Can NVIDIA GB300 Replace Your Company's Multi-GPU AI Server?

For some workloads, yes. For others, not without losing capacity you're using today. A single NVIDIA GB300 system such as a DGX Station has one Blackwell Ultra GPU with 252 GB of HBM3e, plus 496 GB of CPU memory it can address over NVLink-C2C. An existing multi-GPU server, whether it holds four or eight RTX PRO 6000 cards or is an 8-GPU HGX H100, has more GPUs, often more total GPU memory, and redundancy that a deskside system doesn't have. Whether a GB300 can take over comes down to how big your largest model is, how many people use it at once, whether your jobs span several GPUs, and what your facilities and uptime requirements are. The checklist below works through each one, so you can decide before you retire hardware.

How does a GB300 compare with the server you have now?

Source: NVIDIA NVLink page (NVLink 4 at 900 GB/s for Hopper), NVIDIA DGX Station documentation, RTX PRO 6000 Blackwell listing (96 GB). Server configurations vary by vendor; check your own system's specification.

We compared a DGX Station with a new 8-GPU data-centre server in DGX Station vs traditional GPU servers. This post is for teams that already own a multi-GPU server and want to know whether to replace or supplement it.

Step 1: Does your largest model fit?

List every model the server runs, with its precision and the KV cache it needs at your usual context length. Then sort them into three groups:

• Fits in 252 GB of HBM3e: runs at full GPU memory speed on GB300. Most 70B to 120B-class models at 8-bit or 4-bit fall here, and many need no splitting at all, unlike on 96 GB or 80 GB GPUs.
• Fits in about 748 GB, but not 252 GB: runs with part of the model in LPDDR5X at 396 GB/s, about 18 times slower than HBM. Llama 3.1 405B at 8-bit (about 405 GB of weights) is an example. An 8x H100 server holds it entirely in HBM, so expect GB300 to be slower for that model.
• Bigger than 748 GB: doesn't fit on one GB300 at that precision. Keep it on the server or move to a rack system.

Our post on trillion-parameter models on 748 GB has the sizing arithmetic.

Step 2: How many users and requests does it serve?

A server with four or eight GPUs can run several model copies side by side. One GB300 has one GPU, though a large one. The only like-for-like figure we found is StorageReview's gpt-oss-120b test at 128 concurrent requests: a GB300 (ASUS ET900N G3) produced 9,280 output tokens per second against 3,064 for a single RTX PRO 6000. On that workload, one GB300 did about the work of three such cards, if the cards scaled perfectly, which wasn't tested. On that basis, one GB300 might cover a lightly loaded 4-card server, but not a busy 8-card one. Measure your server's real peak concurrency and tokens per second before deciding.

Step 3: Do your jobs span several GPUs?

If your training or inference jobs use tensor or pipeline parallelism across the server's GPUs today, they'll need reconfiguring for a single GPU. Often that's a simplification: a model split four ways across 96 GB cards may fit on one 252 GB GPU. But a job that genuinely needs eight GPUs of compute, such as pre-training or large fine-tuning runs, won't fit on one. Linking two GB300 systems over ConnectX-8 gives 2x 400 Gb/s, or 100 GB/s each way, which is far below the 900 GB/s per GPU an HGX H100 gets over NVLink. That's why multi-node work on stations suits data parallelism better than splitting a model. See GB300 vs traditional RTX workstations for how PCIe and NVLink splitting compare.

Step 4: How do users share it?

On a multi-GPU server, the simplest way to share is one GPU per team. On GB300, MIG can split the GPU into as many as seven isolated instances, each with its own memory slice, or the whole GPU can be shared through a scheduler. Our posts on sharing one DGX Station between seven teams and shared remote access cover the options.

Don't ask whether a GB300 is as powerful as your server. Ask which of your server's jobs it can carry, and what happens to the rest.

Step 5: Will your software run on Arm?

Your existing server runs an x86 host. GB300 runs a 72-core Arm Grace CPU with Ubuntu and NVIDIA's AI developer tools. NGC containers are multi-architecture and current PyTorch builds support it, but check every dependency: custom CUDA extensions, Python wheels, monitoring agents, licence servers and any x86-only applications. Note NVIDIA's guidance that some software built for 4 KB memory pages isn't supported on the 64 KB-page kernel. See why your software might struggle on 72 Arm cores.

Step 6: Can you live without server-grade redundancy?

A rack server usually sits behind redundant power, a UPS and data-centre cooling, and its GPUs can fail one at a time. A GB300 workstation is one system with one GPU. If production services depend on the server today, plan failover before moving them. Our guide Your DGX Station could be a single point of failure sets out the options, and an APC Smart-UPS SRT is a sensible minimum for the desk.

Step 7: What does the move need from facilities and the network?

• Power: 1,600 W maximum. Have a licensed electrician check the circuit; see our power, heat and noise guide.
• Data: if datasets live on server-attached storage, plan how the GB300 reaches them. Its 10GbE port suits light use; its two 400 Gb/s ports suit fast storage or a second station. See DGX Station's networking.
• Management: both have a BMC for remote power control. Add the GB300 to your monitoring, patching and backup routines, as covered in who manages the system.

Step 8: Replace, supplement, or scale up?

Many teams end up with a split. Development, evaluation and private inference move to GB300 workstations, freeing the existing server for production serving or the largest jobs. If the server is due for replacement and the workload is growing, the next step up is a newer NVLink server or a rack-scale system rather than several workstations. Our posts on GB300 NVL72 and scaling from DGX Station to NVL72 explain that path.

Buying in Singapore

SourceIT lists six GB300 systems, all price on request: the HP ZGX Fury G1n, MSI XpertStation WS300, ASUS ExpertCenter Pro ET900N G3, GIGABYTE W775-V10-L01, Supermicro ARS-511GD-NB-LCC and Dell Pro Max with GB300. If you're keeping or expanding a server, we also list GPU servers such as the Lenovo ThinkSystem SR680a V3, SR780a V3, SR685a V3, HPE ProLiant DL380a Gen11 and DL385 Gen11, also price on request, plus a UPS such as the APC Smart-UPS SRT 6kVA (S$7,490, listed 6 Nov 2026) here. Browse our AI workstations and servers. Send us your current server's model list and peak load and we can help map it. Use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.

Frequently asked questions

Can one GB300 replace a 4-GPU RTX PRO 6000 server? For models that fit in 252 GB and moderate concurrency, it may. On one published test, a GB300 produced about three times the output of a single RTX PRO 6000, but a busy four-card server may still need more than one GB300.

Can a GB300 replace an 8-GPU HGX H100 server? Usually not one for one. The H100 server has 640 GB of HBM3 across eight NVLink-connected GPUs. A GB300 suits moving development and smaller inference off that server rather than replacing it entirely.

Do I need to change my code to move from a multi-GPU server? Jobs that split models across GPUs need their parallelism settings changed, and x86-only dependencies need Arm64 versions. Containerised PyTorch workloads usually move with little change.

Can I link GB300 systems to match my server? You can link them over ConnectX-8 at 2x 400 Gb/s, but there is no NVLink between systems. That suits data-parallel work and separate jobs, not splitting one model across machines.

Should I keep my existing server? Often, yes. Many teams move development and private inference to GB300 workstations and keep the server for production or the largest models.