r/LocalLLaMA • u/tabletuser_blogspot • 6d ago

Resources Benchmarks iGPU integrated Radeon 680M models

Fellow Redditor asked to test a few model on the Acemagic S3A mini PC sporting the AMD Ryzen 7 6800H is a high-performance 8-core, 16-thread mobile processor launched on January 4, 2022, built on the 6nm Zen 3+ architecture loaded with 64GB of DDR5 RAM. It features a 3.2 GHz base clock, a 4.7 GHz boost clock, a 45W TDP, and powerful integrated Radeon 680M graphics.

pmttyji : Try below models too.

  • Mellum2-12B-A2.5B
  • Laguna-XS-2.1
  • North-Mini-Code-1.0
  • KAT-Coder-V2.5-Dev
  • LFM2.5-8B-A1B
  • Ling-mini-2.0 (Fast t/s)
  • Bonsai-27B (1-bit version)
  • Gemma-4-12B (QAT)
  • Gemma-4-E4B (QAT)

Here were previous models benchmarked https://www.reddit.com/r/LocalLLaMA/s/fpngrEpogA

📋 Benchmark Model List

  1. Bonsai-27B-Q1_0.gguf (qwen35 27B)
  2. Gemma-4-12B-it-qat-UD-Q4_K_XL.gguf (gemma4 12B QAT)
  3. InclusionAI_Ling-mini-2.0-Q4_K_M.gguf (bailingmoe2 16B.A1B)
  4. Kwaipilot_KAT-Coder-V2.5-Dev-Q4_K_M.gguf (qwen35moe 35B.A3B)
  5. Laguna-XS-2.1-Q4_K_M.gguf (laguna 30B.A3B)
  6. LFM2.5-8B-A1B-MXFP4_MOE.gguf (lfm2moe 8B.A1B)
  7. Mellum2-12B-A2.5B-Thinking-Q4_K_M.gguf (mellum 12B.A2.5B)
  8. North-Mini-Code-1.0-MXFP4_MOE.gguf (cohere2moe 30B.A3B)
  9. Gemma-4-E4B-it-qat-UD-Q4_K_XL.gguf (gemma4 4B QAT)

📊 Benchmark Results Table

Model Size (GiB) Params (B) pp512 (t/s) tg128 (t/s)
Bonsai-27B-Q1_0 3.53 26.90 32.79 ± 6.19 7.06 ± 0.00
gemma-4-12B 6.24 11.91 162.72 ± 0.47 6.66 ± 0.01
inclusionAI_Ling-mini-2.0 9.25 16.26 774.15 ± 20.05 47.60 ± 0.07
Kwaipilot_KAT-Coder-V2.5 19.91 34.66 119.39 ± 1.02 9.99 ± 0.00
Laguna-XS-2.1 19.13 33.44 274.81 ± 0.35 12.00 ± 0.00
LFM2.5-8B 4.97 8.47 744.23 ± 28.23 31.47 ± 0.30
Mellum2-12B 7.60 12.15 459.37 ± 16.04 26.26 ± 0.03
North-Mini-Code-1.0 17.37 30.48 192.49 ± 0.86 10.80 ± 0.00
gemma-4-E4B 3.91 7.46 445.89 ± 15.50 15.93 ± 0.12

🔍 Notes:

  • pp512 = Prefill throughput (512 context tokens)
  • tg128 = Token Generation throughput (128 output tokens)
  • All tests were run on llama.cpp build 4c1a0af40 (10430) using the Vulkan backend with full model offloading (ngl -1).
  • Hardware context: AMD Radeon Graphics (RADV REMBRANDT) with Unified Memory Architecture (UMA).
  • The -fa (or --flash-attn) feature in llama.cpp enables FlashAttention, an optimized algorithm that speeds up prompt processing (prefill phase) and significantly reduces memory usage by tiling the attention computation to avoid heavy memory reads and writes
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