3D Rendering with NVIDIA A100: Performance, Pricing & Workflows

Are you tired of waiting for renders to complete?

3D rendering with NVIDIA A100 can dramatically speed up your projects. This powerful data center GPU offers incredible performance for a variety of rendering tasks.

But is it the right choice for you?

In this article, we will explore why the NVIDIA A100 is the ideal choice for cloud-based rendering. Besides, we’ll discuss its performance, pricing and practical workflows. Let’s get started!

What is NVIDIA A100 GPU?

The NVIDIA A100, built on the Ampere architecture, is a data center GPU. This GPU is not a traditional graphics card. It’s designed for massive computational tasks which makes it perfect for heavy rendering. Its architecture includes Tensor Cores and a huge memory bandwidth that boosts rendering speeds dramatically.

What Makes A100 GPU Ideal for 3D Rendering?

Here are a few considerable factors that make NVIDIA A100 GPU worth it for the rendering tasks.

High memory and bandwidth for large scenes

A100 comes with 40 GB or 80 GB HBM2e. The 80 GB model delivers over 2 TB per second of memory bandwidth, which keeps texture streaming, large BVH structures and heavy frame buffers moving.

When your scenes carry 16K textures, displacement, hair and heavy instancing, bandwidth and VRAM headroom reduce out-of-core thrashing and improve stability.

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Multi-Instance GPU for concurrency

MIG lets you partition a single A100 into up to seven isolated GPU instances. Each slice has guaranteed memory and compute, which is ideal for denoising, wedges or tiled frames. On a cluster, MIG raises job density without resource contention.

Acceleration for AI-assisted steps

Modern pipelines combine path tracing with AI denoising and upscaling. NVIDIA’s OptiX denoiser is designed to dramatically reduce the time to reach a visually noiseless image, which means you can use fewer samples per pixel for the same quality bar.

The A100 is not a graphics-first GPU and does not include dedicated RT cores for interactive ray tracing. If you need high-FPS look development, an RTX 6000 Ada or L40S will feel faster. If you want offline frames with strong AI denoising and many concurrent jobs, the A100 is a strong choice.

Large L2 cache and fast data paths

A100’s sizable L2 cache and dual copy engines keep geometry, textures and BVH updates flowing with fewer trips to HBM. You can overlap transfers and compute, which steadies frame times and speeds final renders on heavy scenes.

Multi-GPU scaling with NVLink and PCIe 4.0

On SXM systems, NVLink enables high-throughput peer-to-peer sharing across GPUs, so large scenes and tile buckets scale cleanly across multiple cards. On PCIe, Gen4 bandwidth improves host I/O, asset streaming and checkpoint writes.

Enterprise-grade stability

ECC HBM2e and mature RAS features reduce crash risk during 12–48 hour jobs. Combined with robust telemetry and headless operation in data centers, A100 delivers consistent results for deadline-driven production work.

NVIDIA GPUs for Rendering: A100 vs L40S vs RTX 6000 Ada vs H100

Let’s compare specs and real-world fit for offline frames, interactive lookdev and AI steps to pick the right card.

Spec snapshot (rendering-relevant)

Below is a side-by-side look at A100, L40S, RTX 6000 Ada and H100 cloud GPUs specs.

Factor	NVIDIA A100	NVIDIA L40S	NVIDIA RTX 6000 Ada	NVIDIA H100
VRAM (type, ECC)	40 GB or 80 GB HBM2e, ECC	48 GB GDDR6, ECC	48 GB GDDR6, ECC	80 GB HBM3, ECC
Memory bandwidth	Up to ~2.0 TB/s (80 GB)	High, ~800–900 GB/s	~960 GB/s	Up to ~3.0 TB/s (SXM)
CUDA / Tensor / RT	CUDA + Tensor, no RT cores	CUDA + Tensor + RT cores	CUDA + Tensor + RT cores	CUDA + Tensor, no RT cores
NVLink	Yes on SXM (NVLink ~600 GB/s per GPU); PCIe: no	Typically no	No	Yes on SXM (NVLink up to ~900 GB/s per GPU); PCIe: no
MIG (Multi-Instance GPU)	Yes, up to 7 slices	No	No	Yes, up to 7 slices
PCIe generation	PCIe 4.0 (PCIe model)	PCIe 4.0	PCIe 4.0	PCIe 5.0 (PCIe model)
Form factor options	SXM4 and PCIe	PCIe	PCIe	SXM5 and PCIe
Resizable BAR	Platform dependent	Platform dependent	Platform dependent	Platform dependent
Typical power	PCIe ~250–300 W, SXM higher	~300–350 W	~300 W	PCIe ~350 W, SXM higher
OptiX support	Yes (denoiser, ray API on CUDA path)	Yes (plus RT cores for ray tracing)	Yes (plus RT cores)	Yes (denoiser, CUDA path)
Renderer ecosystem	Broad CUDA coverage (Cycles, Redshift, V-Ray GPU, Arnold GPU, Octane)	Broad, strong RT viewport support	Broad, strong RT viewport support	Broad CUDA coverage, best for heavy compute
Virtualization	MIG, vGPU options on DC SKUs	vGPU options on DC SKUs, no MIG	vGPU options on select SKUs, no MIG	MIG, vGPU options on DC SKUs
Telemetry	NVML, DCGM	NVML, DCGM	NVML	NVML, DCGM
Cloud availability	Broad across providers	Growing availability	Limited as dedicated SKU	Limited / capacity-constrained
Lifecycle status	Active, previous gen DC workhorse	Current gen DC GPU	Current gen workstation-class	Current flagship DC GPU

Usage-fit matrix (1–5 score, higher is better)

Rendering task	A100	L40S	RTX 6000 Ada	H100
Offline path tracing throughput	5	4	4	5
Large-scene comfort (VRAM + bw)	5	3	3	5
Interactive RT viewport lookdev	2	5	5	2
AI denoise / upscale steps	5	5	5	5
Concurrency for many small jobs (MIG, tiles)	5	2	2	5
Multi-GPU scaling with shared data	5 (NVLink SXM)	3 (PCIe only)	3 (PCIe only)	5 (NVLink SXM)
Out-of-core tolerance	4-5	3	3	5
Perf per dollar (varies by pricing)	4	4-5	4-5	3
Cloud availability / burstability	5	4	3	3

Summary: A100 Vs. L40S vs. RTX 6000 Ada vs. H100

• Choose A100 when you render large offline frames, rely on AI denoising and want high job density via MIG. Excellent multi-GPU scaling on SXM with NVLink.
• Choose L40S when your artists live in ray-traced viewports and you still need solid offline throughput. Great real-time lookdev because of RT cores.
• Choose RTX 6000 Ada for workstation-centric pipelines that prioritize interactive RT lookdev and DCC responsiveness, with strong offline capability when needed.
• Choose H100 when you combine very heavy renders with AI training or inference, or when you need the highest memory bandwidth, MIG and NVLink for multi-node scaling.

If you want to compare the A100 with other gpu options in details, see our guide to the 7 Best GPUs for 3D Rendering & Video Editing.

Renting vs Buying NVIDIA A100 GPU: A Price Comparison Table

Now that you’re convinced to use NVIDIA A100 GPU for 3D rendering, should you buy or rent the high-performance GPU?

Break downing the complete price comparison table of renting vs buying NVIDIA A100 80 GB GPU. Here, we’ve assumed the Neural/AI-assisted and Hybrid (render + AI post) workloads:

Item	Value (assumed)	Notes
Cloud rent (low-cost providers)	$1.74–$1.79 / GPU-hr	RunPod A100 SXM from $1.74/hr; Lambda A100 80 GB SXM $1.79/hr.
Cloud rent (hyperscaler effective per-GPU)	~$3.02 / GPU-hr	AWS p4d.24xlarge is ~$24.15/hr for 8×A100 → ≈$3.02/GPU-hr (ex-storage/egress).
Buy price (A100 80 GB PCIe)	$17,200 (GPU)	Example PNY A100 80 GB PCIe list (EOL listing; market varies).
Server BOM (CPU, RAM, NVMe, chassis, etc.)	$6,800	Typical 1×GPU node; adjust to your quote.
Amortization	24 months	Straight-line.
Maintenance & spares	10% of capex / year	Standard budgetary placeholder.
Rack/colo	$150 / month	If colocated; set to $0 if not applicable.
Electricity	$0.12 / kWh, PUE 1.5	Replace with your rate & data-center PUE.
Node power (avg under load)	~500 W	~300 W GPU TDP (A100 80 GB PCIe) + ~200 W system headroom.
Hours / month for utilization math	≈730 hrs	Matches your “≈219 GPU-hrs at 30%” baseline.
Neural/AI-assisted offline	6 min/frame	Path tracing + AI denoise (fewer samples to hit the same quality).
Hybrid (render + AI post)	7 min/frame	Path tracing + ~1 extra minute for AI upscale/cleanup.

Monthly Ccost: Renting vs Buying (Single A100)

Utilization (GPU busy %)	Rent (low-cost, $1.74/hr)	Rent (low-cost, $1.79/hr)	Rent (AWS eff., $3.02/hr)	Buy (amortized)
30% (≈219 GPU-hrs)	$381	$392	$661	$1,370
60% (≈438 GPU-hrs)	$762	$785	$1,323	$1,389
70% (≈511 GPU-hrs)	$889	$915	$1,543	$1,396
85% (≈621 GPU-hrs)	$1,080	$1,111	$1,874	$1,406

Our Recommendations:

• If your utilization is spiky or project-based, rent A100s from a low-cost provider; you’ll pay for only what you render and keep cost per 1,000 frames low.
• If you run steady nightly renders and must stay on a hyperscaler, consider buying when your node utilization consistently exceeds ~65–70%.
• If you want a safe middle ground, keep a small owned or reserved baseline for dailies and burst into clouds during crunch weeks.

Pro Tip: Add a single row for storage/licensing (e.g., +$30–$50 per 1,000 frames) if you want all-in numbers. That line item won’t change the relative picture much, but it improves budget fidelity.

Render complex scenes faster with NVIDIA A100

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How Should You Architect a Lean A100 Render Stack?

Keep the first build simple. You need a dependable pattern you can repeat across projects.

Compute layout:

• Single A100 node for baseline tests and light parallelism
• Dual A100 node with NVLink for heavier frames and better scaling, using the 600 GB per second link to keep shared data flowing
• MIG slices for concurrency when jobs are small or embarrassingly parallel

Drivers and containers:

Use the supported NVIDIA driver and CUDA pair for your render engine. Containerize the stack so artists and TDs get the same environment every time.

Render manager:

Pick a manager your team knows. Keep job submission scripts in your repo and version them with the project.

Storage path:

• Assets in object storage for authoritative copies
• A local NVMe cache on each render node for hot assets and textures
• Frames written to fast network storage with a lifecycle rule that moves approved frames to cheaper tiers

Licensing and security:

Run a small license server in the same region. Separate projects by account or namespace. Store secrets in a vault. Encrypt data in transit and at rest.

If you prefer a ready pattern, AceCloud can provision A100 instances with the right drivers, help containerize your renderer and set up a clean storage path on day one.

Turn Your Next Render into same-day Delivery

AceCloud’s NVIDIA A100 instances are ready to spin up in minutes with preconfigured drivers, containerized render engines and fast NVMe caching. So, your team can focus on creating, not troubleshooting.

• Run AI denoising, offline path tracing, and concurrent jobs with ease.
• Scale instantly when deadlines hit.
• Predict costs with transparent, finance-friendly pricing.

Get started now:

• Book a 15-min consult to see if A100 fits your pipeline.
• Or claim your free 2-hour benchmark and see real-world results before you commit.

Talk to AceCloud Today +91-789-789-0752 ! Ship frames faster, stay on budget and keep clients happy.