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JarvisLabs GPU Pricing 2026: Cloud GPU Rental Rates

Deploybase · January 6, 2025 · GPU Pricing

Contents

JarvisLabs GPU Pricing: Overview

JarvisLabs is a GPU cloud provider positioned for researchers and ML engineers. The platform offers hourly GPU rental starting from entry-level L4 ($0.44/hr) to high-performance H100 SXM ($2.69/hr) and H200 ($3.80/hr). No long-term contracts. Pay-per-second billing. JarvisLabs is cheaper than Lambda and comparable to RunPod for single-GPU workloads, making it a solid mid-tier option between budget providers (Vast.AI) and large-scale providers (CoreWeave).

Pricing Structure

Billing Model

  • Per-second billing: Rent a GPU for 30 seconds, pay for 30 seconds. No hourly minimums.
  • On-demand pricing: Rates are consistent (no marketplace fluctuations like Vast.AI).
  • Spot pricing: Optional lower rates for interruptible instances. Discounts vary (20-50% off on-demand).
  • No data transfer charges: Inbound and outbound data transfer are free.
  • Storage: Pay per GB/month for persistent storage (if used). Typical: $0.10-0.20/GiB/month.

Discount Tiers

Monthly commitments open up discounts:

  • Pay-as-teams-go: Full price
  • 1-month prepay: discount
  • 3-month prepay: discount
  • Annual commitment: discount

Check JarvisLabs console for current discount rates; they vary by GPU model and region.

GPU Hourly Rates

Consumer/Inference GPUs

GPU ModelVRAM$/Hour$/Month (730 hrs)Use Case
RTX 409024GB$0.29$212Inference, Fine-tuning
RTX 408012GB$0.19$139Prototyping
L4048GB$0.49$358Inference
L40S48GB$0.59$431High-throughput inference

Workhorse Training GPUs

GPU ModelVRAM$/Hour$/Month (730 hrs)Use Case
A100 (40GB)40GB$1.29$942Training, Fine-tuning
A100 (80GB)80GB$1.49$1,088Large model training
H100 SXM80GB$2.69$1,964Multi-GPU training

High-Performance / Next-Gen GPUs

GPU ModelVRAM$/Hour$/Month (730 hrs)Use Case
H200 SXM141GB$3.80$2,774Large model training

Pricing as of March 21, 2026. Check jarvislabs.ai/pricing for latest rates.

Spot vs On-Demand

On-Demand Pricing

Consistent hourly rates with guaranteed availability. Best for:

  • Production workloads requiring uptime
  • Research projects with fixed timelines
  • Multi-day training jobs

Spot Pricing

Lower rates (typically 30-50% discount) but instances can be terminated with notice. Best for:

  • Batch processing with checkpointing
  • Experimental jobs (loss is acceptable)
  • Cost-conscious development
  • Non-critical fine-tuning

Example: H100 PCIe

  • On-demand: $1.79/hr
  • Spot: $0.89-1.25/hr (50-30% discount)

Monthly savings on H100: $500-650 if using spot instances 24/7.

Storage and Data Transfer

Persistent Storage

JarvisLabs offers attached storage for long-running instances:

  • Price: $0.10-0.20/GiB/month
  • Ideal for: Datasets, model checkpoints, results
  • Alternative: Mount external S3 (AWS) or Google Cloud Storage

A 100GB dataset costs $10-20/month on JarvisLabs storage. Inbound data transfer from S3 is free.

Data Transfer

  • Inbound: Free
  • Outbound: Free (no egress charges)
  • Cross-datacenter: Not applicable (single region)

This is a major advantage over some cloud providers. AWS charges $0.12/GB outbound.

Cost Comparison

JarvisLabs vs Competitors (Single H100 SXM, 24/7 Monthly)

Provider$/Hour$/MonthNotes
JarvisLabs$2.69$1,964Consistent pricing
RunPod$2.69$1,964Comparable pricing
Lambda$3.78$2,760SXM; $2.86 for PCIe
AWS$3.41+$2,489+Hyperscaler tax
Vast.AI$1.80-2.80$1,314-2,044Marketplace, price volatility

JarvisLabs is middle-ground pricing. Cheaper than Lambda/AWS. More expensive than Vast.AI but more stable.

A100 Training Cost Over 1 Week

  • JarvisLabs A100 (40GB): $1.29 × 168 hrs = $217
  • JarvisLabs A100 (80GB): $1.49 × 168 hrs = $250
  • RunPod A100 PCIe: $1.19 × 168 hrs = $200
  • Lambda A100 PCIe: $1.48 × 168 hrs = $249
  • Vast.AI A100 (avg): $0.78 × 168 hrs = $131

RunPod is the most competitive on A100 pricing. JarvisLabs is competitive with Lambda.

Billing and Minimum Commitments

Minimum Charges

  • Per-second billing: No minimum. Rent for 1 second, charge for 1 second.
  • No setup fees
  • No cancellation fees
  • No long-term contracts required

This is consumer-friendly. Spin up a GPU, use it for 5 minutes, pay proportionally.

Invoice and Billing

  • Charges posted to account immediately after instance stops
  • Invoices generated monthly
  • Payment methods: Credit card, PayPal, crypto (Bitcoin, Ethereum)
  • Billing timezone: UTC (does not follow local time)

Unused Credits

JarvisLabs offers no "credits" system. Teams pay as teams go. No prepaid balances.

Use Case Recommendations

JarvisLabs is Best For:

Budget-conscious researchers and startups. Cheaper than Lambda ($0.99/hr A100 vs $1.48/hr), more stable than Vast.AI (fixed prices, no marketplace volatility). For academic labs and early-stage companies with tight budgets, this price point is critical.

Multi-day training jobs with intermittent compute. Per-second billing means no waste on idle time. Stop the GPU when training completes at 3am, pay only for what was used. Typical per-second billing: for teams that train for 47 hours and 23 minutes, teams pay for exactly that, not 48 hours (as hourly billing would charge).

Example: Fine-tuning job that takes 15.5 hours.

  • Hourly billing: $0.99 × 16 = $15.84
  • Per-second billing on JarvisLabs: $0.99 × 15.5 = $15.35
  • Savings: $0.49 per job. Across 50 jobs/month: $24.50/month. Across a year: $294.

Teams avoiding vendor lock-in. No contracts. Free data transfer inbound and outbound. Vendor-agnostic deployment. Standard Docker image support. Train a model on JarvisLabs, deploy to RunPod or Lambda without code changes.

Prototyping before scaling. Develop on single H100 at JarvisLabs ($1.79/hr), validate approach and training pipeline, then scale to multi-GPU clusters on RunPod ($10.76/hr for 4x H100) or Lambda when moving to production.

Educational use and learning. Students and learners benefit from low hourly costs. A100 at $0.99/hr is the cheapest way to learn distributed training without university cluster access.

JarvisLabs Is NOT Best For:

Production inference serving. No SLA guarantees or uptime commitments. Lambda's 99%+ uptime SLA is necessary for customer-facing APIs where downtime costs revenue. JarvisLabs is research-grade, not production-grade.

Enterprises needing compliance and audit trails. Lambda, AWS, and Azure provide HIPAA, SOC 2, and GDPR compliance certifications. Financial, healthcare, and regulated workloads need these. JarvisLabs does not provide formal compliance infrastructure.

Multi-node distributed training at 16+ GPU scale. RunPod and Lambda have better orchestration for large clusters. JarvisLabs is single-machine focused. Scaling beyond 8 GPUs requires manual networking configuration.

Vision/multimodal workloads requiring NVLink efficiency. JarvisLabs A100 is PCIe variant (slower inter-GPU communication). For vision training across 8x A100, Lambda's SXM variant (95%+ efficiency) is worth the 50% price premium.

Teams requiring professional support. JarvisLabs has community forums but no dedicated support tiers. Lambda and AWS have professional support with SLAs.

Real-World Scenarios

Scenario 1: Fine-Tune Llama 7B on Custom Data (24 Hours)

Team wants to fine-tune Llama 7B on 10M tokens of custom data. Single A100 is sufficient.

JarvisLabs A100 (40GB):

  • Duration: 24 hours (actual training time: 23 hours 45 minutes)
  • Cost: $1.29 × 23.75 hours = $30.64
  • Total including overhead (wait time, setup): ~$32

RunPod A100 PCIe:

  • Cost: $1.19 × 24 hours = $28.56

Lambda A100 PCIe:

  • Cost: $1.48 × 24 hours = $35.52

RunPod is cheapest at A100 tier. JarvisLabs and Lambda are comparable. Savings compound across many training jobs.

Scenario 2: Research Team Training 13B Model (1 Week)

Multi-GPU job. 4x A100 SXM, training for 7 days continuous (168 hours).

JarvisLabs A100 (80GB):

  • Cost: $1.49 × 4 GPUs × 168 hours = $1,001
  • Provides standard connectivity between GPUs
  • Straightforward single-machine setup

RunPod A100 PCIe:

  • Cost: $1.19 × 4 × 168 = $800
  • More affordable at A100 tier

Lambda A100 PCIe:

  • Cost: $1.48 × 4 × 168 = $995
  • Similar to JarvisLabs 80GB A100

For research teams, RunPod has the edge on A100 pricing. JarvisLabs is on par with Lambda.

Scenario 3: Continuous Fine-Tuning Service (Monthly Workload)

Company runs 50 fine-tuning jobs per month. Each job uses 1x H100 for 12 hours.

JarvisLabs Monthly Cost (H100 SXM):

  • 50 jobs × 12 hours × $2.69/hr = $1,614/month

Spot Instances (if available and interruption tolerable):

  • 50 jobs × 12 hours × $1.35/hr = $810/month (estimated 50% discount)
  • Savings: $804/month
  • Risk: Interruptions mid-training. Mitigated with hourly checkpointing.

RunPod H100 SXM (for comparison):

  • Cost: $2.69 × 12 × 50 = $1,614/month
  • Comparable pricing to JarvisLabs

For a continuous fine-tuning service, JarvisLabs at-demand pricing ($1,074) is attractive. If the service can tolerate occasional interruptions (which fine-tuning can, via checkpoints), spot instances reduce monthly cost to $534.

Scenario 4: Academic Lab Annual Budget ($10,000/year)

Typical ML research lab budget: $10K/year for cloud GPU.

With JarvisLabs (A100 40GB):

  • $10,000 ÷ $1.29/hr = 7,752 hours
  • ~323 days of single-GPU compute
  • Or 41 days of 8x GPU compute
  • Or 6-7 substantial research projects (2+ months each)

With RunPod (A100 PCIe, cheapest):

  • $10,000 ÷ $1.19/hr = 8,403 hours
  • ~350 days of compute
  • ~8% more compute for same budget

With Lambda (comparable setup):

  • $10,000 ÷ $1.48/hr = 6,757 hours
  • ~281 days of compute
  • ~13% less compute than JarvisLabs for same budget

JarvisLabs stretches academic budgets further.

Scenario 5: Prototyping Before Production Deployment

Team wants to validate a fine-tuning pipeline before deploying to production.

  • Week 1-2: Develop and debug on JarvisLabs H100 SXM ($2.69/hr)
  • Cost: ~50 hours × $2.69 = ~$135
  • Week 3: Run final validation (full 7-day training simulation)
  • Cost: 8x H100 × 168 hours × $2.69 = $3,619
  • Total prototyping cost: ~$2,500

Once validated, move to production on Lambda (more SLA, better support). The prototyping phase is cheap on JarvisLabs, validating architecture before committing to expensive production infrastructure.

FAQ

How does JarvisLabs compare to Vast.AI? Vast.AI is cheaper ($0.79-1.29/hr for A100) but prices fluctuate daily. JarvisLabs is stable ($0.99/hr). Choose Vast.AI if you can tolerate price volatility and handle instance termination. Choose JarvisLabs for consistency.

Does JarvisLabs offer reserved instances? Not directly. But monthly/annual prepayment discounts apply. Contact sales for custom pricing on long-term commitments.

Can I run multiple GPUs on JarvisLabs? Yes. Rent 2-8 GPUs per instance. Multi-GPU pricing scales linearly ($1.79 × 4 for 4x H100).

Is JarvisLabs GDPR-compliant? JarvisLabs processes data in US and EU datacenters. Check their privacy policy and contact support for formal compliance details, as they do not currently publish GDPR certifications.

How long does it take to start an instance? Typically 2-5 minutes. Faster than AWS/Azure, slower than Lambda.

Can I use my own Docker image? Yes. Upload custom images or use pre-installed templates (PyTorch, TensorFlow, JAX). Full root access.

What regions does JarvisLabs support? JarvisLabs operates datacenters in the US and EU. Check jarvislabs.ai for the current list of available regions, as this can change with capacity expansions.

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