Packet AI
Bare MetalPacket AI provides bare-metal L40S and H100 GPU servers with no virtualization overhead and straightforward on-demand billing, making it a cost-effective option for AI inference and training workloads that need dedicated hardware performance. Bare-metal configurations eliminate the latency and overhead of hypervisor layers, delivering consistent GPU throughput for production LLM inference and model deployment. A practical choice for teams that need dedicated GPU hardware without the complexity of managed cloud services.
Cheapest On-Demand
$0.550/hr
Cheapest Spot
—
GPU Listings
3
Billing
On-demand
Performance Benchmarks
Compare With Another Cloud Provider
Provider Info
Headquarters
United States
Founded
2023
Regions
US
Min Commitment
None
Support
Standard
Strengths
- ▸Competitive L40S pricing
- ▸Bare metal performance
- ▸No virtualisation overhead
- ▸Simple billing
Limitations
- ▸Bare-metal only — requires more infrastructure management
- ▸Limited ML tooling vs managed cloud services
- ▸Smaller ecosystem and fewer integrations
Best For
Full GPU Catalog
| GPU Model | vRAM | On-Demand | Spot | Availability | Region |
|---|---|---|---|---|---|
| L40S | 48 GB | $0.550 | — | High | US |
| A100 80GB | 80 GB | $0.970 | — | High | US |
| H100 80GB | 80 GB | $2.07 | — | Med | US |
Community Reviews
Browse Other Providers
Packet AI GPU pricing overview
Packet AI is a bare-metal GPU cloud provider headquartered in United States. Packet AI provides bare-metal L40S and H100 GPU servers with no virtualization overhead and straightforward on-demand billing, making it a cost-effective option for AI inference and training workloads that need dedicated hardware performance. Bare-metal configurations eliminate the latency and overhead of hypervisor layers, delivering consistent GPU throughput for production LLM inference and model deployment. A practical choice for teams that need dedicated GPU hardware without the complexity of managed cloud services. Billing is On-demand with a minimum commitment of None. Available regions include US. On-demand GPU instances can be provisioned in minutes with no upfront cost, making Packet AI suitable for both short-duration experiments and sustained production workloads.
Packet AI vs other GPU providers
Packet AI 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: Competitive L40S pricing; Bare metal performance; No virtualisation overhead. Use the side-by-side comparison tool above to see Packet AI pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 3 Packet AI listings alongside 94+ providers in a single sortable view.
Best use cases for Packet AI
Packet AI is best suited for: Inference workloads, Cost-sensitive L40S users, Bare metal performance. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 3 active GPU listings on Packet AI, covering L40S, H100 80GB, A100 80GB. 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.
Packet AI billing model and cost structure
Packet AI uses On-demand 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 Packet AI
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 Packet AI pricing data is collected
Prices shown are sourced from Packet AI'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.
Ready to get started?
Launch your first GPU on Packet AI
On-demand from $0.550/hr — 3 GPU configurations available. On-demand billing.