10 Best GPUs for running AI Models
Graphics Processing Unit (GPU) plays a vital role for efficient AI model training and deep learning. There are lots of GPU currently available from different companies. If you are looking for best GPUS for AI than these GPU can helps you. NVIDIA is the major company that provides the GPU to all AI enthusiasts.
Factors to consider when choosing a GPU for AI:
- Having higher teraflops provides faster processing.
- To trains complex models, need larger GPU memory.
- To transfer the data faster, memory bandwidth is necessary.
- Support for AI framework like TensorFlow, PyTorch, etc.
Read More: The 50 Best AI Tools for 2025 – Free & Paid
10 Best GPUs for running AI Models
Followng are the 10 Best GPUs for running AI Models.
Pricing of 10 Best GPUs for running AI Models
Following are the Pricing of 10 Best GPUs for running AI Models.
| S.N | 10 Best GPUs for running AI Models | Price |
|---|---|---|
| 1 | NVIDIA A100 Tensor Core GPU | $8,000 to $10,000 for the 40GB. |
| 2 | NVIDIA H100 Tensor Core GPU | $25,000 per unit |
| 3 | NVIDIA RTX 4090 | $1,599.00 |
| 4 | NVIDIA RTX 4090 | $1,599.00 |
| 5 | NVIDIA RTX 3090 / 3090 Ti | $2000 |
| 6 | NVIDIA RTX A6000 | $4,650.00 |
| 7 | Google TPU | Starting at $2.7000 per chip-hour. |
| 8 | GB200 NVL72 | around $3 million. |
| 9 | RTX 6000 Ada Generation | $6,800.00 |
| 10 | RTX A5000 | $2,357.06. |
| 11 | GeForce RTX 4090 | $1,599. |
| 12 | GeForce RTX 4080 | $999. |
| 13 | GeForce RTX 4070 | $834.99. |
NVIDIA A100 Tensor Core GPU
NVIDIA A100 is one of the best GPU for AI model training. It is designed for AI workloads, dep learning, and interference. A100 provides up to 20X higher performance over the prior generation.
Key Features:
- Third Genration tensor Cores.
- High bandwith memory.
- Next Genration NVLink.
- Structural sparsity.
- Multi-instance GPU.
Specifications:
- Memory: 40GB or 80GB HBM2e
- Computer Power: 312 TFLOPS (FP16 Tensor Cores).
- Memory Bandwidth: 1,935 GB/s and 2039 GB/s.
- Max Thermal Design Power (TDP): 300 W and 400 W
- Multi-Instance GPU: Up to 7 MIGs @ 10GB.
- FP64 Tensor Core: 19.5 TFLOPS
- Best for: AI training, large scale models, cloud computing.
Also Read: Best AI for Coding.
NVIDIA H100 Tensor Core GPU
NVIDIA H100 Tensor Core GPU delivers exceptional performance, scalability, and security for every data center. It is industry-leading conversational AI with speeding up large language models (LLMs) by 30X. With dedicated transformer, it can solve trillion parameter language models. It is known for extraordinary performance, scalability, and security.
Key Features:
- Fourth-generation Tensor Cores.
- High-bandwidth memory (HBM3).
- Next-generation NVLink.
- Multi-instance GPU (MIG).
- Transformer Engine.
- NVIDIA confidential computing.
Product Specification
| Features | H100 SXM | H100 NVL |
|---|---|---|
| FP64 Tensor | Core 67 teraFLOPS | 60 teraFLOPs |
| TF32 Tensor Core | 989 teraFLOPS | 835 teraFLOPs |
| BFLOAT16 Tensor Core | 1,979 teraFLOPS | 1,671 teraFLOPS |
| FP16 Tensor Core | 1,979 teraFLOPS | 1,671 teraFLOPS |
| FP8 Tensor Core | 3,958 teraFLOPS | 3,341 teraFLOPS |
| INT8 Tensor Core | 3,958 TOPS | 3,341 TOPS |
| GPU Memory | 80GB | 94GB |
| GPU Memory Bandwidth | 3.35TB/s | 3.9TB/s |
| Max Thermal Design Power | (TDP) Up to 700W (configurable) | 350-400W (configurable) |
| Multi-Instance GPUs | Up to 7 MIGS @ 10GB each | Up to 7 MIGS @ 12GB each |
Also Read: 20 Best AI for marketing – Free & Paid.
NVIDIA RTX 4090
NVIDIA RTX 4090 is good option for researchers or small business with limited budgets.
Major Features
- Cutting-Edge GPUs.
- AI-Accelerated Performance.
- AI-Enhanced Voice and Video.
- Fast-Track Your Creativity.
- Built for Live Streaming.
- Game-Winning Responsiveness.
Major Specification
| Features | Information |
|---|---|
| NVIDIA CUDA Cores | 16384 |
| Boost Clock (GHz) | 2.52 |
| Memory Size | 24 GB |
| Memory Type | GDDR6X |
| Memory Bandwidth: | 1,000 GB/s. |
Reviews of User:
- I couldn’t quite believe my eyes… The RTX 4090 is a beast – The Verge
- DLSS 3 is pure magic – TweakTown
- More powerful than we even thought possible – TechRadar
- Fantastically, futuristically fast – PCWorld
Read More: Top 10 best Artificial Intelligence (AI) companies in the World.
NVIDIA RTX 3090 / 3090 Ti
NVIDIA RTX 3090 / 3090 Ti is powered by Ampere. It has dedicated 2nd gen RT Cores and 3rd gen Tensor Cores, streaming multiprocessors, and a staggering 24 GB of G6X memory to deliver high-quality performance for gamers and creators. It is best for Researchers, students, smaller businesses and startups with limited budgets.
Features of NVIDIA RTX 3090 / 3090 Ti
- Cutting-Edge GPUs.
- Realistic & Immersive Graphics.
- AI-acceleratedPerformance.
- Lowest System Latency.
- Built for Live Streaming.
- Performance & Reliability.
Reviews of Users
- Stunning creator performance. Monster gaming performance -Techspot.
- Supremely powerful -Pc Gamer.
- The king of GeForce GPUs -Dave2d.
- Jaw-dropping 3D rendering and encoding performance -Tech Radar.
Read More: 10 Best AI for Graphic Design.
NVIDIA RTX A6000
NVIDIA RTX A6000 is the world’s most powerful visual computing GPU for desktop workstations. It is best for Professional AI projects, visualization tasks, workstation setups.
Key Features
- NVIDIA Ampere Architecture Based CUDA Cores.
- 48 Gigabytes (GB) of GPU Memory.
- Second-Generation RT Cores.
- Third-Generation Tensor Cores.
- Third-Generation NVIDIA NVLink.
Product Specification
| GPU Features | NVIDIA RTX A6000 |
|---|---|
| GPU Memory | 48 GB GDDR6 with error-correcting code (ECC). |
| Display Ports | 4x DisplayPort 1.4a |
| Max Power Consumption | 300 W |
| Graphics Bus | PCI Express Gen 4 x 16 |
| Form Factor | 4.4” (H) x 10.5” (L) dual slot |
| Thermal | Active |
Also Read: 12 Best AI Apps for iPhone and iPad.
Technically not GPU but it is known for hardware accelerators designed explicitly for AI workloads. It is best for Large-scale AI workloads, research, TensorFlow-focused projects. It is AI base Like a GPU for AI enthusiast for training to inference.
There are three types of Cloud TPU pricing:
| Cloud TPU Version | Evaluation Price (USD) | 1-year commitment (USD) | 3-year commitment (USD) |
|---|---|---|---|
| Trillium | Starting at $2.7000 per chip-hour. | Starting at $1.8900 per chip-hour. | Starting at $1.2200 per chip-hour. |
| Cloud TPU v5p | Starting at $4.2000 per chip-hour. | Starting at $2.9400 per chip-hour. | Starting at $1.8900 per chip-hour. |
| Cloud TPU v5e | Starting at $1.2000 per chip-hour | Starting at $0.8400 per chip-hour. | Starting at $0.5400 per chip-hour. |
H200 Tensor Core GPU
H200 Tensor Core GPU supercharges generative AI. It manage the workloads with game-changing performamce and memory capabilities.
Key Features
- Experience Next-Level Performance
- Higher Performance With Larger, Faster Memory.
- Supercharge High-Performance Computing.
- Reduce Energy and TCO.
GB200 NVL72
GB200 NVL72 is powering the new era of computing. It connects 36 Grace CPUs and 72 Blackwell GPUs in a rack-scale, liquid-cooled design. It delivers 30X faster real-time trillion parameter large language model (LLM) inference.
Key Features:
- Supercharging Next-Generation AI and Accelerated Computing.
- Real-Time LLM Inference.
- Energy-Efficient Infrastructure.
- Data Processing.
- Fifth-Generation NVIDIA NVLink.
- Blackwell Architecture.
Also Read: The 8 best AI image generators in 2025 – Free & Paid.
Product Specification
| GPU Features | GB200 NVL72 |
|---|---|
| Configuration | 36 Grace CPU : 72 Blackwell GPUs. |
| FP4 Tensor Core | 1,440 PFLOPS. |
| FP8/FP6 Tensor Core | 720 PFLOPS |
| INT8 Tensor Core | 720 POPS |
| FP16/BF16 Tensor Core | 360 PFLOPS |
| TF32 Tensor Core | 180 PFLOPS |
| FP32 | 5,760 TFLOPS |
| FP64 | 2,880 TFLOPS |
| FP64 Tensor Core | 2,880 TFLOPS |
| GPU Memory | Bandwidth | Up to 13.4 TB HBM3e | 576 TB/s |
| NVLink Bandwidth | 130TB/s |
RTX 6000 Ada Generation
RTX 6000 Ada Generation is engineered for professional workflows. With 48GB of graphics memory for unprecedented rendering, AI, graphics, and compute performance.
Key Features:
- NVIDIA Ada Lovelace Architecture-Based CUDA Cores.
- Third-Generation RT Cores.
- Fourth-Generation Tensor Cores.
- 48GB of GPU Memory.
- Virtualization-Ready.
- Performance for Endless Possibilities.
RTX A5000
RTX A5000 is a GPU for perfect balance of power, performance, and reliability to tackle complex workflows. Its best for designers, engineers and artists to reliaze their visions forthe future.
Key Features
- NVIDIA Ampere Architecture-Based CUDA Cores.
- Second-Generation RT Cores.
- Third-Generation Tensor Cores.
- 24GB of GPU Memory.
- Third-Generation NVIDIA NVLink.
- Power Efficiency.
Hence, these are the 10 Best GPUs for running AI Models.
Frequently Asked Questions
a. What is the best GPU for running AI models?
= NVIDIA A100 and H100 are the best GPU for running AI Models.
b. What GPUs do AI companies use?
= Mostly, AI companies use NVDIA A100 for training.
c. What GPU is needed for generative AI?
= At least 8GB VRAM and Tensor Cores (e.g., NVIDIA RTX 3060 or 4060) GPU are needed for generative AI.
d. What is the best budget GPU for AI?
= NVIDIA GTX 1080 Ti or AMD Radeon RX 5700 XT are the best Budget GPU for AI model training.
e. Is GPU or CPU better for AI?
= GPU are built to train the AI technologies.
f. Best GPU for machine learning?
= NVIDIA H100 NVL or NVIDIA A100 Tensor Core GPU is the best GPU for machine Learning.
