NVIDIA B300 Specs: 288GB HBM3e Memory, Power Consumption & Pricing

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The NVIDIA B300 is widely expected to represent the next evolution of the Blackwell architecture, often referred to in industry roadmaps as Blackwell Ultra.

While NVIDIA officially introduced the Blackwell generation (B100, B200, and GB200 systems), the B300 generation is expected to be the next refresh optimized for larger AI models and more efficient inference workloads.

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NVIDIA B300 GPU server Blackwell architecture, designed for large AI training clusters, LLM inference, and high-performance computing (HPC) workloads ienterprise and hyperscale data centers. server-parts.eu refurbished

Blackwell GPUs are designed for massive AI infrastructure, including:

hyperscale AI clusters
large language model (LLM) training
AI inference platforms
high-performance computing (HPC)

Compared to earlier GPUs, the B300 generation is expected to increase memory capacity, AI efficiency, and cluster scalability.

What is the NVIDIA B300?

The NVIDIA B300 is a data center GPU designed for large-scale AI workloads. It is expected to be deployed mainly inside HGX platforms, DGX systems, and Grace-Blackwell architectures used in hyperscale AI clusters.

Unlike traditional PCIe accelerators, the B300 will likely be primarily deployed as an SXM module connected through NVIDIA’s high-speed GPU interconnect technologies.

These systems allow multiple GPUs to behave almost like a single large GPU with a unified high-bandwidth memory pool.

NVIDIA B300 Architecture Overview

Blackwell builds on the previous Hopper generation and introduces several major improvements aimed at modern AI workloads.

Key architectural features include:

5th-generation Tensor Cores optimized for FP4 and FP8 AI workloads
Transformer Engine designed for LLMs
HBM3e high-bandwidth memory
NVLink 5 GPU interconnect
NVSwitch GPU fabric for multi-GPU scaling

One of the most important innovations is FP4 precision, which allows large AI models to run more efficiently by reducing memory and compute requirements.

NVIDIA B300: NVLink 5 and GPU interconnect

The Blackwell architecture uses NVLink 5, the newest generation of NVIDIA’s GPU interconnect.

Key characteristics:

up to ~1.8 TB/s GPU-to-GPU bandwidth
extremely low latency between GPUs
high-speed memory sharing across accelerators

In large systems, GPUs are connected using NVSwitch chips, which create a fully connected GPU fabric.

This allows AI systems to scale from:

8 GPUs per server
to dozens of GPUs per rack
to hundreds or even thousands of GPUs in large AI clusters.

Large Blackwell deployments can scale across multi-rack GPU clusters connected with NVLink and InfiniBand networking.

NVIDIA B300 Specifications

While final specifications may vary depending on system implementations, the following values represent widely discussed industry expectations.

Feature	NVIDIA B300
Architecture	Blackwell evolution (often called Blackwell Ultra)
Memory	up to 288 GB HBM3e
Memory bandwidth	~8 TB/s
Tensor cores	5th-generation Tensor Cores
Precision support	FP4, FP8, BF16, FP16
Interconnect	NVLink 5
Form factor	primarily SXM module
Typical power	~1000 W
Peak power estimate	up to ~1400 W
Main workloads	AI training, inference, HPC

The large 288 GB HBM3e memory capacity enables very large AI models to run on fewer GPUs.

NVIDIA B300 vs B200 Comparison

The B300 generation builds on the earlier Blackwell B200 GPU.

Feature	NVIDIA B200	NVIDIA B300
Architecture	Blackwell	Blackwell evolution
Memory	192 GB HBM3e	288 GB HBM3e
AI workloads	training	training + inference
Efficiency	high	improved for larger models

Roadmaps suggest performance improvements roughly in the 1.3–1.5× range depending on workload, although NVIDIA has not published exact official figures.

Grace-Blackwell systems (NVIDIA GB300)

Many deployments using the B300 generation will likely appear in Grace-Blackwell systems.

These platforms combine:

NVIDIA Grace CPU
Blackwell GPU
NVLink-C2C connection

Example simplified architecture:

Grace CPU   │NVLink-C2C   │B300 GPU

This design enables extremely fast CPU-GPU communication and is expected to power future DGX and hyperscale AI systems.

NVIDIA B300 Servers and Platforms

The B300 will primarily appear in large AI infrastructure platforms.

HGX systems

Typical configuration:

8 × B300 GPUs
NVLink GPU connections
NVSwitch GPU fabric
PCIe Gen5 CPU platform

DGX systems

DGX servers are NVIDIA’s integrated AI systems.

Typical configuration:

Component	Example
GPUs	8× NVIDIA B300
GPU memory	~2.3 TB
CPU	Grace CPU or x86
Networking	400–800 Gb/s
Storage	NVMe Gen5

Because each GPU contains 288 GB memory, an 8-GPU system provides roughly 2.3 TB total GPU memory.

NVIDIA B300 Power consumption

Modern AI GPUs require significant power.

Estimated power usage:

NVIDIA GPU	Typical power
NVIDIA A100	~400 W
NVIDIA H100	~700 W
NVIDIA B200	~1000 W
NVIDIA B300	potentially higher

For the B300 generation:

~1000 W typical
up to ~1400 W peak

Because of this, many systems will rely on liquid cooling infrastructure.

NVIDIA B300 Pricing

NVIDIA does not publish official GPU pricing, but industry estimates suggest:

Component	Estimated price
NVIDIA B300 GPU module	~$30,000 – $40,000
NVIDIA HGX B300 server	~$300,000 – $500,000
NVIDIA DGX B300 system	$400,000+

Actual pricing varies depending on:

networking hardware
storage configuration
support contracts
purchase volume.

Large AI clusters can easily cost tens or hundreds of millions of dollars.

NVIDIA B300 Data Center GPU Comparison

Below is a simplified comparison of recent NVIDIA AI GPUs.

NVIDIA GPU	Architecture	Form Factor	Memory	Typical Power
A100 PCIe	Ampere	PCIe	40–80 GB HBM2	~250–300 W
A100 SXM	Ampere	SXM	80 GB HBM2	~400 W
H100 PCIe	Hopper	PCIe	80 GB HBM3	~350 W
H100 SXM	Hopper	SXM	80 GB HBM3	~700 W
H200	Hopper	SXM	141 GB HBM3e	~700 W
B100	Blackwell	SXM	~192 GB HBM3e	~1000 W
B200	Blackwell	SXM	192 GB HBM3e	~1000 W
B300	Blackwell evolution	SXM	up to 288 GB HBM3e	~1000–1400 W

This progression highlights how NVIDIA GPUs are rapidly increasing memory capacity, compute performance, and power consumption to support modern AI workloads.

NVIDIA B300 Expected Release Timeline

Current industry roadmaps suggest the following timeline.

Stage	Timeline
NVIDIA Blackwell architecture announcement	2024
NVIDIA B200 / GB200 systems shipping	2025
NVIDIA Blackwell refresh (B300 generation)	2026

Early deployments will likely appear in:

hyperscale cloud providers
government AI programs
large enterprise AI clusters
advanced research labs.

NVIDIA B300 FAQ

What is the NVIDIA B300?

The NVIDIA B300 is a data-center GPU expected to represent the next evolution of the Blackwell architecture, designed for large AI workloads such as training, inference, and reasoning models.

What is the difference between NVIDIA B300 and B200?

The B300 is expected to expand the Blackwell platform with larger memory capacity (up to 288 GB HBM3e) and improved efficiency for large AI workloads.

How much memory does the NVIDIA B300 have?

The NVIDIA B300 is expected to include up to 288 GB of HBM3e memory, enabling very large AI models to run on fewer GPUs.

What is the difference between NVIDIA H200 and NVIDIA B300?

The H200 uses the Hopper architecture, while the B300 represents a newer evolution of the Blackwell architecture with larger memory capacity and newer Tensor Cores.

What is the difference between NVIDIA B300 and NVIDIA GB300?

The B300 refers to the GPU itself. GB300 refers to a Grace-Blackwell platform, where the GPU is paired with NVIDIA’s Grace CPU using NVLink-C2C for high-speed communication.

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