A100 provides Ampere compute for established training, fine-tuning and batch inference pipelines. It remains an option to evaluate when compatibility and job economics matter more than selecting the newest generation.
On-demand prices are Arvica's live catalogue rates (USD per GPU-hour), shown at deploy time. Reserved and enterprise prices are set in an accepted Arvica proposal.
Term, volume and dedicated-capacity prices are set in an accepted Arvica proposal. Configuration, region and delivery are confirmed in your proposal.
Availability confirmed before order acceptance. Taxes and additional services are confirmed before payment.
Choose around your workload
01
When to choose this GPU
Consider A100 for existing Ampere-tested applications and repeatable experiments. Compare the complete job duration and memory requirements using the exact 40 GB or 80 GB configuration.
02
When to consider alternatives
Compare H100 for Hopper-specific acceleration or H200 for greater memory capacity. A lower hourly rate does not necessarily mean a lower completed-job cost.
03
Memory planning
A100 comes in 40 GB and 80 GB variants. Identify the memory size and PCIe or SXM form factor before comparing prices or reusing a sizing result.
04
Interconnect and scale
Confirm the available GPU-to-GPU links for distributed work. Host RAM, storage bandwidth and network limits can dominate data-heavy training jobs.
05
Software compatibility
Use Ampere-compatible CUDA builds and validate the precision modes supported by your framework. Test older pinned dependencies before moving an existing application.
Specify the OS, framework and container image version. Arvica confirms the driver and GPU compatibility before activation. A template name is not a software licence or a guaranteed preinstalled image.
SSH & private access
Save an SSH public key during configuration. Keep the private key on your own device. The verified deployment record provides the actual host and login user once access is ready.
Storage & checkpoints
Separate boot disk, datasets, model weights and checkpoints in your capacity plan. Confirm persistence, backup ownership, attachment rules and deletion behaviour before relying on a volume.
Network & data location
Confirm ingress, egress, public IP access and private networking where required. For regulated or location-sensitive data, specify the required region in advanced settings before submitting.
First connection checklist
Once Arvica supplies verified access, connect using your private key and the exact host/user shown in your instance. Then inspect the allocated GPUs and topology.
Replace USER and HOST with your own deployment details. These commands inspect an existing machine; they do not provision one.
Size the job before committing
Model memory
Parameter count × bytes per weight estimates weights only. Add KV cache, activations, temporary tensors and framework overhead. Training additionally needs gradients and optimiser state.
Measure useful throughput
Record model version, precision, input/output length, concurrency, latency and error rate together. Compare the cost of a completed job or successful request under the same conditions.
Complete cost
Include all allocated GPUs, runtime, storage, networking, taxes and support. Monthly projections use 730 hours for comparison; your contract defines the billable period and minimum term.
Model fit and throughput depend on precision, software, batch size, context and system topology. Compare workload estimates rather than an unsupported tokens-per-second promise.
Consider A100 for existing Ampere-tested applications and repeatable experiments. Compare the complete job duration and memory requirements using the exact 40 GB or 80 GB configuration.
What should I check about memory?
A100 comes in 40 GB and 80 GB variants. Identify the memory size and PCIe or SXM form factor before comparing prices or reusing a sizing result.
What software should I prepare?
Use Ampere-compatible CUDA builds and validate the precision modes supported by your framework. Test older pinned dependencies before moving an existing application.
What interconnect is included?
Confirm the available GPU-to-GPU links for distributed work. Host RAM, storage bandwidth and network limits can dominate data-heavy training jobs.
Can I stop billing by shutting down the operating system?
Do not assume so. Resource allocation, storage and reserved commitments may continue billing. Check the contract and request the appropriate lifecycle action through support.
Is my data retained after termination?
Retention depends on the agreed storage configuration. Export data and confirm backup and volume retention before requesting termination.
Can I deploy immediately?
Current configurations enter Arvica capacity review. Running status is shown only after infrastructure is verified.
Is the live GPU-hour price my total cost?
Not necessarily. On-demand usage is billed at Arvica's live catalogue rate; storage, network, tax and service charges are itemised before you commit, and reserved or enterprise terms are set in your proposal.
Can I use a larger cluster?
Use the enterprise capacity path for multi-node networking and reserved allocations.
Consider A100 for existing Ampere-tested applications and repeatable experiments. Compare the complete job duration and memory requirements using the exact 40 GB or 80 GB configuration.
Evaluate H100 for repeatable training jobs and inference services with established CUDA kernels. Benchmark your actual sequence lengths and batch sizes before reserving a larger allocation.
Test H200 when model weights, KV cache and concurrent requests compete for memory. Extra capacity can reduce the need to split a workload across GPUs, depending on precision and runtime overhead.
Evaluate L40S for image generation, rendering and inference applications that can use a graphics-capable GPU. Test the actual model, resolution and concurrency you intend to serve.
What changes between configurations
Configuration
Practical difference
Confirm before ordering
PCIe · VM · 40 / 80 GB HBM2e
Guest OS and assigned resources within a virtual machine
Exact card, GPU allocation, peer-to-peer connectivity, CPU and disk limits
PCIe · Bare Metal · 40 / 80 GB HBM2e
Dedicated physical host configuration; host access follows the confirmed service
Exact card, GPU allocation, peer-to-peer connectivity, CPU and disk limits
SXM · VM · 40 / 80 GB HBM2e
Guest OS and assigned resources within a virtual machine
GPU count, NVLink/NVSwitch topology, inter-node fabric and NUMA placement
SXM · Bare Metal · 40 / 80 GB HBM2e
Dedicated physical host configuration; host access follows the confirmed service
GPU count, NVLink/NVSwitch topology, inter-node fabric and NUMA placement
Size the working set
Weights alone are approximately parameters × bytes per parameter. A 7B model at BF16 needs about 14 GB just for weights; a 70B model about 140 GB. Add KV cache, activations, framework buffers and training state. These are arithmetic estimates, not fit guarantees.
Measure useful performance
Compare the same model revision, precision, sequence length and concurrency. Record time to first token, p95 latency, output tokens/s, peak memory and full instance cost. Training comparisons should include step time and convergence at the same quality target.
Evidence status
Manufacturer specifications are linked below. No Arvica application-throughput benchmark is published for this configuration yet. Request a workload-specific acceptance test before making performance commitments.
Configure dedicated PCIe / SXM, virtual-machine and bare-metal capacity with Arvica. Submit your requirements for a deployment proposal.
Request your preferred configuration. Host sizes are illustrative; the written proposal confirms capacity, location, final pricing and delivery. Submitting an enquiry is free and does not reserve capacity.
4 configurations available for enquiry
Capacity confirmation requiredVirtual machine
A100 PCIe
Price in your proposalYour configuration and term are priced in an Arvica proposal. Delivery schedule confirmed in proposal.
Illustrative configuration
GPU
1 × A100
GPU memory
40 / 80 GB HBM2e
vCPU
16
RAM
128 GB
Storage
500 GB
AvailabilityConfirmation requiredRegionConfirm in proposalServiceArvica Cloud
