Specifications Compared
| Spec | B300 | RTX-4080 |
|---|---|---|
| TDP | 1200W | 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 B300's FP16 performance of 2250 TFLOPS vastly exceeds the RTX 4080's 48.7 TFLOPS: this enables rapid inference on large models, reducing latency in deployment scenarios. Its FP8 capability at 4500 TFLOPS further accelerates quantized inference, a feature absent in the RTX 4080 specs. For training, the B300's 90 TFLOPS FP32 throughput surpasses the RTX 4080's 48.7 TFLOPS, speeding up forward and backward passes in deep learning pipelines.
Memory differences prove critical: the B300's 12000 GB/s bandwidth and 288 GB HBM3e VRAM allow massive batch sizes for models exceeding 100 billion parameters, minimizing data transfer bottlenecks. The RTX 4080's 717 GB/s and 16 GB GDDR6X constrain it to smaller batches and models, often requiring gradient accumulation that extends training times. Interconnects like NVSwitch on B300 enable seamless scaling across nodes, unlike the RTX 4080's standalone PCIe interface.
Power efficiency varies with workload intensity: B300's 1200W TDP delivers over 45 times the FP16 performance per watt in dense computations, while RTX 4080's 320W suits lighter loads efficiently.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B300
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA B300 SXM6 262GB VRAM | 262GB | 0 vCPU 0GB RAM | 🌍global | $7.39/GPU/hr | |||
 VERDA | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 240 vCPU 2040GB RAM | Helsinki | $7.50/GPU/hr $60.00/hr total (8×) | Available | ||
 Scaleway | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 224 vCPU 3840GB RAM 22352GB Storage | Paris | $8.73/GPU/hr $69.84/hr total (8×) | Available |
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 B300
The B300 excels in large-scale LLM training and inference: its 288 GB VRAM fits entire models without sharding, and 12000 GB/s bandwidth supports batch sizes up to thousands. Enterprise users leverage NVLink and NVSwitch for multi-GPU clusters handling trillion-parameter models at 2250 TFLOPS FP16.
High-throughput scientific simulations or fine-tuning massive datasets demand the B300's 90 TFLOPS FP32 and 4500 TFLOPS FP8, where RTX 4080 falls short on capacity.
When to Choose the RTX 4080
The RTX 4080 suits budget-conscious prototyping and small-scale inference: at $0.11 per hour from cloud offers, it processes models under 16 GB VRAM with 48.7 TFLOPS FP16 efficiently. Hobbyists or developers testing Stable Diffusion or fine-tuning compact LLMs benefit from its 320W TDP and PCIe simplicity.
Gaming-integrated workflows or edge deployments favor the RTX 4080, avoiding the B300's $2.45 per hour minimum cost and 1200W power demands.
Use Cases
B300's 288 GB VRAM and 90 TFLOPS FP32 handle billion-parameter models without sharding. RTX 4080's 16 GB limits scale to small LLMs only.
2250 TFLOPS FP16 and 4500 TFLOPS FP8 on B300 deliver low-latency serving for large models. RTX 4080's 48.7 TFLOPS suits tiny models exclusively.
B300 accelerates large dataset fine-tuning with 12000 GB/s bandwidth. RTX 4080 suffices for models under 16 GB at lower cost.
RTX 4080's 48.7 TFLOPS FP16 generates images quickly for models fitting 16 GB VRAM. B300 overkill for single-user creative tasks.
B300's 288 GB VRAM and NVLink support massive simulations. RTX 4080's 16 GB restricts complex datasets.
Frequently Asked Questions
What is the VRAM capacity of B300 versus RTX 4080?▾
B300 offers 288 GB HBM3e VRAM for large models. RTX 4080 provides 16 GB GDDR6X, suitable for smaller workloads.
How do memory bandwidths compare?▾
B300 delivers 12000 GB/s, enabling huge batch sizes. RTX 4080 achieves 717 GB/s, limiting data-intensive tasks.
What are the FP16 performance differences?▾
B300 reaches 2250 TFLOPS FP16 for fast inference. RTX 4080 hits 48.7 TFLOPS, adequate for lighter models.
What is the cloud pricing range?▾
B300 starts at $2.45 per hour, averaging $6.44 per hour across 7 offers. RTX 4080 begins at $0.11 per hour, averaging $0.28 per hour across 8 offers.
Which has higher TDP?▾
B300 requires 1200W TDP for datacenter use. RTX 4080 uses 320W, fitting consumer setups.
What architectures power these GPUs?▾
B300 uses Blackwell Ultra from 2025. RTX 4080 employs Ada Lovelace from 2022.
Which is cheaper to rent, the B300 or the RTX 4080?▾
Cloud rental prices for both the B300 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 B300 have compared to the RTX 4080?▾
The B300 has 288 GB of HBM3e memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find B300 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 B300 and the RTX 4080?▾
The B300 uses the Blackwell Ultra architecture (2025) while the RTX 4080 uses Ada Lovelace (2022). The B300 delivers 46.2x the FP16 throughput and 16.7x the memory bandwidth of the RTX 4080.