NVIDIA GB300
0
CUDA Cores
288GB
VRAM
5000
GB/s
Technical Specifications
0
CUDA Cores
2500
Base MHz
2800
Boost MHz
288GB HBM3e
8192-bit bus
Performance
40
FP32 TFLOPS
80
FP16 TFLOPS
1200W
TDP
Cloud Availability
1
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | Blackwell (Unknown) |
| Release Date | 2025-12-01 |
| Launch Price | $50,000.00 |
| Process | 4nm |
| Transistors | 100+ Billion |
AI Features
Gen 3
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Additional Specifications
module
Form Factor
About GB300 GPU
The NVIDIA GB300 was introduced in 2025 on the Blackwell architecture. Its specification combines its vendor-specific compute units with 288GB of HBM3e, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 288GB memory capacity, the GB300 is best evaluated for large-model training, high-batch inference, and memory-intensive scientific computing. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec does not currently have an active hourly cloud listing for the GB300. This is not a zero-dollar offer: it means no comparable provider price is available in the present dataset. Check the provider directory for availability or compare a related GPU with an active listing.
For software planning, the GB300 combines 5,000 GB/s of memory bandwidth, CUDA compute capability the latest, and a 4nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
External Resources
Learn more about GPUs from these authoritative sources:
Official CUDA programming guide
NVIDIA GPU Specifications →Official NVIDIA GPU specs
TechPowerUp GPU Database →Comprehensive GPU specifications
CUDA Compute Capability Guide →GPU compute capability reference
What You Need to Know About the GB300
Complete Specifications for the NVIDIA GB300
The NVIDIA GB300 uses the Blackwell architecture and combines vendor-specific compute units with 288GB of HBM3e. GPUvec records 5,000 GB/s memory bandwidth alongside architecture, release date, process node, accelerator features, and physical specifications so the page can be evaluated from source data instead of model-name assumptions.
Compare NVIDIA GB300 Cloud Rental Prices per Hour
GPUvec currently has no comparable active hourly listing for this model; this means pricing is unavailable, not free. Provider rates can vary by region, host configuration, billing model, and availability. Use the provider directory and related-GPU links to compare an available alternative when this exact accelerator is not listed.
Is the NVIDIA GB300 the Right GPU for Your AI Workload?
Start with the 288GB memory limit, then evaluate 5,000 GB/s memory bandwidth, compute features, and software compatibility for your workload. Model weights, KV cache, activations, optimizer state, and framework overhead all consume memory. Compare this page with related accelerators rather than assuming that a higher core count alone guarantees a better training or inference result.
Top GPUs for Training and Inference
| Category | Rank 1 | Rank 2 | Rank 3 |
|---|---|---|---|
| Best for Training | NVIDIA H200 | NVIDIA H100 | NVIDIA B200 |
| Best for Inference | NVIDIA A40 | NVIDIA A100 | NVIDIA A10 |
Compare GPU specifications and cloud instances to find the best GPU for your workload.