LLM inference
Serve chat, agent and coding models in your own environment.
- FP16
- ~10B
- FP8
- ~20B
- FP4
- ~40B
XcellGPU|NVIDIA RTX PRO 4500 Blackwell
32 GB of Blackwell in a single slot.
The Server Edition card at 165 W, with FP4 Tensor Cores, RT cores and three video encoders. Run inference, RAG, rendering, video AI and virtual workstations on 1, 2 or 4 GPUs, hosted in our Mumbai Tier IV data centre. Every configuration is priced on quote.
32 GB GDDR7 · 800 GB/s
5th-gen Tensor Cores with FP4
MIG: up to 2 × 16 GB
vGPU: RTX vWS and vPC
Mumbai Tier IV, MeitY empanelled
Price on quote, INR with GST
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NVIDIA RTX PRO 4500 Blackwell
NVIDIA RTX PRO 4500 BlackwellSpecs per NVIDIAServer Edition · Nov 2026
165 W · single slotPassive, server-fan cooled
RTX PRO 4500 · Server Edition
BLACKWELL
01One slot, 165 W
Drawing to scale-ish
Memory32 GBGDDR7
Bandwidth800 GB/s256-bit
FP4 Tensor1.6 PF‡5th gen
PriceOn quoteINR + GST
Price on quote · every configMumbai Tier IV data centreMeitY empanelledISO 27001 · 27017 · 27018 · 20000-1SOC 2 Type I & II24×7 NOC & SOC
Overview
A Blackwell GPU in one PCIe slot.
NVIDIA makes the RTX PRO 4500 Blackwell in two editions. We host the one built for servers.
The RTX PRO 4500 Blackwell Server Edition is a single-slot, full-height, full-length PCIe card with 32 GB of GDDR7 memory and a 165 W power limit. It has no fan of its own: the server's fans push air through its heatsink, which is why it can sit densely in standard rack servers.
It carries the same Blackwell building blocks as larger cards — fifth-generation Tensor Cores with FP4, fourth-generation RT cores, and NVENC and NVDEC video engines — at a size and power draw that suit inference, RAG, video analytics, rendering and virtual workstations. For Indian teams, that means a modern GPU you can run in Mumbai without paying for HBM you don't need.
Source: NVIDIA RTX PRO 4500 Blackwell Server Edition and Workstation Edition product pages, nvidia.com, retrieved 12 Nov 2026.
Server EditionWhat we host
- Single slot, FHFL, passive cooling
- 165 W · one PCIe CEM5 16-pin
- 800 GB/s · 3 NVENC · 3 NVDEC
- MIG 2 × 16 GB · vGPU · confidential computing
Workstation EditionFor desks
- Dual slot, active fan
- 200 W maximum
- 896 GB/s · 2 NVENC · 2 NVDEC
- 4 × DisplayPort 2.1 outputs
Specifications
Every number, as NVIDIA states it.
Server Edition figures from NVIDIA's product page. Where NVIDIA does not say whether a Tensor figure is sparse or dense, we mark it ‡.
32 GBGDDR7 · 256-bit
1.6 PF‡FP4 Tensor Core
10,496CUDA cores
165 WMax power · single slot
| Specification | RTX PRO 4500 · Server Edition |
|---|---|
| Architecture | NVIDIA Blackwell |
| GPU memory | 32 GB GDDR7 · 256-bit interface |
| Memory bandwidth | 800 GB/s |
| CUDA cores | 10,496 |
| Tensor Cores | 5th generation, with FP4 |
| RT Cores | 82 · 4th generation · 154 TFLOPS peak RT |
| FP4 Tensor Core | 1.6 PFLOPS ‡ |
| FP8 Tensor Core | 811 TFLOPS ‡ |
| FP16 / BF16 Tensor Core | 406 TFLOPS ‡ |
| TF32 Tensor Core | 203 TFLOPS ‡ |
| FP32 (single precision) | 51 TFLOPS |
| Video engines | 3 × NVENC · 3 × NVDEC |
| Interconnect | PCI Express 5.0 x16 · no NVLink |
| Multi-Instance GPU | Up to 2 MIG instances of 16 GB |
| Virtual GPU | NVIDIA RTX Virtual Workstation (vWS) and Virtual PC (vPC), including MIG-backed time-slicing |
| Confidential computing | Supported |
| Power | 165 W max · 1 × PCIe CEM5 16-pin |
| Form factor · cooling | Single-slot FHFL, 4.4″ × 10.5″ · passive |
| Display outputs | Not listed for the Server Edition (Workstation Edition: 4 × DisplayPort 2.1) |
‡ NVIDIA does not state whether these Tensor figures are sparse or dense. PF = PFLOPS, TF = TFLOPS. NVIDIA also states, for this card, over 5× the small-model inference performance of the previous-generation L4 — a vendor claim we have not measured. Specifications retrieved from nvidia.com on 12 Nov 2026; verify before purchase.
Use cases
What 32 GB of Blackwell is good at.
Model sizes below are indicative: weights plus a cautious 50% headroom for KV cache, activations and runtime, on one 32 GB GPU. Short prompts fit more; long contexts and big batches fit less.
Fine-tuning small models
LoRA and QLoRA on 7–8B models fit on one card; larger models need 2–4 GPUs or a bigger card.
Batch size and sequence length decide the fitRAG and embeddings
Run embedding, reranker and a mid-size generator side by side, or split them across two MIG instances.
NVIDIA cites up to 50× vector-database speed vs CPU-only3D, rendering and Omniverse
82 RT cores for path-traced rendering, digital twins and OpenUSD pipelines on a server-room card.
Scenes over 32 GB → RTX PRO 6000Video transcoding and analytics
Three NVENC and three NVDEC engines decode camera feeds, run vision models and re-encode — on one card.
Stream counts depend on codec and resolutionVDI and virtual workstations
RTX vWS for designers and engineers, vPC for knowledge workers, with MIG-backed time-slicing.
NVIDIA vGPU licences quoted separately*CAD and CAE
Interactive CAD, simulation pre- and post-processing and visualisation for engineering teams working remotely.
ISV certification: check with your software vendorConfigurations
One, two or four GPUs. Priced on quote.
Starting points for a dedicated RTX PRO 4500 server. CPU, memory and NVMe can be changed; availability and lead time are confirmed in your quotation.*
1×
RTX PRO 4500 · 1 GPU
32 GB GPU memory- vCPU
- 16
- RAM
- 64 GB
- NVMe
- 1 TB
- Network
- 10 Gbps*
One model up to ~40B at FP4 or ~20B at FP8, an embedding service, or two MIG instances.
Price on quoteAvailability: confirm*
2×
RTX PRO 4500 · 2 GPUs
64 GB GPU memory- vCPU
- 32
- RAM
- 128 GB
- NVMe
- 2 TB
- Network
- 10 Gbps*
A 32B model at FP8 split over two GPUs, or inference plus RAG on separate cards.
Price on quoteAvailability: confirm*
4×
RTX PRO 4500 · 4 GPUs
128 GB GPU memory- vCPU
- 64
- RAM
- 256 GB
- NVMe
- 4 TB
- Network
- 25 Gbps*
A 70B model at FP8 across four GPUs, a render node, or up to eight MIG instances.
Price on quoteAvailability: confirm*
| Configuration | GPU memory | vCPU | RAM | NVMe | MIG instances (max) | Availability | Price |
|---|---|---|---|---|---|---|---|
| 1 × RTX PRO 4500 | 32 GB | 16 | 64 GB | 1 TB | 2 | Confirm* | On quote |
| 2 × RTX PRO 4500 | 64 GB | 32 | 128 GB | 2 TB | 4 | Confirm* | On quote |
| 4 × RTX PRO 4500 | 128 GB | 64 | 256 GB | 4 TB | 8 | Confirm* | On quote |
| 8 × RTX PRO 4500 | 256 GB | Sized with you | 16 | On request* | On quote | ||
* vCPU, RAM, NVMe and network are suggested starting points, not stock lines; XcellHost confirms the exact server, availability, lead time and term in writing. The cards link over PCIe Gen 5 — there is no NVLink — so multi-GPU jobs split work across PCIe. MIG instance counts are 2 per GPU as NVIDIA states.
Tool · workload picker
Is RTX PRO 4500 the right GPU for this?
Pick a workload and a scale. The picker suggests a configuration — or tells you honestly when another GPU fits better. A starting point, not a benchmark.
1Workload
2Model size
Tool · VRAM fit calculator
Parameters × precision. How many cards?
Weights take parameters × bytes per parameter. Serving also needs memory for the KV cache, activations and runtime, entered here as an overhead. The 50% default is a cautious, illustrative allowance.
B
Precision · bytes per parameter
Illustrative · 50% is a cautious default; lower it for short prompts
Memory needed
48GB
RTX PRO 4500 cards (32 GB each)
Same model on other GPUs
Cards needed = ⌈memory needed ÷ 32 GB⌉, assuming the model is split across GPUs (tensor or pipeline parallel) over PCIe. Capacity only, not speed. Indicative — confirm with a test on your own model.
Compare
RTX PRO 4500 next to RTX PRO 6000, L4, L40S and H200.
Specifications only, per NVIDIA. Pick by memory first: if your model or scene doesn't fit in 32 GB, the extra Tensor throughput of a smaller card won't help.
| Spec | RTX PRO 4500 SE | RTX PRO 6000 SE | L4 | L40S | H200 |
|---|---|---|---|---|---|
| Architecture | Blackwell | Blackwell | Ada Lovelace | Ada Lovelace | Hopper |
| GPU memory | 32 GB GDDR7 | 96 GB GDDR7 | 24 GB | 48 GB GDDR6 | 141 GB HBM3e |
| Bandwidth | 800 GB/s | 1.6 TB/s | 300 GB/s | 864 GB/s | 4.8 TB/s |
| FP4 Tensor | 1.6 PF ‡ | 4 PF ‡ | — | — | — |
| FP8 Tensor | 811 TF ‡ | Not listed | 485 TF sparse | 1,466 TF sparse | 3,958 TF sparse (SXM) |
| Max power | 165 W | 400–600 W | 72 W | 350 W | Up to 700 W (SXM) |
| Form factor | PCIe · 1 slot | PCIe · 2 slot | PCIe · 1 slot low-profile | PCIe · 2 slot | SXM or PCIe NVL |
| MIG | Up to 2 × 16 GB | Up to 4 × 24 GB | None | None | Up to 7 |
| NVLink | None · PCIe Gen 5 | None · PCIe Gen 5 | None · PCIe Gen 4 | None · PCIe Gen 4 | 900 GB/s |
| NVENC · NVDEC | 3 · 3 | 4 · 4 | 2 · 4 | 3 · 3 | — · 7 |
| RT cores | 82 | 188 | — | 142 | — |
| Fits best | Inference to ~40B at FP4, RAG, video, vWS | 30–70B, big scenes, Omniverse | 7–9B models, video at 72 W | Graphics plus AI, 48 GB | 70B+ inference, training |
| From XcellHost | On quote · confirm* | Available | Available | On request | Available |
‡ Sparse or dense not stated by NVIDIA. "Sparse" assumes 2:4 structured sparsity; dense is typically half. "—" = not offered or not listed. Figures per NVIDIA product pages, retrieved 30 Oct – 12 Nov 2026.
Compare all 23 NVIDIA GPUs →Software stack
Drivers to model server, ready when you log in.
We install and test the layers you ask for. You keep root access to change anything.
05
Model serving
NVIDIA NIM microservices, Triton Inference Server, vLLM and TensorRT-LLM.
04
Frameworks and images
PyTorch and other framework containers from the NVIDIA NGC catalogue, or your own images.
03
Containers and orchestration
Docker with the NVIDIA Container Toolkit, Kubernetes with the GPU Operator, or Slurm.*
02
CUDA and libraries
CUDA toolkit, cuDNN and NCCL matched to the driver; Omniverse and OptiX for 3D.
01
Driver, MIG and vGPU
NVIDIA data-centre driver, MIG profiles set to your split, and vGPU software for vWS or vPC desks.*
* NVIDIA AI Enterprise and NVIDIA vGPU are licensed separately by NVIDIA; licence costs are quoted with your server. Kubernetes and Slurm set-up is scoped per project.
Hosted in India
Your GPUs, data and models stay in Mumbai.
RTX PRO 4500 servers run in XcellHost's Tier IV data centre in Mumbai. Prompts, training data and model weights stay in India unless you choose otherwise — helpful when DPDP Act obligations or a sector regulator call for local processing.
- Tier IV data centre, Mumbai · MeitY empanelled
- ISO 9001, 27001, 27017, 27018 and 20000-1 · SOC 2 Type I and II
- 24×7 NOC and SOC watching power, cooling, hardware and access
- Billed in INR with a GST invoice from an Indian company, since 1999
Request a quote
Build your server list. We price it.
Add configurations and extras to your quote list, then send it. We reply with availability, lead time and an INR quotation with GST within one business day.*
Your quote list0 items
Add a configuration
Extras (indicative · excl. 18% GST)
Extra NVMe storage₹6 per GB a monthGB
Public IPv4 addresses₹299 per IP a monthIPs
GPU serversPrice on quote
Extras, indicative / month₹0
Extras: Indicative · excl. 18% GST. GPU servers are always priced on quote.
SECURITY & COMPLIANCE
Security And Compliance
Plan access, isolation and data handling for your dedicated GPU workload with the XcellHost team.
Private Access
Agree private networking and access controls for your model weights, applications and datasets.
Workload Isolation
Choose dedicated GPU capacity or supported MIG partitions to suit your workload.
Data Location
Confirm hosting, backup and recovery locations in your deployment agreement.
WHY XCELLHOST
Why XcellHost For RTX PRO 4500 Blackwell
INR Billing
Get a configuration-specific quotation with a GST invoice.
Hosted In India
GPU servers hosted in Mumbai for Indian workloads.
GPU Stack Support
Help with drivers, frameworks and inference servers.
Scale Your Configuration
Choose one, two or four GPUs to suit your model and workload.
24/7 Operations
Infrastructure support from the XcellHost operations team.
One Partner
GPU hosting, configuration and support through one team.
FAQ
RTX PRO 4500, answered.
Every RTX PRO 4500 configuration is priced on quote. GPU supply and pricing move week to week, and the total depends on GPU count, CPU, memory, storage, network and term. We send an INR quotation with GST, including hosting and 24×7 operations, usually within one business day.*
The Server Edition. It is single-slot and passively cooled at 165 W, built for rack servers. The Workstation Edition is a dual-slot, fan-cooled 200 W card with four DisplayPort 2.1 outputs, meant for desks.
Indicatively, about 40B parameters at FP4, 20B at FP8 or 10B at FP16, keeping a cautious 50% headroom over the weights for KV cache and runtime in 32 GB. Short contexts allow more; long contexts or large batches allow less. The VRAM fit calculator on this page does the sum for any model.
Yes, for small models. LoRA and QLoRA on 7–8B models fit on one card. Full fine-tuning or larger models need more memory — two to four RTX PRO 4500s, or a 96 GB RTX PRO 6000 or an H200.
No. The cards connect over PCI Express 5.0 x16. Multi-GPU inference works by splitting the model across cards over PCIe, which suits inference better than large training runs. For NVLink, look at H200 or B300.
Multi-Instance GPU splits one card into isolated GPUs with their own memory. NVIDIA states up to two 16 GB MIG instances on the RTX PRO 4500 Server Edition — so a 4-GPU server can give up to eight isolated instances to different teams or services.
Yes. NVIDIA supports RTX Virtual Workstation (vWS) and Virtual PC (vPC) on this card, including MIG-backed time-slicing. NVIDIA vGPU licences are separate; we include them in your quote if you need them.
Against the L4 (24 GB, 72 W) it adds 8 GB, FP4 support, MIG and a higher power budget; NVIDIA claims over 5× L4 small-model inference performance. The RTX PRO 6000 Blackwell has three times the memory (96 GB) and about twice the bandwidth, for 30–70B models and large scenes.
In XcellHost's Tier IV data centre in Mumbai, which is MeitY empanelled and watched by our 24×7 NOC and SOC. Your data, prompts and model weights stay in India unless you choose otherwise.
Whatever you ask for: the NVIDIA driver and CUDA at minimum, plus PyTorch images, NVIDIA NIM, Triton, vLLM or TensorRT-LLM, Docker or Kubernetes with the GPU Operator. You keep root access. NVIDIA AI Enterprise and vGPU licences are quoted separately.