Packet AI

Packet AI GPU pricing overview

Packet AI is a specialist GPU cloud provider headquartered in United States. Packet AI offers dedicated single-tenant and dynamic scheduler-managed shared GPU instances for AI workloads. Both deployment options are available with on-demand hourly and monthly billing. Packet AI also provides Token Factory, a managed LLM inference API with an OpenAI-compatible endpoint for teams that want to serve models without operating their own inference stack. Billing is On-demand hourly and monthly with a minimum commitment of None. Available regions include US, UK. GPU instances can be provisioned on-demand, 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: Dedicated and dynamic GPU instances; Hourly and monthly billing; Managed OpenAI-compatible LLM inference API. 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 5 Packet AI listings alongside 91+ providers in a single sortable view.

Best use cases for Packet AI

Packet AI is best suited for: Dedicated GPU workloads, Scheduler-managed GPU capacity, Managed LLM inference. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 5 active GPU listings on Packet AI, covering B200, A100 80GB, RTX 4090, RTX PRO 6000 Blackwell and more. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

Packet AI billing model and cost structure

Packet AI uses On-demand hourly and monthly pricing. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. 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 — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used 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 kept in our history archive 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 many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.