Vera Rubin vs B300

Rubin has about 2.5 times the dense FP4 and the memory bandwidth of a B300, and almost the same memory: 288 GB of HBM4 against 270 GB of HBM3E per GPU. It also needs liquid cooling in NVIDIA's eight-GPU system, about 24 kW against about 14 kW, and a CUDA release that reached general availability in November 2026. NVIDIA marks HGX B300 servers as shipping now, and for Rubin it lists the Vera Rubin NVL72 rack as available. The decision is therefore less about the chip than about your room, your software and your date. QuantaCloud builds both to order as reserved capacity, and neither is sold by the hour.

Rubin vs B300 specs#

Memory is the one row that barely moves. These are NVIDIA's figures: the Vera Rubin datasheet (October 2026), the Blackwell Ultra datasheet (October 2025) and the HGX, Vera Rubin NVL72 and DGX pages as read on 2026-11-12. NVIDIA publishes Blackwell's FP8, BF16 and INT8 figures with sparsity and Rubin's dense, so the table halves the Blackwell ones to compare dense with dense.

The Rubin column uses the per-GPU figures on NVIDIA's Vera Rubin NVL72 page. An eight-GPU server is the fairer match for a B300, and there the memory gap disappears:

Memory: 288 GB against 270 GB#

Memory does not decide this comparison. On NVIDIA's tables a Rubin GPU has 18 GB more than a B300, and NVIDIA lists 2 TB for the eight GPUs of an HGX Rubin NVL8 against 2.1 TB for an HGX B300. The per-GPU figures may not even be counted the same way: NVIDIA's MIG guide calls the B300 a 288 GB product too, with a largest profile of 269 GB. A model that needs a second B300 server needs a second Rubin server too. The B300 page has the fit table: at vLLM's default setting the engine gets 247.1 GiB of a B300 (our calculation from the 275,040 MiB it reports). I have not found a measured figure for a Rubin GPU, so I do not quote a budget for one.

What Rubin changes is how fast that memory is read: 19.2 TB/s per GPU against 7.7. NVIDIA's own engineers describe the decode phase of inference, where tokens are generated one at a time, as bound by the memory subsystem. That makes bandwidth, more than capacity, the reason to pick Rubin for serving.

Compute: 2.5 times the dense FP4#

Rubin's figures are higher in every floating-point format, and FP8 gains more than FP4. Dense FP4 goes from 14 to 35 PFLOPS per GPU and dense FP8 from 4.5 to 17.5. NVIDIA's headline 50 PFLOPS is a sparse figure that depends on the adaptive compression in Rubin's Transformer Engine, and the like-for-like B300 figure is 18. Rubin also restores what Blackwell Ultra gave up: 33 TFLOPS of FP64 per GPU against 1.2.

NVIDIA's own performance claims are against the older B200. It projects 10 times the token throughput of an HGX B200 for an HGX Rubin NVL8, and the same pretraining with a quarter of the GPUs. Both carry the note that projected performance is subject to change, the inference one compares sparse FP4 on Rubin with dense FP4 on the B200, and the pages I read give no projection against the B300. These are specifications, not measurements, and I have no benchmark of the two on one workload to show you.

Power, cooling and the room#

The room is where the two differ most. NVIDIA's DGX B300 is air-cooled, fills 10 rack units and draws about 14 kW. Its DGX Rubin NVL8 is liquid-cooled, fills 2 rack units and draws about 24 kW, and NVIDIA's HGX specification gives a 45°C inlet temperature. A Rubin server needs a coolant loop where a B300 server needs airflow, and in NVIDIA's own systems it puts 1.7 times the power into a fifth of the space.

Per kilowatt, the gain is smaller than per GPU. The DGX Rubin NVL8 delivers 11.25 dense FP4 PFLOPS per kW against 7.71 for the DGX B300, about 1.5 times (our calculation: 270 / 24 and 108 / 14). At rack scale the ratio is similar: a Vera Rubin NVL72 delivers 2,520 dense FP4 PFLOPS for 227 kW at NVIDIA's highest power setting, against 1,080 for up to 142 kW on a GB300 NVL72, about 1.5 times per kW (our calculation: 11.1 against 7.6).

CPU, NVLink and networking#

The platform around the GPU changes less than the names suggest. An HGX B300 is an x86 server. An HGX Rubin NVL8 can be x86 as well, and NVIDIA's DGX Rubin NVL8 keeps the two Xeon 6776P processors of the DGX B300. The other option is NVIDIA's Vera CPU, an Arm processor with 88 cores, which means arm64 builds of everything you run. NVLink moves from the fifth generation at 1.8 TB/s per GPU to the sixth at 3 TB/s. Between servers the speed stays where it was: one 800 Gb/s port per GPU on both DGX systems. The InfiniBand vs NVLink guide explains which traffic uses which link, and the Vera Rubin page covers the Vera CPU and the rack products.

Software: sm_103 against sm_107#

The B300 runs on a stack that has been public since 2025, and Rubin's reached general availability in November 2026. The B300 needs CUDA 12.9 or newer, a release from May 2025, and an R580 driver, first listed in 580.82.07. Rubin is compute capability 10.7. CUDA 13.4 carried it only as a developer preview that NVIDIA said was not intended for production, CUDA 13.5 is the general-availability release, and newly enabled platforms need an R620 driver, whose first data-center release is dated 2026-11-04.

Frameworks follow with a lag. PyTorch 2.14.0 added awareness of sm_107 for extension builds, and vLLM was still publishing its Rubin images as nightly builds in 0.31.0 and pinned a kernel library with initial Rubin support in 0.32.0. The two GPUs share major version 10, and NVIDIA's rule is that a binary runs on the same major version with an equal or higher minor, so plain sm_100 code should carry from a B300 to Rubin. Kernels built for sm_103a do not. The driver and CUDA version guide shows how to confirm what a server is running.

Build on B300 now or wait for Rubin#

The deciding question is what limits you today: the room, the date, the software, or the chip.

Waiting only makes sense when one of the Rubin rows describes you and none of the first three does. Work done on a B300 is not lost if you move later: NVIDIA's datasheet calls Rubin's Transformer Engine fully compatible with Blackwell, and plain sm_100 kernels should run on both, since they share major compute-capability version 10.

Availability and price#

NVIDIA marks HGX B300 as shipping now and lists the DGX B300 and the Vera Rubin NVL72 as available. Its HGX page has no shipping note for HGX Rubin NVL8 yet. QuantaCloud builds either to order, and lead time comes back in writing with each quote, before you commit.

NVIDIA publishes no list price for either. Reuters reported on 2026-04-30, citing two industry sources, that a B300 server with eight GPUs was priced at about $550,000 in the United States, up from around $500,000 late in 2025. For Rubin the only dated figure I have is for the rack: on 2026-03-24 a Tom's Hardware source put a Vera Rubin NVL72 at $5 million to $7 million, against $6 million to $6.5 million for an inference-optimized GB300 NVL72. I have found no dated price for an HGX Rubin NVL8 server. QuantaCloud quotes both in writing. The DGX B300 guide explains how NVIDIA's own system differs from the HGX servers we build, and the B300 vs B200 comparison covers the choice inside Blackwell.

Rubin vs B300 questions#

Should I wait for Rubin?

Wait only if three things are true: bandwidth or compute limits your job and memory does not, you have liquid cooling, and you can run a software stack that became generally available in November 2026. If any of the three fails, build on B300 now.

Does Rubin have more memory than a B300?

Slightly. A Rubin GPU has 288 GB against 270 GB, and NVIDIA lists 2 TB for an eight-GPU Rubin server against 2.1 TB for an HGX B300.

Can I test Rubin code before the hardware arrives?

Only partly. The on-demand RTX PRO 6000 Blackwell runs FP4 checkpoints, but it is compute capability 12.0, so it cannot test sm_103 or sm_107 kernels. It is useful for model and serving setup. Stopping an on-demand instance deletes its disk, so copy results off first.

The rule I follow: build on B300 when the room is air-cooled, the date matters or the stack has to stay put, and order Rubin when memory bandwidth or dense compute is what holds the job back and the site has liquid cooling. If you are not sure, send one brief on the reserved capacity page with the model, precision, context length, GPU count and site, and ask for both configurations. QuantaCloud will quote each in writing with its lead time.