Tesla V100 16GB on Lambda Labs
Visit Lambda LabsLambda Labs provides the NVIDIA Tesla V100 16GB, a Volta architecture GPU with 16GB HBM2 VRAM, optimized for AI, deep learning, and HPC workloads. This enterprise-tier GPU delivers 125 TFLOPS FP16 performance via 640 Tensor Cores, making it suitable for training models like ResNet-152 or BERT-base that fit within 16GB memory. Lambda Labs, a premier GPU cloud with system integrator expertise, stands out with pre-configured Lambda Stack—featuring Ubuntu, CUDA 12.x, PyTorch, TensorFlow, and Jupyter—enabling ML engineers to start instantly without setup friction. Ideal for teams prioritizing rapid iteration over bleeding-edge hardware, it offers per-hour billing for cost flexibility in bursty or experimental workloads. Key value propositions include reliable uptime, deep hardware optimization, and scalable multi-GPU configs, positioning this combo as a dependable choice for production ML pipelines valuing stability and ease over raw speed.
Why NVIDIA Tesla V100 16GB on Lambda Labs?
Choose Lambda Labs for V100 16GB due to their hardware integrator roots, ensuring optimized BIOS, drivers, and cooling for sustained performance. Lambda Stack eliminates environment headaches, pre-loading ML frameworks and dependencies, perfect for V100's legacy Volta ecosystem. Per-hour on-demand billing suits variable workloads without long-term commitments, often cheaper than hyperscalers for short runs. Their infrastructure complements V100's strengths in FP16/INT8 tasks with high-speed NVMe storage (up to 3.8TB local SSD) and 10-100Gbps networking for distributed training. Unlike general clouds, Lambda's ML-focused ops provide faster support and fewer interruptions, ideal for engineers needing quick, reliable access to this cost-effective GPU for fine-tuning or inference on memory-constrained models.
Live Pricing
Real-time NVIDIA Tesla V100 16GB offers from Lambda Labs
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 88 vCPU 448GB RAM 6041GB Storage | Texas | $0.79/GPU/hr $6.32/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 92 vCPU 448GB RAM 6041GB Storage | 🌍global | $0.79/GPU/hr $6.32/hr total (8×) | Sold Out | ||
Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 88 vCPU 448GB RAM 6041GB Storage | 🌍global | $0.79/GPU/hr $6.32/hr total (8×) | Sold Out |
Performance Notes
On Lambda Labs, expect V100 16GB to deliver benchmark TFLOPS close to NVIDIA specs: ~15 TFLOPS FP32, 125 TFLOPS FP16 with Tensor Cores. Strong for single-GPU training of CNNs/RNNs up to 16GB batches; multi-GPU scaling (up to 8x in clusters) via PCIe 3.0/NVLink yields 80-95% efficiency on frameworks like PyTorch DDP. Networking at 10Gbps Ethernet standard (faster in premium clusters) supports moderate distributed jobs; NVMe storage enables fast dataset loading. No public benchmarks specific to Lambda-V100, but user reports confirm low-latency Jupyter access and consistent clocks. Limitations: slower than A100/H100 for modern transformers; verify multi-node InfiniBand availability as it's cluster-dependent.
A premier GPU cloud provider with deep hardware expertise, offering pre-configured environments for ML engineers.
Best For
Unique Features
- Lambda Stack for easy setup
- Deep hardware expertise as a system integrator
VRAM
16GB
Architecture
Volta
Tier
enterprise
Platform Features
Getting Started
Getting started with Lambda Labs' NVIDIA Tesla V100 16GB is straightforward for ML engineers. Sign up for an account, select an on-demand instance with this GPU, and launch using the pre-configured Lambda Stack image. Connect via SSH or web Jupyter, and begin training/inference immediately with optimized CUDA and ML frameworks—no custom setup required.
Steps
- 1Create a free account at lambdalabs.com and add payment method.
- 2Navigate to 'On-Demand' instances and select 1x V100 16GB configuration.
- 3Choose 'Lambda Stack' image (includes CUDA, PyTorch, TensorFlow).
- 4Click 'Launch Instance'; wait 2-5 minutes for SSH keys and IP.
- 5SSH in (ssh root@<IP>) or access JupyterLab via browser link.
Pro Tips
- Resize instances dynamically via dashboard to match workload memory needs and control costs.
- Use Lambda Stack's pre-built Docker containers for reproducible environments across spot/on-demand.
- Monitor GPU utilization with nvidia-smi and Lambda's console for optimal batch sizing on 16GB VRAM.
Frequently Asked Questions
What is Lambda Labs's billing model for NVIDIA Tesla V100 16GB?▾
Lambda Labs bills per-hour for GPU instances including NVIDIA Tesla V100 16GB. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.
Does Lambda Labs offer spot instances for NVIDIA Tesla V100 16GB?▾
No, Lambda Labs does not currently offer spot instances for NVIDIA Tesla V100 16GB. All instances are billed at on-demand rates. However, they do offer reserved instances for committed usage, which can provide significant discounts for long-term workloads.
Does Lambda Labs offer reserved instances for NVIDIA Tesla V100 16GB?▾
Yes, Lambda Labs offers reserved instance pricing for NVIDIA Tesla V100 16GB, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact Lambda Labs for current reserved pricing and commitment terms.
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