*[DediGPU](https://dedigpu.com/) — Markdown mirror of [https://dedigpu.com/gpu/rtx-pro-6000](https://dedigpu.com/gpu/rtx-pro-6000) · updated 2026-09-02 · index for LLMs: [llms.txt](https://dedigpu.com/llms.txt) · everything: [llms-full.txt](https://dedigpu.com/llms-full.txt)*

NVIDIA · Blackwell

# Dedicated NVIDIA RTX PRO 6000 Blackwell server, $493 a month.

The whole card — 96 GB of GDDR7 ECC at 1.79 TB/s — with 16 dedicated vCPU, 128 GB of RAM and 1 TB of NVMe, on a 25 Gbit/s port with free egress. Root in under 5 minutes. Priced from a cost sheet you can read below, with zero margin on the server.

- 1× to 8× cards, PCIe 5.0
- 4 regions, same price
- No setup fee, no KYC, crypto balance
- 99.9% SLA credited ×10

- **96 GB** GDDR7 ECC per card
- **1.79 TB/s** memory bandwidth
- **PCIe 5.0** interconnect
- **25 Gbit/s** port, $0 egress
- **600 W** board power, in the cost sheet

The cost sheet

## What an RTX PRO 6000 costs us, per card, per month.

Six lines, added up, rounded up to the next dollar. That is the price. The assumptions are the same for every card and are explained on the [pricing page](https://dedigpu.com/pricing#method).

- Hardware $12,000 card + $2,500 chassis share, over 36 months $402.78
- Power 423 kWh at $0.052 · 720 W, 70% load, PUE 1.15 $22.00
- Rack & cooling PCIe chassis, air $25.00
- Network & IP transit share, one IPv4, port $9.00
- Spares & failures 2% of the card price per year $20.00
- Operations monitoring, on-call, provisioning $14.00
- Total rounded up to the next dollar $492.78 → $493

Cheapest published price we found for the same card: $949 per month at Hyperstack (reserved) (reserved), checked 2 September 2026 — 48% more than our cost. Where the money comes from, then: options with a normal margin, power bought at scale, full racks, no card network. [Read how](https://dedigpu.com/pricing#margin).

Configurations

## One card to eight. The price is linear.

CPU, RAM and NVMe scale with the cards. Cards share the chassis over PCIe 5.0.

| Server | GPU memory | CPU | RAM | NVMe | Network | Per month | 12-month rate |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **1× RTX PRO 6000** | 96 GB | 16 vCPU | 128 GB | 1 TB | 25 Gbit/s | $493 | $443.70 | [Order](https://dedigpu.com/signup?gpu=rtx-pro-6000&n=1) |
| **2× RTX PRO 6000** | 192 GB | 32 vCPU | 256 GB | 2 TB | 25 Gbit/s | $986 | $887.40 | [Order](https://dedigpu.com/signup?gpu=rtx-pro-6000&n=2) |
| **4× RTX PRO 6000** | 384 GB | 64 vCPU | 512 GB | 4 TB | 25 Gbit/s | $1,972 | $1,774.80 | [Order](https://dedigpu.com/signup?gpu=rtx-pro-6000&n=4) |
| **8× RTX PRO 6000** | 768 GB | 128 vCPU | 1024 GB | 8 TB | 25 Gbit/s | $3,944 | $3,549.60 | [Order](https://dedigpu.com/signup?gpu=rtx-pro-6000&n=8) |

Per month for a one-month term; the 12-month rate is the same server on a 12-month term (−10 %). Options — extra NVMe, IPv4, 25 Gbit/s uplink, private VLAN — are added on the order form at the prices on the [pricing page](https://dedigpu.com/pricing#options).

Terms

## Prepay longer, pay less per month.

A longer term costs us less to run — one provisioning, no re-image between tenants, a firmer power forecast — and the difference is passed on. The rate is locked for as long as the server renews.

| Term | Discount | Per month | Charged at order | Saved over the term |
| --- | --- | --- | --- | --- |
| **1 month** | cost sheet | $493.00 | $493.00 | — |
| **3 months** | −3 % | $478.21 | $1,434.63 | $44.37 |
| **6 months** | −6 % | $463.42 | $2,780.52 | $177.48 |
| **12 months** | −10 % | $443.70 | $5,324.40 | $591.60 |

For one RTX PRO 6000; multiply by the card count. Renewal takes the same term at the same rate. [How renewals, grace and suspension work](https://dedigpu.com/docs/terms-and-renewals).

Specifications

## Per card, before you multiply.

- **GPU:** NVIDIA RTX PRO 6000 Blackwell
- **Architecture:** Blackwell
- **GPU memory:** 96 GB GDDR7 ECC
- **Memory bandwidth:** 1.79 TB/s
- **Interconnect:** PCIe 5.0
- **Form factor:** PCIe, dual-slot, passively cooled
- **Board power:** 600 W
- **CPU per card:** 16 dedicated vCPU
- **System RAM per card:** 128 GB ECC
- **Local NVMe per card:** 1 TB
- **Network:** 25 Gbit/s · one IPv4 · $0 egress
- **Cards per server:** 1×, 2×, 4×, 8×
- **Out-of-band:** IPMI console, power control, virtual media

What fits

## Popular models on 1× RTX PRO 6000.

Memory needed = parameters × bytes per parameter × 1.2 (KV cache and runtime). Change the card count above and the table follows. [The full guide](https://dedigpu.com/guides/how-much-vram-to-run-an-llm).

| Model | BF16 | FP8 | INT8 | INT4 |
| --- | --- | --- | --- | --- |
| Llama 3.1 8B | 20 GB | 10 GB | 10 GB | 5 GB |
| Mistral Small 3.1 24B | 58 GB | 29 GB | 29 GB | 15 GB |
| Gemma 3 27B | 65 GB | 33 GB | 33 GB | 17 GB |
| Qwen3 32B | 77 GB | 39 GB | 39 GB | 20 GB |
| Llama 3.3 70B | 168 GB | 84 GB | 84 GB | 42 GB |
| Qwen2.5 72B | 173 GB | 87 GB | 87 GB | 44 GB |
| Llama 4 Scout · 109B MoE | 262 GB | 131 GB | 131 GB | 66 GB |
| Mixtral 8x22B · 141B MoE | 339 GB | 170 GB | 170 GB | 85 GB |
| Qwen3 235B-A22B · MoE | 564 GB | 282 GB | 282 GB | 141 GB |
| Llama 3.1 405B | 972 GB | 486 GB | 486 GB | 243 GB |
| DeepSeek-V3 / R1 · 671B MoE | 1611 GB | 806 GB | 806 GB | 403 GB |

Green fits in 96 GB with a working KV cache; red does not. FP8 needs Ada, Hopper, Blackwell or MI300X silicon. MoE models count every expert.

Software

## Pre-installed before you log in.

Pick the system and the template on the order form; serving templates pull the model you name before first boot. [Every template, with its ports](https://dedigpu.com/templates).

### Operating systems

- **Ubuntu 24.04 LTS** Kernel 6.8 · the default for every template
- **Ubuntu 22.04 LTS** Kernel 5.15 HWE · for stacks pinned to CUDA 11.8 / 12.1
- **Debian 12** Bookworm · minimal, driver from the vendor repository
- **Rocky Linux 9** RHEL-compatible · Slurm, enterprise and HPC stacks
- **Windows Server 2022** Standard · RDP on :3389 · licence billed at our rate · $28/mo licence

### Templates

- Bare OS + driver
- PyTorch 2.7
- TensorFlow 2.18 + JAX 0.5
- Jupyter Lab
- vLLM 0.9 + model
- SGLang 0.4 + model
- Text Generation Inference 3 + model
- Ollama + Open WebUI + model
- ComfyUI
- Stable Diffusion WebUI Forge
- Render node
- Docker + GPU toolkit
- Kubernetes node (k3s)
- Slurm worker

Root over SSH, IPMI console and an API token come with every server. [Access docs](https://dedigpu.com/docs/ssh-and-root-access).

What it is for

## The jobs this card is right for.

- ### Image & video generation
  Flux, SDXL, Wan and HunyuanVideo on GDDR7 cards that were built for exactly this. ComfyUI is one click away.
  Also a fit. Our first pick here is the [RTX 5090](https://dedigpu.com/gpu/rtx-5090) at $163/mo.
- ### Rendering & simulation
  96 GB of ECC memory for scenes that do not fit anywhere else. Blender, Octane, Redshift and Houdini out of the box.
  Our recommendation for this workload.

Regions

## Deployable in four regions, at the same price.

- **Reykjavík** 18 ms London · 40 ms New York 100% geothermal + hydro
- **Helsinki** 27 ms Frankfurt · 36 ms London 100% wind + hydro
- **Amsterdam** 7 ms Frankfurt · 12 ms London 100% wind
- **Singapore** 38 ms Tokyo · 45 ms Sydney Grid + certified RECs

[Latency tables, power and cooling per region](https://dedigpu.com/regions).

Alternatives

## Cards near this price.

Every one billed the same way. [Compare all 13 side by side](https://dedigpu.com/gpu).

- [L40S 48 GB GDDR6 ECC · Ada Lovelace $356 /mo · at cost](https://dedigpu.com/gpu/l40s)
- [A100 80 GB 80 GB HBM2e · Ampere $654 /mo · at cost](https://dedigpu.com/gpu/a100-80)
- [RTX 6000 Ada 48 GB GDDR6 ECC · Ada Lovelace $331 /mo · at cost](https://dedigpu.com/gpu/rtx-6000-ada)
- [RTX A6000 48 GB GDDR6 ECC · Ampere $208 /mo · at cost](https://dedigpu.com/gpu/a6000)

Guides

## Read before you rent.

- [Image & video RTX 4090 vs RTX 5090 for Stable Diffusion, Flux and video generation Memory, FP8 and FP4, real workflow fit for SDXL, Flux.1, Wan and HunyuanVideo, and the monthly price of each card on a dedicated server. 2 September 2026 · 7 min read](https://dedigpu.com/guides/rtx-4090-vs-rtx-5090-for-diffusion)
- [Image & video Run ComfyUI on a rented GPU server: setup, checkpoints, secure access Order a server with the ComfyUI template, reach it safely through an SSH tunnel, bring your checkpoints, add custom nodes, and keep it running for months. 2 September 2026 · 7 min read](https://dedigpu.com/guides/run-comfyui-on-a-rented-gpu-server)

Questions

## About the RTX PRO 6000 server.

**How much does a dedicated RTX PRO 6000 server cost per month?**

$493 per card per month for a one-month term, which is the sum of the six lines of its cost sheet rounded up to the dollar: hardware over 36 months, power at our contract rate, rack and cooling, network and IP, spares, operations. A 3, 6 or 12-month term is 3, 6 or 10 % cheaper per month ($443.70 on 12 months). No setup fee, no egress charge, no contract beyond the term.

**Is the RTX PRO 6000 dedicated or shared?**

Dedicated. You rent a physical server with the whole card: no vGPU profile, no time-slicing, no other tenant on the machine. nvidia-smi shows the full 96 GB because you have the full 96 GB. The CPU cores, the RAM and the NVMe are yours too, and so is the IPMI console.

**How many RTX PRO 6000 cards can I have in one server?**

1, 2, 4 or 8, at $493 per card: the chassis share is already in each card's cost, so the price is linear. Cards connect over PCIe 5.0. For NVLink all-to-all between cards, look at the SXM models.

**Which LLMs fit on an RTX PRO 6000?**

With the fit rule (parameters × bytes per parameter × 1.2), one card holds up to Qwen2.5 72B at FP8 and up to Mixtral 8x22B · 141B MoE at INT4. An eight-card server (768 GB) holds up to Llama 3.1 405B at FP8. The order form checks the model you name against the server you pick before you pay.

**Which operating systems and templates run on the RTX PRO 6000?**

Ubuntu 24.04 LTS, Ubuntu 22.04 LTS, Debian 12, Rocky Linux 9, Windows Server 2022. Templates: Bare OS + driver, PyTorch 2.7, TensorFlow 2.18 + JAX 0.5, Jupyter Lab, vLLM 0.9, SGLang 0.4, Text Generation Inference 3, Ollama + Open WebUI, ComfyUI, Stable Diffusion WebUI Forge, Render node, Docker + GPU toolkit, Kubernetes node (k3s), Slurm worker. Serving templates pull the model you name before first boot.

**How long does provisioning take, and can I cancel?**

A single-card server is online in under 5 minutes, an eight-card node in under fifteen minutes, in any of the four regions. The term renews automatically from your balance at the locked price; turn renewal off and the server stops at the end of the paid term, or cancel at any time. No notice period.

**What is the cheapest RTX PRO 6000 rental elsewhere?**

The lowest published monthly price we found for the same card on 2 September 2026 was $949 at Hyperstack (reserved) (reserved; hourly rates converted at 730 hours). That is 48 % more than our cost sheet. We re-check every month and print the date.

## Order an RTX PRO 6000 now.

$493 a month at cost, online in under 5 minutes. Create an account, top up in crypto, pick a region.

[Order this server](https://dedigpu.com/signup?gpu=rtx-pro-6000) See the cost sheet