NVIDIA / Ada Lovelace

L40S
Specs, pricing & compute

L40S combines Ada Lovelace compute and graphics capabilities with 48 GB GDDR6. Consider it for inference, visual workloads and development environments that fit within its memory budget.

HARDWARE REFERENCE

48 GB GDDR6

Ada Lovelace

Exact GPU variant, CPU, RAM and interconnect depend on the confirmed instance. Memory values are hardware references, not allocated resources.

Manufacturer specifications ↗
CAPACITY

Request availability

Choose on-demand or reserved compute. Location, start time and access environment are confirmed before provisioning.

ARVICA PRICE / USD

L40S · Arvica pricing

ON-DEMAND

View live price →

Arvica's live catalogue rate in USD per GPU-hour, for the exact configuration and current availability, locked in a signed quote before payment.

RESERVED & ENTERPRISE

Request capacity 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

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.

02

When to consider alternatives

Compare RTX PRO 6000 Server for a larger graphics-capable memory footprint. Compare H100 or H200 for workloads needing an HBM-based configuration and suitable multi-GPU fabric.

03

Memory planning

NVIDIA lists 48 GB GDDR6 with ECC. Leave room for runtime allocations and temporary tensors in addition to the model weights.

04

Interconnect and scale

L40S does not provide NVLink. Do not assume an NVSwitch-connected training node when selecting multiple L40S GPUs; confirm PCIe topology and network requirements.

05

Software compatibility

Validate both compute and graphics requirements, including container access, display or rendering libraries and application licensing where applicable.

NVIDIA hardware reference ↗ · Selection guidance is Arvica's assessment; validate it against your workload

FROM CONFIGURATION TO ACCESS

Prepare your compute environment

Operating system & containers

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.

ssh -i ~/.ssh/your_key USER@HOST
nvidia-smi
nvidia-smi topo -m

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.

RESERVED / CLUSTERS

Need more capacity?

Define your GPU allocation, interconnect, storage, location and commitment. Arvica prepares a capacity proposal around the workload.

Request capacity ↗

Plan before benchmarking

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.

Training cost calculator →

FAQ

How do I choose the GPU count?

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 should I check about memory?

NVIDIA lists 48 GB GDDR6 with ECC. Leave room for runtime allocations and temporary tensors in addition to the model weights.

What software should I prepare?

Validate both compute and graphics requirements, including container access, display or rendering libraries and application licensing where applicable.

What interconnect is included?

L40S does not provide NVLink. Do not assume an NVSwitch-connected training node when selecting multiple L40S GPUs; confirm PCIe topology and network requirements.

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.

Explore related GPUs

Compare before you reserve

GPUMemory referenceBest-fit decision
L40S48 GB GDDR6Evaluate 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.
H10080 GB HBM3Evaluate 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.
A10040 / 80 GB HBM2eConsider 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.
RTX PRO 6000 Server96 GB GDDR7Evaluate it for visual AI, rendering and inference requiring more than 48 GB of local memory. Benchmark both the AI pipeline and any graphics or video stages.

What changes between configurations

ConfigurationPractical differenceConfirm before ordering
PCIe · VM · 48 GB GDDR6Guest OS and assigned resources within a virtual machineExact card, GPU allocation, peer-to-peer connectivity, CPU and disk limits
PCIe · Bare Metal · 48 GB GDDR6Dedicated physical host configuration; host access follows the confirmed serviceExact card, GPU allocation, peer-to-peer connectivity, CPU and disk limits

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.

NVIDIA ↗
CONFIGURATIONS · RESERVATIONS

Choose your deployment configuration

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.

2 configurations available for enquiry

Capacity confirmation requiredVirtual machine

L40S PCIe

Price in your proposalYour configuration and term are priced in an Arvica proposal. Delivery schedule confirmed in proposal.

Illustrative configuration

GPU
1 × L40S
GPU memory
48 GB GDDR6
vCPU
16
RAM
128 GB
Storage
500 GB
AvailabilityConfirmation requiredRegionConfirm in proposalServiceArvica Cloud
Request this configuration →Check capacity and terms →
Capacity confirmation requiredBare metal

L40S PCIe

Price in your proposalYour configuration and term are priced in an Arvica proposal. Delivery schedule confirmed in proposal.

Illustrative configuration

GPU
1 × L40S
GPU memory
48 GB GDDR6
vCPU
16
RAM
128 GB
Storage
500 GB
AvailabilityConfirmation requiredRegionConfirm in proposalServiceArvica Cloud
Request this configuration →Check capacity and terms →
Arvica follows up on your capacity request with suitable configurations, pricing and delivery options.