Specifications Compared
| Spec | GB300 | RTX-4080 |
|---|---|---|
| TDP | 1400W | 320W |
| VRAM | 288 GB | 16 GB |
| Memory Type | HBM3e | GDDR6X |
| Architecture | Blackwell Ultra | Ada Lovelace |
| Form Factors | SXM | PCIe |
| Interconnect | NVSwitch, NVLink | |
| FP8 Performance | 4,500 TFLOPS | |
| FP16 Performance | 2,250 TFLOPS | 48.7 TFLOPS |
| FP32 Performance | 90 TFLOPS | 48.7 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | 780 TOPS |
| Memory Bandwidth | 12,000 GB/s | 717 GB/s |
Performance Analysis
The GB300 SXM6 dominates in compute: its 2250 TFLOPS FP16 performance accelerates AI training by handling models with trillions of parameters, far beyond the RTX 4080's 48.7 TFLOPS which limits it to smaller datasets. FP32 throughput of 90 TFLOPS on the GB300 supports high-precision tasks like scientific simulations, exceeding the RTX 4080's 48.7 TFLOPS for faster convergence in training loops. FP8 at 4500 TFLOPS on the GB300 optimizes inference for quantized models, unavailable on the RTX 4080. Memory capacity defines real-world impact: 288 GB HBM3e on the GB300 loads entire large language models without sharding, while 16 GB GDDR6X on the RTX 4080 requires model parallelism for datasets over 10 GB. Bandwidth of 12000 GB/s versus 717 GB/s allows the GB300 to process batch sizes up to 20 times larger, reducing training time from days to hours in distributed setups. The GB300's NVLink interconnect scales across nodes seamlessly, contrasting the RTX 4080's lack of such features.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4080
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4080 SUPER 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr | |||
RunPod | NVIDIA GeForce RTX 4080 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the GB300 SXM6
The GB300 SXM6 suits enterprise AI training: its 288 GB VRAM and 2250 TFLOPS FP16 enable full-model loading for LLMs exceeding 100 billion parameters. High bandwidth of 12000 GB/s supports massive batch sizes in multi-GPU clusters via NVSwitch. It excels in datacenter environments demanding 1400W TDP for sustained peak performance.
When to Choose the RTX 4080
The RTX 4080 fits budget-conscious users: cloud pricing from $0.11 per hour makes it ideal for prototyping and inference on models under 10 GB. Its 320W TDP and PCIe form factor integrate easily into workstations without specialized cooling. 48.7 TFLOPS FP16 handles fine-tuning and creative tasks efficiently at low cost.
Use Cases
The GB300's 288 GB VRAM and 2250 TFLOPS FP16 load massive models without sharding. This enables faster training iterations compared to the RTX 4080's 16 GB limit.
4500 TFLOPS FP8 and 12000 GB/s bandwidth on the GB300 support high-throughput serving of large LLMs. The RTX 4080's 717 GB/s bandwidth restricts batch sizes for production inference.
90 TFLOPS FP32 on the GB300 accelerates precise parameter updates on datasets over 50 GB. RTX 4080's 48.7 TFLOPS suits only smaller models.
RTX 4080's 48.7 TFLOPS FP16 generates images quickly at $0.11 per hour. Its 16 GB VRAM handles typical diffusion models without excess capacity.
GB300's 90 TFLOPS FP32 outperforms RTX 4080's 48.7 TFLOPS for simulations. 288 GB VRAM processes large matrices in fields like physics.
Frequently Asked Questions
What is the VRAM difference between GB300 SXM6 and RTX 4080?▾
The GB300 SXM6 has 288 GB HBM3e VRAM, while the RTX 4080 provides 16 GB GDDR6X. This 18-fold increase allows the GB300 to manage models up to 200 GB in memory. Bandwidth reaches 12000 GB/s on GB300 versus 717 GB/s on RTX 4080.
How do FP16 performance levels compare?▾
GB300 SXM6 delivers 2250 TFLOPS FP16, dwarfing the RTX 4080's 48.7 TFLOPS by over 46 times. This gap accelerates deep learning training significantly. Inference benefits from GB300's FP8 at 4500 TFLOPS.
What are the power requirements?▾
The GB300 SXM6 consumes 1400W TDP in SXM form factor for datacenter use. RTX 4080 requires 320W in PCIe slots, suiting desktops. GB300 needs advanced cooling for sustained loads.
Is the RTX 4080 available on cloud platforms?▾
RTX 4080 offers start at $0.11 per hour, averaging $0.26 per hour across five providers. GB300 SXM6 has no live cloud offers yet. This makes RTX 4080 ideal for immediate access.
Which GPU supports multi-GPU interconnects?▾
GB300 SXM6 uses NVSwitch and NVLink for cluster scaling. RTX 4080 lacks dedicated interconnects, relying on PCIe. This enables GB300 for distributed training across dozens of GPUs.
When were these GPUs released?▾
GB300 SXM6 launches in 2025 with Blackwell Ultra architecture. RTX 4080 debuted in 2022 on Ada Lovelace. The generational gap emphasizes GB300's advancements in AI compute.
Which is cheaper to rent, the GB300 or the RTX 4080?▾
Cloud rental prices for both the GB300 and RTX 4080 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the GB300 have compared to the RTX 4080?▾
The GB300 has 288 GB of HBM3e memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find GB300 and RTX 4080 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the GB300 and the RTX 4080?▾
The GB300 uses the Blackwell Ultra architecture (2025) while the RTX 4080 uses Ada Lovelace (2022). The GB300 delivers 46.2x the FP16 throughput and 16.7x the memory bandwidth of the RTX 4080.