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What Are 64GB GPUs? Compare 64GB GPU Specs, Lists, Price and Hosting

A 64GB GPU is a graphics processing unit (GPU) that provides 64 gigabytes of video memory (VRAM). Unlike standard consumer GPUs (which usually range from 4GB to 24GB), 64GB GPUs are typically found in datacenter accelerators or achieved through multi-GPU setups (e.g., 4 × 16GB or 2 × 32GB cards).

These GPUs are designed for memory-intensive workloads, where smaller GPUs run out of VRAM. With 64GB, you can handle large datasets, high-resolution textures, and complex AI/ML models without hitting performance bottlenecks.

64GB GPU Model Lists (NVIDIA & AMD)

Brand Series	Models (Official Link)	Release Year	Official Positioning / Description	Market Price (USD)
NVIDIA GeForce	2 × RTX 5090 (32GB each)	2025	Next-gen GeForce Blackwell GPUs, combined for extreme gaming, AI inference, and rendering power	$3,000 – $4,000 each $6,000–$8,000 total
NVIDIA Quadro	4 × RTX A4000 (16GB each)	2021	Professional visualization and rendering, optimized for CAD, content creation, and multi-GPU setups	~$1,000 each → ~$4,000 total
NVIDIA Tesla	4 × Tesla V100 16GB PCIe	2017	HPC, AI training, and scientific computing with NVLink scaling	~$2,500 each → ~$10,000 total
AMD Radeon Pro	2 × Radeon Pro W6800 (32GB each, split allocation)	2021	Professional workstation GPU for CAD, BIM, and visualization; combined in server racks	~$2,000 each → ~$4,000 total

64GB GPU Specifications Comparison

GPU Model	Architecture	CUDA Cores (Total)	Memory Type	Memory Capacity (Total)	Memory Bandwidth (Per GPU)	Core Frequency (Base/Boost)	TDP (Total)	Interface	FP32 Performance (Total)	Tensor Cores (Total)	PCIe
2 × RTX 5090 (32GB each)	Blackwell	~32,000 × 2 ≈ 64,000	GDDR7	64GB (2 × 32)	~1.5 TB/s (per GPU)	~2.2 / 2.8 GHz	~600 × 2 ≈ 1200W	PCIe 5.0	~200 × 2 = 400 TFLOPS	21,760x2 = 43520	x16
4 × RTX A4000 (16GB each)	Ampere	6,144 × 4 = 24,576	GDDR6 ECC	64GB (4 × 16)	448 GB/s (per GPU)	1.4 / 1.7 GHz	140 × 4 = 560W	PCIe 4.0	19.2 × 4 = 76.8 TFLOPS	192 × 4 = 768	x16
4 × Tesla V100 (16GB each)	Volta	5,120 × 4 = 20,480	HBM2	64GB (4 × 16)	900 GB/s (per GPU)	1.2 / 1.45 GHz	300 × 4 = 1200W	PCIe 3.0 / NVLink	14 × 4 = 56 TFLOPS	640 × 4 = 2,560	x16
2 × Radeon Pro W6800 (32GB each)	RDNA2	3,840 × 2 = 7680 (Stream Processors)	GDDR6 ECC	64GB (2 × 32)	512 GB/s (per GPU)	1.7 / 2.3 GHz	250 × 2 = 400W	PCIe 4.0	17.8 × 2 = 35.6 TFLOPS	N/A	x16

Notes:

Tesla V100 leverages NVLink for higher effective memory bandwidth scaling in multi-GPU setups.
Radeon Pro W6800 is professional workstation-class, not AI-specific like Tesla/RTX.
TDP totals show why 64GB GPU servers require strong cooling & power supply.

What Can a 64GB GPU Do?

A 64GB GPU offers massive video memory, making it ideal for data-intensive, professional, and enterprise workloads. While single consumer cards don’t usually reach 64GB, this memory capacity can be achieved through multi-GPU servers or datacenter accelerators such as NVIDIA A100, H100, or AMD Instinct series.

✅ Suitable for

AI & Machine Learning Training – Handle extremely large models (LLMs, multimodal AI) without frequent memory swapping.
3D Rendering & VFX – Render massive production scenes with high-resolution textures and assets in real-time.
Scientific & HPC Applications – Support simulations in physics, chemistry, or genomics that require very large datasets.
Virtualization & Cloud Workloads – Power multiple GPU-accelerated VMs or containerized environments in parallel.
Big Data Analytics – Speed up data mining, graph analytics, and real-time decision systems.
Multi-Instance GPU (MIG) – Partition a 64GB GPU server into smaller GPU instances for shared cloud workloads.

⚠️ Limits

Not Needed for Gaming – Even 4K and VR gaming rarely exceed 12GB VRAM.
High Cost – 64GB GPU servers are expensive and generally used in enterprise settings, not personal builds.
Power & Cooling Requirements – Multi-GPU or datacenter cards demand specialized infrastructure.
Software Compatibility – Many consumer applications (e.g., games, small editing suites) won’t benefit from such capacity.

In short, 64GB GPUs are designed for enterprise-class computing, AI research, cloud hosting, and professional rendering — not consumer use.

64GB GPU Hosting / 64GB GPU VPS

64GB GPU Hosting is built for users who require extreme GPU power with massive VRAM capacity. Whether achieved through multi-GPU configurations (such as 4 × 16GB GPUs or 2 × 32GB GPUs) or single datacenter-class accelerators like NVIDIA A100 (80GB) and H100 (96GB), this hosting solution enables enterprises and researchers to run workloads that demand unparalleled GPU memory.

With a 64GB GPU VPS, you can:

Train and deploy large-scale AI/ML models (LLMs, diffusion models, multimodal AI).
Handle professional 3D rendering and animation with ultra-high-resolution assets.
Run GPU-accelerated virtualization for multiple users or applications on the same server.
Process big data analytics and simulations without bottlenecks.
Ensure high reliability with 24/7 support, USA datacenter locations, and 99.9% uptime.

👉 If your project requires top-tier GPU resources, explore our GPU Server Hosting Plans for scalable 64GB GPU VPS solutions.

FAQs of 64GB GPUs

Is there a single 64GB GPU card?

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Most consumer and workstation GPUs do not come with 64GB VRAM on a single card. However, datacenter accelerators like the NVIDIA A100 (80GB) and H100 (96GB) exceed 64GB. For standard setups, 64GB is usually achieved through multi-GPU servers (e.g., 4 × 16GB or 2 × 32GB).

Do I need a 64GB GPU for gaming or video editing?

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No. Even 4K gaming or professional video editing typically uses less than 12–24GB VRAM. A 64GB GPU is overkill for gaming and is only useful for enterprise workloads.

Can I rent a 64GB GPU instead of buying?

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Yes. Through Database Mart GPU Server Hosting, you can access 64GB GPU VPS solutions without the upfront cost of buying expensive hardware.

What are 64GB GPUs used for?

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They are mainly used for AI training, large-scale machine learning, 3D rendering, HPC (high-performance computing), cloud GPU hosting, and big data analytics. They are not meant for gaming.

How much does a 64GB GPU cost?

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Costs vary depending on configuration:

Multi-GPU setups (4 × 16GB cards) may cost $6,000 – $10,000+.

Datacenter GPUs like NVIDIA A100 (80GB) can cost $12,000 – $15,000+.

Are 64GB GPUs good for AI training?

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Absolutely. They are designed for large language models (LLMs), image generation, multimodal AI, and scientific research where smaller GPUs would run out of memory.

Conclusion: 64GB GPUs

A 64GB GPU represents the upper tier of GPU memory capacity, designed for enterprise workloads, AI/ML research, high-performance computing, and large-scale rendering. While single consumer cards rarely offer 64GB, this capacity can be achieved through multi-GPU servers or datacenter accelerators like NVIDIA A100 (80GB), H100 (96GB), or AMD Instinct series.

For businesses, researchers, and studios, a 64GB GPU makes it possible to handle datasets, models, and scenes that smaller GPUs cannot process efficiently. However, it is not practical for gaming or small-scale creative work, as the cost and power requirements are far beyond consumer needs.

If you need enterprise-grade GPU power without the upfront investment, you can choose 64GB GPU Hosting / VPS solutions to access scalable, high-performance servers on demand.

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