Specifications Compared
| Spec | L4 | RTX-4080 |
|---|---|---|
| TDP | 72W | 320W |
| VRAM | 24 GB | 16 GB |
| CUDA Cores | 7,424 | 9,728 |
| Memory Type | GDDR6 | GDDR6X |
| Architecture | Ada Lovelace | Ada Lovelace |
| Form Factors | PCIe | PCIe |
| Interconnect | PCIe 4.0 | |
| Tensor Cores | 232 | 304 |
| FP8 Performance | 242 TFLOPS | |
| FP16 Performance | 121 TFLOPS | 48.7 TFLOPS |
| FP32 Performance | 30.3 TFLOPS | 48.7 TFLOPS |
| FP64 Performance | 0.5 TFLOPS | |
| INT8 Performance | 242 TOPS | 780 TOPS |
| Memory Bandwidth | 300 GB/s | 717 GB/s |
Performance Analysis
Compute specs reveal specialized strengths: L4's 121 TFLOPS FP16 and 242 TFLOPS FP8 accelerate quantized inference and mixed-precision training, where FP32 at 30.3 TFLOPS suffices for many steps. RTX 4080 SUPER's balanced 48.7 TFLOPS across FP16 and FP32 supports graphics rendering and FP32-dominant training phases equally well. The FP16/FP32 delta means L4 prioritizes tensor operations for AI scale-out, while RTX 4080 SUPER handles diverse compute without precision bottlenecks.
Memory traits impact workloads profoundly: RTX 4080 SUPER's 717 GB/s bandwidth enables larger batch sizes in training, minimizing data loading stalls compared to L4's 300 GB/s. L4 counters with 24 GB VRAM versus 16 GB, fitting bigger models or sequences in inference without offloading. Power draw of 72W for L4 versus 320W for RTX 4080 SUPER affects density: L4 packs more units per server rack, lowering cooling costs in prolonged cloud sessions.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
L4
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA L4 24GB VRAM | 24GB | 64 vCPU 101GB RAM 485GB Storage | Iceland | $0.33/GPU/hr | Available | ||
 RunPod | NVIDIA L4 24GB VRAM | 24GB | 12 vCPU 50GB RAM | 🌍global | $0.39/GPU/hr | |||
 TensorDock | NVIDIA L40S 48GB VRAM | 48GB | 0 vCPU 0GB RAM | Wolverhampton | $0.55/GPU/hr | Available | ||
RunPod | NVIDIA L40 48GB VRAM | 48GB | 8 vCPU 94GB RAM | 🌍global | $0.82/GPU/hr | |||
RunPod | NVIDIA L40S 48GB VRAM | 48GB | 16 vCPU 94GB RAM | 🌍global | $0.86/GPU/hr |
RTX 4080 SUPER
| 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 L4
The L4 stands out for inference-heavy deployments: 24 GB VRAM accommodates large language models without splitting, and 121 TFLOPS FP16 with 242 TFLOPS FP8 speeds batched serving. Its 72W TDP supports dense cloud instances, ideal for 24/7 edge AI at $0.32/hr starting price. Datacenter optimizations ensure reliability over consumer-grade alternatives.
When to Choose the RTX 4080 SUPER
The RTX 4080 SUPER excels in bandwidth-intensive training: 717 GB/s memory speed handles massive batches, pairing with 48.7 TFLOPS FP32 for gradient computations. Lower pricing from $0.17/hr (average $0.32/hr) delivers value for bursty workloads. Balanced compute suits creative AI like diffusion models alongside ML.
Use Cases
RTX 4080 SUPER's 717 GB/s bandwidth supports larger training batches than L4's 300 GB/s. Balanced 48.7 TFLOPS FP32 aids optimization loops.
L4's 24 GB VRAM fits full models without quantization losses, exceeding RTX 4080 SUPER's 16 GB. 242 TFLOPS FP8 accelerates serving.
L4's higher FP16 at 121 TFLOPS suits low-precision tuning; RTX 4080 SUPER's bandwidth handles data flows. Choice depends on model size.
RTX 4080 SUPER's 48.7 TFLOPS FP32 and 717 GB/s bandwidth speed image generation pipelines. Lower $0.17/hr cost fits iterative creative work.
L4's 72W TDP enables dense simulations; 24 GB VRAM manages large datasets. FP16 efficiency at 121 TFLOPS boosts parallel solves.
Frequently Asked Questions
Which GPU has more VRAM?▾
The L4 provides 24 GB GDDR6 VRAM, surpassing the RTX 4080 SUPER's 16 GB GDDR6X. This advantage aids loading larger AI models in inference. Bandwidth remains higher on RTX 4080 SUPER at 717 GB/s.
What are the power consumption differences?▾
L4 draws 72W TDP, far lower than RTX 4080 SUPER's 320W. Lower power suits high-density cloud racks and reduces operational costs. RTX 4080 SUPER demands robust cooling for sustained loads.
Which is cheaper in the cloud?▾
RTX 4080 SUPER starts at $0.17/hr (average $0.32/hr) across 3 offers, undercutting L4's $0.32/hr (average $0.68/hr) over 15 offers. Price reflects availability and power efficiency. Compute value varies by task.
How do FP16 performances compare?▾
L4 delivers 121 TFLOPS FP16, doubling RTX 4080 SUPER's 48.7 TFLOPS. This boosts mixed-precision AI workloads on L4. FP8 on L4 reaches 242 TFLOPS for further quantization gains.
Is L4 or RTX 4080 SUPER better for inference?▾
L4 leads with 24 GB VRAM and 242 TFLOPS FP8 for high-throughput serving. RTX 4080 SUPER's 717 GB/s bandwidth aids smaller models. Choose L4 for memory-bound LLMs.
What interconnect do they use?▾
Both employ PCIe form factors, with L4 specifying PCIe 4.0. RTX 4080 SUPER aligns via PCIe for cloud compatibility. Speeds support direct server integration without NVLink.
Which is cheaper to rent, the L4 or the RTX 4080?▾
Cloud rental prices for both the L4 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 L4 have compared to the RTX 4080?▾
The L4 has 24 GB of GDDR6 memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find L4 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 L4 and the RTX 4080?▾
The L4 uses the Ada Lovelace architecture (2023) while the RTX 4080 uses Ada Lovelace (2022). The L4 delivers 2.5x the FP16 throughput and 2.4x the memory bandwidth of the RTX 4080.