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GPU COMPARISON MATRIX FOR AI / LLM WORKLOADS

GPU COMPARISON MATRIX FOR AI / LLM WORKLOADS

Detailed technical comparison between fourfive graphics cards: RTX 3060 12GB, RTX 5060 Ti 16GB, 9060 XT 16GB, Arc A770 16GB, and Arc B60 16GB24GB, focusing on core metrics for AI, Deep Learning, and Local LLM deployments.

📊 Detailed Comparison Matrix

Metric NVIDIA GeForce RTX 3060 NVIDIA GeForce RTX 5060 Ti
AMD Radeon RX 9060 XT Intel Arc A770 (Graphics) Intel Arc B60 (Workstation) 1. VRAM Capacity 12 GB GDDR6 16 GB GDDR7 16 GB GDDR6 16 GB GDDR6 24 GB GDDR6 2. Memory Speed 15 Gbps
(Bus: 192-bit)
(Bandwidth: 360 GB/s) 28 Gbps
(Bus: 128-bit)
(Bandwidth: 448 GB/s) 16 Gbps
(Bus: 128-bit)
(Bandwidth: 320 GB/s) 17.5 Gbps
(Bus: 256-bit)
(Bandwidth: 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) ~410 TOPS
(RDNA 4 AI Accelerators) ~256 TOPS
(XMX Engines) ~197 TOPS
(XMX Engines Gen 2) 4. Release Date Q1 / 2021 Q1 / 2025 Q1 / 2025 Q4 / 2022 Q1 / 2025

💡 Quick Analysis for AI / Local LLM Engineers

  1. VRAM & Model Capacity:

    • The Intel Arc B60 stands out with its massive 24GB VRAM, matching upper-tier workstation cards in capacity. This allows it to comfortably host larger models (up to quantized 32B or heavily compressed 70B models) entirely within the GPU memory.
  2. The 5060 Ti, A770,9060 XT, and B60A770 allsit featurecomfortably at 16GB VRAM, which is the ideal sweet spot for running quantized mid-sized Large Language Models (LLMs)models like Llama-3-8B or Qwen-2.5-14B entirely on the GPU without risking system RAM spillover.
  3. The RTX 3060 falls behind with 12GB, but consideringremains itsa budget-friendly market price, it remains an excellent entry-level "gateway"gateway card for AI beginners.card.

  4. Memory Bandwidth (Token Generation Speed):

    • This is where the anomalies lie. The older Intel Arc A770 boastsstill holds the highest raw bandwidth (560 GB/s) thanksdue to its wideunconstrained 256-bit bus width.width, Theoretically, this ensures a highlyensuring fluid data pipelinetransfer during LLM inference tokenloops.
    generation.
  5. The Arc B60 sits at 456 GB/s; even with its 24GB capacity, the 192-bit bus acts as a subtle speed limit compared to high-end architectures.
  6. The RTX 5060 Ti compensatesachieves fora itshighly narrowerefficient 128-bit448 bus widthGB/s by utilizingleveraging next-gen GDDR7 28 Gbps memorymodules, chips, pushingovercoming its bandwidthnarrow up128-bit tobus a respectable 448 GB/s.constraint.
  7. The ArcRX B609060 XT is the most bottlenecked here at 320 GB/s, despitewhich beingslightly abottlenecks newerits generation,token-per-second takesoutput aduring steppure backLLM withinference an artificially constrained memory subsystem, utilizing factory-overclocked older GDDR6 chips.tasks.

  8. Raw Inference Performance (INT8 TOPS):

    • The RTX 5060 Ti absoluteabsolutely obliterates the competitionfield with a staggering 759 TOPS., Ifmaking yourit workflowthe involvespremier choice for fine-tuning, computer vision, and heavy Stable Diffusion image generation loops,loops.
    or
  9. The intenseRX Computer9060 VisionXT workloads,occupies NVIDIA'a strong mid-range position at ~410 TOPS, representing a substantial compute upgrade for AMD's Blackwellmainstream architecture is unmatched.platform.
  10. The Intel Arc B60 significantly underdelivers significantly,in compute density, dropping below 200 TOPS—effectively losing to both its predecessor (A770) and even the aging RTX 3060 whenin utilizing coreraw matrix calculations.mathematical operations.

  11. 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).
  12. AMD (9060 XT): Relies on ROCm. Highly performant and native on Linux-based environments (via Ollama or Llama.cpp), though Windows support requires slightly more setup.
  13. Intel (A770, B60): Requires reliance on the OneAPI / OpenVINO toolkit or establishing specialized execution environments like IPEX (Intel Extension for PyTorch)., It demandsdemanding a higher degree of manual environment configuration and command-line troubleshooting.
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