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Llama vs Qwen — On Apple Silicon

Llama 3.1 8B Instruct vs Qwen 2.5 14B Instruct. Which runs better on your Mac? Measured tok/s across 44 chips, Q4_K - Medium quantization.

1.85× Average Llama/Qwen speed ratio (same chip)
~4.5 GB RAM for Llama 3.1 8B (Q4)
~8.5 GB RAM for Qwen 2.5 14B (Q4)
44 Chips with both models benchmarked

Speed comparison — 44 chips, Q4_K - Medium

Llama 3.1 8B Instruct (Q4) vs Qwen 2.5 14B Instruct (Q4) on the same chip. The ratio column shows how much faster Llama runs — smaller models always run faster at the same quant level, but Qwen 2.5 14B offers better quality.

Chip Llama 3.1 8B tok/s Qwen 2.5 14B tok/s Speed ratio
M3 Ultra (80-core GPU, 256 GB) 63.3 tok/s 36.7 tok/s 1.72×
M3 Ultra (80-core GPU, 512 GB) 62.7 tok/s 35.8 tok/s 1.75×
M2 Ultra (60-core GPU, 64 GB) 59.5 tok/s 34.2 tok/s 1.74×
M4 Max (40-core GPU, 48 GB) 55.1 tok/s 30.1 tok/s 1.83×
M1 Ultra (64-core GPU, 128 GB) 54.3 tok/s 32.4 tok/s 1.68×
M4 Max (40-core GPU, 64 GB) 52.4 tok/s 27.7 tok/s 1.89×
M4 Max (40-core GPU, 128 GB) 51.6 tok/s 28.7 tok/s 1.80×
M1 Ultra (48-core GPU, 128 GB) 48.9 tok/s 27.8 tok/s 1.76×
M4 Max (32-core GPU, 36 GB) 48.1 tok/s 24.6 tok/s 1.95×
M2 Max (38-core GPU, 96 GB) 46.4 tok/s 25.2 tok/s 1.84×
M3 Max (40-core GPU, 128 GB) 45.8 tok/s 25.5 tok/s 1.80×
M2 Max (38-core GPU, 32 GB) 44.7 tok/s 20.6 tok/s 2.17×
M1 Max (32-core GPU, 64 GB) 37.8 tok/s 19.0 tok/s 1.99×
M3 Max (30-core GPU, 96 GB) 37.7 tok/s 20.8 tok/s 1.82×
M3 Max (30-core GPU, 36 GB) 37.5 tok/s 19.8 tok/s 1.90×
M1 Max (32-core GPU, 32 GB) 35.4 tok/s 20.1 tok/s 1.76×
M4 Pro (20-core GPU, 64 GB) 32.9 tok/s 18.0 tok/s 1.83×
M4 Pro (20-core GPU, 48 GB) 32.7 tok/s 18.0 tok/s 1.82×
M4 Pro (20-core GPU, 24 GB) 32.5 tok/s 18.0 tok/s 1.81×
M1 Max (24-core GPU, 64 GB) 32.1 tok/s 15.1 tok/s 2.12×
M4 Pro (16-core GPU, 24 GB) 30.5 tok/s 15.2 tok/s 2.01×
M4 Pro (16-core GPU, 48 GB) 30.2 tok/s 16.8 tok/s 1.80×
M2 Pro (19-core GPU, 32 GB) 26.3 tok/s 14.1 tok/s 1.86×
M3 Max (40-core GPU, 64 GB) 25.4 tok/s 13.8 tok/s 1.84×
M2 Pro (16-core GPU, 16 GB) 24.3 tok/s 13.4 tok/s 1.82×
M5 (10-core GPU, 32 GB) 22.3 tok/s 11.5 tok/s 1.93×
M3 Pro (18-core GPU, 36 GB) 22.1 tok/s 12.0 tok/s 1.84×
M1 Pro (16-core GPU, 16 GB) 21.9 tok/s 11.9 tok/s 1.84×
M1 Pro (16-core GPU, 32 GB) 21.7 tok/s 11.6 tok/s 1.87×
M3 Pro (14-core GPU, 36 GB) 21.5 tok/s 12.1 tok/s 1.77×

Data from LocalScore community benchmarks. Both at Q4_K - Medium quantization. Source: benchmarks.json.

When to choose Llama vs Qwen on Apple Silicon

Choose Llama 3.1 8B when:

  • You have 8–16 GB RAM — it fits comfortably, 14B is tighter
  • Speed matters more than quality: ~2× faster generation than 14B
  • Running a coding assistant where latency matters most
  • You want to step up to Q8 (only ~9 GB for Q8 8B)
  • Serving multiple concurrent requests on limited RAM

Choose Qwen 2.5 14B when:

  • You have 16 GB+ RAM to spare
  • Quality matters: 14B is noticeably better at reasoning and instruction following
  • You want strong multilingual support (Qwen is excellent in Chinese/English)
  • Running longer conversations where context quality accumulates
  • Research or writing tasks where output quality is the bottleneck

Consider Qwen 3 instead of Qwen 2.5

  • Qwen 3 8B matches or exceeds Qwen 2.5 14B quality in many benchmarks
  • Qwen 3 4B MoE at 2.5 GB is surprisingly capable for its size
  • Qwen 3 30B A3B (MoE) runs at 18 GB — similar footprint to a 14B dense model
  • M4 Max benchmarks: Qwen 3 30B A3B at 92 tok/s Q4 on 16 GB RAM

The 8B vs 14B quality gap

  • For simple chat and Q&A: 8B is usually sufficient
  • For complex reasoning, writing, and code generation: 14B has a visible edge
  • Instruction following: Llama 3.1 and Qwen 2.5 are both well-tuned at their respective sizes
  • Qwen 3 32B is where quality becomes clearly superior — but requires 64 GB+ for comfort
Practical recommendation: start with Llama 3.1 8B Q4_K_M, then try Qwen 2.5 14B if quality matters.

The 2× speed premium on Llama makes it the better daily-driver choice for most interactive workloads. If you find the quality lacking on complex tasks, step up to Qwen 2.5 14B — it fits in 16 GB and the quality gap is meaningful. If you have 24+ GB, consider Qwen 3 8B or Qwen 3 30B A3B (MoE) for a better quality-per-RAM tradeoff than Qwen 2.5 14B dense.

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