Best NVIDIA GPUs for LLM Inference in 2025

LLM inference demands high-performance GPUs with exceptional computing capabilities, efficiency, and support for advanced AI workloads. This blog compares the latest and most relevant GPUs for AI inference in 2025: RTX 5090, RTX 4090, RTX A6000, RTX A4000, Nvidia A100 and H100. We'll evaluate their performance based on tensor cores, precision capabilities, architecture, and key advantages and disadvantages.

LLM inference refers to the process of using a trained language model to generate predictions or outputs based on new input data. Unlike the training phase, which involves adjusting the model’s parameters, inference is about utilizing the learned parameters to produce results. This process still requires substantial computational resources, especially for real-time applications or when processing large volumes of data.

When selecting an NVIDIA GPU for LLM inference, several crucial factors come into play:

1. Performance: This is typically measured in terms of Floating Point Operations per Second (FLOPS) and is influenced by the number of CUDA cores, Tensor cores, and clock speeds.

2. Memory Capacity: The amount of VRAM (Video RAM) determines the size of the models that can be loaded and processed efficiently.

3. Memory Bandwidth: Higher bandwidth allows for faster data transfer between GPU memory and processing units.

4. Cost: The initial investment and ongoing operational expenses are crucial considerations, particularly for large-scale deployments.

Architecture: Hopper

Launch Date: Mar. 2023

Computing Capability: 9.0

CUDA Cores: 14,592

Tensor Cores: 456 4th Gen

VRAM: 40/80GB HBM2e

Memory Bandwidth: 2 TB/s

Single-Precision Performance: 51.22 TFLOPS

Half-Precision Performance: 204.9 TFLOPS

Tensor Core Performance: FP64 67 TFLOPS, TF32 989 TFLOPS, BFLOAT16 1,979 TFLOPS, FP16 1,979 TFLOPS, FP8 3, 958 TFLOPS, INT8 3,958 TOPS

NVIDIA’s H100 dominates the AI training sector with its Hopper architecture, enhanced memory bandwidth, and improved tensor core efficiency. It’s the go-to choice for large-scale AI models such as GPT and Llama, offering unparalleled performance in multi-GPU server configurations.

Architecture: Ampere

Launch Date: May. 2020

Computing Capability: 8.0

CUDA Cores: 6,912

Tensor Cores: 432 3rd Gen

VRAM: 40/80 GB HBM2e

Memory Bandwidth: 1,935GB/s 2,039 GB/s

Single-Precision Performance: 19.5 TFLOPS

Double-Precision Performance: 9.7 TFLOPS

Tensor Core Performance: FP64 19.5 TFLOPS, Float 32 156 TFLOPS, BFLOAT16 312 TFLOPS, FP16 312 TFLOPS, INT8 624 TOPS

The Tesla A100 is built for data centers and excels in large-scale AI training and HPC tasks. Its Multi-Instance GPU (MIG) feature allows partitioning into multiple smaller GPUs, making it highly versatile. The A100’s HBM2e memory ensures unmatched memory bandwidth, making it ideal for training massive AI models like GPT variants.

Architecture: Blackwell 2.0

Launch Date: Jan. 2025

Computing Capability: 10.0

CUDA Cores: 21,760

Tensor Cores: 680 5th Gen

VRAM: 32 GB GDDR7

Memory Bandwidth: 1.79 TB/s

Single-Precision Performance: 104.8 TFLOPS

Half-Precision Performance: 104.8 TFLOPS

Tensor Core Performance: 450 TFLOPS (FP16), 900 TOPS (INT8)

The highly anticipated RTX 5090 introduces the Blackwell 2.0 architecture, delivering a significant performance leap over its predecessor. With increased CUDA cores and faster GDDR7 memory, it’s ideal for more demanding AI workloads. While not yet widely adopted in enterprise environments, its price-to-performance ratio makes it a strong contender for researchers and developers.

Architecture: Ada Lovelace

Launch Date: Oct. 2022

Computing Capability: 8.9

CUDA Cores: 16,384

Tensor Cores: 512 4th Gen

VRAM: 24 GB GDDR6X

Memory Bandwidth: 1.01 TB/s

Single-Precision Performance: 82.6 TFLOPS

Half-Precision Performance: 165.2 TFLOPS

Tensor Core Performance: 330 TFLOPS (FP16), 660 TOPS (INT8)

The RTX 4090, primarily designed for gaming, has proven its capability for AI tasks, especially for small to medium-scale projects. With its Ada Lovelace architecture and 24 GB of VRAM, it’s a cost-effective option for developers experimenting with deep learning models. However, its consumer-oriented design lacks enterprise-grade features like ECC memory.

Architecture: Ampere

Launch Date: Apr. 2021

Computing Capability: 8.6

CUDA Cores: 10,752

Tensor Cores: 336 3rd Gen

VRAM: 48 GB GDDR6

Memory Bandwidth: 768 GB/s

Single-Precision Performance: 38.7 TFLOPS

Half-Precision Performance: 77.4 TFLOPS

Tensor Core Performance: 312 TFLOPS (FP16)

The RTX A6000 is a workstation powerhouse. Its large 48 GB VRAM and ECC support make it perfect for training large models. Although its Ampere architecture is older compared to Ada and Blackwell, it remains a go-to choice for professionals requiring stability and reliability in production environments.

Architecture: Ampere

Launch Date: Apr. 2021

Computing Capability: 8.6

CUDA Cores: 6,144

Tensor Cores: 192 3rd Gen

VRAM: 16 GB GDDR6

Memory Bandwidth: 448.0 GB/s

Single-Precision Performance: 19.2 TFLOPS

Half-Precision Performance: 19.2 TFLOPS

Tensor Core Performance: 153.4 TFLOPS

NVIDIA RTX A4000 is a powerful GPU designed for professional workstations, offering excellent performance for AI inference tasks. While A4000 is powerful, more recent GPUs like A100 and A6000 offer higher performance and larger memory options, which may be more suitable for very large-scale AI inference tasks.

Choosing the right GPU for AI inference in 2025 depends on your workload and budget. The RTX 5090 leads with state-of-the-art performance but comes at a premium cost. For high-end enterprise applications, the Tesla A100 and RTX A6000 remain reliable choices. Meanwhile, the RTX A4000 offers a balance of affordability and capability for smaller-scale tasks. Understanding your specific needs will guide you to the optimal GPU for your AI inference journey.

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