RTX PRO 6000 Blackwell Server Edition offers 96 GB GDDR7 for AI and professional visual workloads. Confirm the Server Edition rather than assuming workstation and server configurations are interchangeable.
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
Evaluate 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.
02
When to consider alternatives
Compare L40S when 48 GB is sufficient. Compare B200 or B300 for an HBM-based training or inference configuration with a different memory and interconnect profile.
03
Memory planning
The Server Edition specifies 96 GB GDDR7. GPU memory is distinct from host RAM; both must be sized for model loading, preprocessing and concurrent work.
04
Interconnect and scale
Confirm the server PCIe layout, GPU count and isolation model. A large-memory graphics GPU is not equivalent to an NVLink-connected Blackwell training node.
05
Software compatibility
Check Blackwell support in the CUDA runtime, renderer and inference framework. Verify commercial application licensing separately from the compute allocation.
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.
Evaluate 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 should I check about memory?
The Server Edition specifies 96 GB GDDR7. GPU memory is distinct from host RAM; both must be sized for model loading, preprocessing and concurrent work.
What software should I prepare?
Check Blackwell support in the CUDA runtime, renderer and inference framework. Verify commercial application licensing separately from the compute allocation.
What interconnect is included?
Confirm the server PCIe layout, GPU count and isolation model. A large-memory graphics GPU is not equivalent to an NVLink-connected Blackwell training node.
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.
Evaluate 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.
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 B200 for workloads with a Blackwell-compatible software stack and a clear need for low-precision acceleration. Measure quality as well as throughput when changing precision.
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 · 96 GB GDDR7
Guest OS and assigned resources within a virtual machine
Exact card, GPU allocation, peer-to-peer connectivity, CPU and disk limits
PCIe · Bare Metal · 96 GB GDDR7
Dedicated physical host configuration; host access follows the confirmed service
Exact 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.
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
RTX PRO 6000 Server PCIe
Price in your proposalYour configuration and term are priced in an Arvica proposal. Delivery schedule confirmed in proposal.
Illustrative configuration
GPU
1 × RTX PRO 6000 Server
GPU memory
96 GB GDDR7
vCPU
16
RAM
128 GB
Storage
500 GB
AvailabilityConfirmation requiredRegionConfirm in proposalServiceArvica Cloud
