Lambda Labs

Lambda Labs GPU pricing overview

Lambda Labs is a specialist GPU cloud provider headquartered in San Francisco, CA. Lambda Labs offers on-demand and reserved H100, A100, and RTX A6000 GPU instances with simple flat pricing and no egress fees — a refreshing contrast to hyperscaler complexity. Pre-configured PyTorch and TensorFlow environments mean researchers can start LLM training or fine-tuning in minutes without any setup overhead. A go-to on-demand GPU cloud for ML teams that want predictable hourly GPU rental costs without long-term commitments. Billing is On-demand, Reserved (1yr/3yr) with a minimum commitment of None (on-demand). Available regions include us-east-1, us-west-1, us-west-3, eu-central-1 and 1 more. On-demand GPU instances can be provisioned in minutes with no upfront cost, making Lambda Labs suitable for both short-duration experiments and sustained production workloads.

Lambda Labs vs other GPU providers

Lambda Labs competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Simple pricing; Pre-configured ML stack; No egress fees. Use the side-by-side comparison tool above to see Lambda Labs pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 12 Lambda Labs listings alongside 94+ providers in a single sortable view.

Best use cases for Lambda Labs

Lambda Labs is best suited for: ML researchers, Deep learning training, Teams wanting simplicity. Support tiers range from Community → Enterprise, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 12 active GPU listings on Lambda Labs, covering H100 80GB, A100 80GB, A100 40GB, H100 40GB and more. For workloads requiring the highest single-GPU throughput, H100 SXM5 instances with NVLink interconnect deliver the best performance per dollar at scale. For cost-sensitive fine-tuning or inference of models up to 13B parameters, A100 40GB or RTX 4090 instances typically offer the best value.

Lambda Labs billing model and cost structure

Lambda Labs uses On-demand, Reserved (1yr/3yr) pricing. On-demand instances are billed per second or per hour depending on the instance type, with no termination fees. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing, where available, can reduce costs by 30–60% for predictable long-running workloads. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Lambda Labs

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — the 3,350 GB/s HBM3 bandwidth and 989 TFLOPS FP16 throughput make them 2–2.5× faster than A100 for transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB offers the best cost-per-token on most providers. RTX 4090 instances are ideal for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Lambda Labs pricing data is collected

Prices shown are sourced from Lambda Labs's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are retained for 90 days and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and 14+ other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.