Explore the most powerful and efficient graphics cards for AI inference tasks, crucial for deploying machine learning models in production. This guide compares models from NVIDIA, AMD, and Intel, highlighting their performance in AI workloads, VRAM capacity, and value for money. Find the ideal GPU for your artificial intelligence projects, from large language models (LLMs) to computer vision applications. Optimize your AI operations with the right hardware choice.
Close in AI performance to L40S
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This graphics card delivers unprecedented performance for AI inference workloads, thanks to its 5th generation Tensor Cores which triple the performance of the previous generation and support FP4 precision. Its 48 GB of GDDR6 ECC memory is crucial for handling complex AI models and extensive datasets, enabling superior efficiency and speed in AI processing.
AI-enhanced graphics and performance
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This graphics card offers 16 GB of GDDR7 VRAM and high bandwidth, crucial features for running large language models (LLMs) and image generation models locally. Its optimized architecture with NVIDIA DLSS 4 and Reflex 2 significantly enhances performance in AI inference tasks, providing a powerful and efficient solution for professionals and enthusiasts.
Cost-effective for small-scale LLMs
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The NVIDIA GeForce GTX 1660 Super offers exceptional value in terms of VRAM and FP32/16 compute capabilities, making it a cost-effective option for small LLM inference workloads. It efficiently handles AI inference tasks, such as Stable Diffusion and Fooocus, while keeping costs in check.
78% memory bandwidth increase over 4090
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The NVIDIA RTX 5090 delivers exceptional performance for AI inference, distinguished by its Blackwell architecture with FP8/FP4 support and 32 GB of GDDR7 VRAM. It provides a significant boost in inference speed, making it ideal for large and complex AI models, and offers strong value for fine-tuning and inference.
Meets NVIDIA's SFF-Ready spec
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This graphics card features fifth-gen Tensor Cores and DLSS 4.5, enabling it to deliver maximized AI performance with FP4. Its Streaming Multiprocessors are optimized for neural shaders, providing advanced capabilities for AI inference in various applications. The NVIDIA Blackwell architecture significantly enhances efficiency and speed in artificial intelligence workloads.
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Good throughput in MLPerf Client benchmark
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The AMD RX 9070 XT delivers robust performance in AI inference tasks, notably featuring second-generation AI accelerators that provide over 1,500 AI TOPS. Its architecture and 16GB of GDDR6 memory make it a competitive option for AI workloads, especially in LLM inference where memory bandwidth is crucial.
Accelerates AI experiences
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The AMD Radeon RX 6700 XT offers significant value for local AI inference, particularly for 7B–13B LLM models and Stable Diffusion, thanks to its 12GB of VRAM. While it may not match higher-end cards in intensive tasks, its cost-effectiveness and ability to accelerate AI experiences via Vulkan and ROCm make it a viable option for budget-conscious users.
Performs well in Stable Diffusion tests
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The Intel Arc B580 offers unbeatable value for local AI inference, especially with its 12GB of VRAM, making it ideal for running large language models and chatbots. Its Battlemage architecture and driver optimizations have shown surprising performance in AI workloads, rivaling higher-end cards.
Handles LLM inference at 10 to 30+ tokens per second
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The NVIDIA RTX 4090 delivers exceptional performance for Large Language Model (LLM) inference, handling 10 to 30+ tokens per second thanks to its 16,384 CUDA cores and 24 GB of GDDR6X memory. It represents excellent value for enthusiasts, researchers, and developers needing to run 30B-70B models locally, balancing high performance with contained costs.
Delivers powerful, low-latency AI inference
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This graphics card delivers exceptional performance for AI inference, thanks to its powerful BMG-G31 GPU and 32GB of GDDR6 memory. Its architecture is optimized to handle intensive AI workloads, providing 367 INT8 TOPS of processing capability that significantly accelerates machine learning tasks and large language models.
Our methodology for ranking the best graphics cards for AI inference is based on a comprehensive analysis of their performance across various artificial intelligence workloads, the relevance of their software ecosystem, and their value for money. We focus on providing a useful guide for professionals and enthusiasts.