L4 on JarvisLabs
Visit JarvisLabsJarvisLabs provides the NVIDIA L4 GPU, featuring 24GB GDDR6 VRAM on the Ada Lovelace architecture, as an enterprise-tier option optimized for AI inference, video transcoding, and virtual workstations. This combination stands out for developers, hobbyists, students, and fast.ai learners seeking simplicity in AI workloads. JarvisLabs emphasizes one-click JupyterLab environments, pause functionality to suspend compute billing while preserving data, per-minute billing, and spot instances for cost efficiency. The L4's efficient tensor cores deliver strong inference performance (up to 2x better than prior gens in some workloads) without the power draw of higher-end GPUs, making it ideal for prototyping, fine-tuning smaller models, or edge-like deployments. Key value propositions include rapid iteration cycles, minimal setup friction, and low-cost experimentation—perfect for ML engineers evaluating inference pipelines without long-term commitments. Limitations include potentially single-GPU focus, suiting bursty rather than sustained training.
Why NVIDIA L4 on JarvisLabs?
Choose JarvisLabs for NVIDIA L4 if you prioritize simplicity and cost control for inference-heavy workloads. The provider's pause feature complements the L4's low idle power (72W TDP), allowing instant resumption without data loss, ideal for intermittent experimentation. Per-minute billing and spot instances minimize costs for hobbyists/students versus hourly commitments elsewhere. One-click Jupyter aligns with L4's strengths in real-time inference and multimedia tasks, enabling fast prototyping. JarvisLabs' developer focus ensures pre-configured ML environments (PyTorch, TensorFlow), reducing setup time. This combo excels for cost-effective validation of L4's 242 TOPS INT8 inference, outperforming A10 in efficiency, without enterprise complexity.
Live Pricing
Real-time NVIDIA L4 offers from JarvisLabs
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
JarvisLabs | NVIDIA L4 24GB VRAM | 24GB | 32 vCPU 24GB RAM | 🌍Global | $0.44/GPU/hr |
Performance Notes
On JarvisLabs, expect NVIDIA L4 to deliver robust inference performance: ~30 TFLOPS FP32, 242 TOPS INT8, leveraging 4th-gen Tensor Cores for models like Stable Diffusion or LLMs up to 24GB. Network bandwidth is typically 10-25Gbps (provider-standard, unconfirmed specifics), sufficient for dataset loading but not H100-scale training. Storage options include fast NVMe SSDs for quick checkpoints. Multi-GPU scaling unknown—likely single-GPU emphasis for simplicity; confirm via docs. Real-world: strong for batch inference/video, but CPU pairing and exact interconnects impact end-to-end. Benchmarks align with NVIDIA specs; user reports praise responsiveness for prototyping.
A developer and hobbyist-focused provider emphasizing extreme simplicity for AI workloads.
Best For
Unique Features
- Pause functionality to stop compute billing while preserving storage
- One-click Jupyter environments
VRAM
24GB
Architecture
Ada Lovelace
Tier
enterprise
Platform Features
Getting Started
JarvisLabs makes launching NVIDIA L4 straightforward for AI workloads. Sign up, select an L4 instance from the dashboard, customize storage/RAM, and access via one-click JupyterLab or SSH. Pause anytime to save costs. Ideal for quick experiments with 24GB VRAM for inference or fine-tuning.
Steps
- 1Create a free account at jarvislabs.ai and add payment method.
- 2From dashboard, select 'Create Instance' and choose NVIDIA L4 GPU.
- 3Configure storage (e.g., 100GB NVMe), RAM, and environment (Jupyter/PyTorch).
- 4Launch instance; connect via provided Jupyter URL or SSH key.
- 5Work on workloads, then pause instance to stop compute billing.
Pro Tips
- Opt for spot instances on L4 for up to 50% savings during low-demand periods.
- Pre-load datasets to NVMe storage before pausing to resume instantly.
- Use pause aggressively for overnight/idle times to optimize per-minute costs.
Frequently Asked Questions
What is JarvisLabs's billing model for NVIDIA L4?▾
JarvisLabs bills per-minute for GPU instances including NVIDIA L4. Check their pricing page for the most current billing details.
Does JarvisLabs offer spot instances for NVIDIA L4?▾
Yes, JarvisLabs offers spot/preemptible instances for NVIDIA L4, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and training jobs with checkpointing. Note that spot instances can be interrupted when demand is high, so ensure your workflow can handle preemption gracefully.
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