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GPU COMPARISON MATRIX FOR AI / LLM WORKLOADS

Detailed technical comparison between four graphics cards: RTX 3060 12GB, RTX 5060 Ti 16GB, Arc A770 16GB, and Arc B60 16GB, focusing on core metrics for AI, Deep Learning, and Local LLM deployments.

📊 Detailed Comparison Matrix

Metric NVIDIA RTX 3060 NVIDIA RTX 5060 Ti Intel Arc A770 (Graphics) Intel Arc B60 (Workstation)
1. VRAM Capacity 12 GB GDDR6 16 GB GDDR7 16 GB GDDR6 1624 GB GDDR6
2. Memory Speed 15 Gbps
(Bus: 192-bit)
(Bandwidth: 360 GB/s)		 28 Gbps
(Bus: 128-bit)
(Bandwidth: 448 GB/s)		 17.5 Gbps
(Bus: 256-bit)
(Băng thông: 560 GB/s)		 19 Gbps
(Bus: 192-bit)
(Bandwidth: 456 GB/s)
3. AI Compute (INT8 TOPS) ~244 TOPS
(Tensor Cores Gen 3)		 ~759 TOPS
(Tensor Cores Gen 5)		 ~256 TOPS
(XMX Engines)		 ~197 TOPS
(XMX Engines Gen 2)
4. Release Date Q1 / 2021 Q1 / 2025 Q4 / 2022 Q1 / 2025

💡 Quick Analysis for AI / Local LLM Engineers

  1. VRAM & Model Capacity:

    • The 5060 Ti, A770, and B60 all feature 16GB VRAM, which is the sweet spot for running quantized mid-sized Large Language Models (LLMs) like Llama-3-8B or Qwen-2.5-14B entirely on the GPU without risking system RAM spillover.
    • The RTX 3060 falls behind with 12GB, but considering its budget-friendly market price, it remains an excellent entry-level "gateway" card for AI beginners.

  2. Memory Bandwidth (Token Generation Speed):

    • This is where the anomalies lie. The older Intel Arc A770 boasts the highest bandwidth (560 GB/s) thanks to its wide 256-bit bus width. Theoretically, this ensures a highly fluid data pipeline during LLM inference token generation.
    • The RTX 5060 Ti compensates for its narrower 128-bit bus width by utilizing next-gen GDDR7 28 Gbps memory chips, pushing its bandwidth up to a respectable 448 GB/s.
    • The Arc B60, despite being a newer generation, takes a step back with an artificially constrained memory subsystem, utilizing factory-overclocked older GDDR6 chips.

  3. Raw Inference Performance (INT8 TOPS):

    • The RTX 5060 Ti absolute obliterates the competition with a staggering 759 TOPS. If your workflow involves fine-tuning, heavy Stable Diffusion image generation loops, or intense Computer Vision workloads, NVIDIA's Blackwell architecture is unmatched.
    • The Intel Arc B60 underdelivers significantly, dropping below 200 TOPS—effectively losing to both its predecessor (A770) and even the aging RTX 3060 when utilizing core matrix calculations.

  4. Software Ecosystem & Compatibility:

    • NVIDIA (3060, 5060 Ti): Seamless out-of-the-box operation natively powered by CUDA. It offers 100% day-one compatibility with major AI repositories and frameworks (PyTorch, HuggingFace, vLLM).
    • Intel (A770, B60): Requires reliance on the OneAPI / OpenVINO toolkit or establishing specialized execution environments like IPEX (Intel Extension for PyTorch). It demands a higher degree of manual environment configuration and command-line troubleshooting.

Automatically generated for AI deployment and hardware evaluation purposes.

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