIA GB200 NVL72 — 13.5 TB HBM3E across the rack, rent
- NVIDIA GB300 NVL72 — Higher HBM3E capacity than GB200; per-rack figure not consistently published, rent
- NVIDIA H100 — 80 GB HBM3, rent
- NVIDIA H200 — 141 GB HBM3E, rent
Want this whole layer as one table?
The accelerators on these pages as a single CSV — memory, bandwidth, compute, power, interconnect, software stack, availability, who each part is for and the catch — ready to sort, filter or load into a notebook.
Get the silicon table — free for research and evaluation
Four short lines and the download starts at once (CC-BY-NC-4.0, built from these pages the moment you ask). What you type is used only to answer the request and is never sold. Prefer Hugging Face? Request it there.
Using it inside a product, a client report or anything you sell? That needs a commercial licence.