Apple Silicon LLM Benchmarks

Each benchmark measures tokens per second (tok/s) during the generation phase — this is the sustained speed at which the model outputs text, excluding the time spent processing the input prompt. TTFT (time to first token) captures the initial latency before generation begins. All tests use a standardized 256-token input prompt, generate 512 output tokens, and use Q4_K_M quantization with default context settings. Results are averaged over 3 consecutive runs.

Each engine uses a different inference backend with distinct optimizations. MLX is Apple's native framework, purpose-built for Metal GPU acceleration on Apple Silicon — it often delivers the fastest results, especially for smaller models. Ollama uses llama.cpp with Metal support and provides reliable, consistent performance. LM Studio also wraps llama.cpp but adds a GUI layer that can introduce minor overhead. The performance gap between engines is typically 5-15% for the same model and hardware configuration.

It depends on your target model size. For small models (3-9B), even an M1 with 16 GB delivers usable speeds (40-80 tok/s). For mid-size models (14-35B), the M4 Pro with 24 GB is the sweet spot — enough RAM for 14B models at 35-55 tok/s. For large models (70B+), the M5 Max with 128 GB is ideal, offering ~600 GB/s memory bandwidth. The M4 Ultra with 192 GB handles the biggest models but is overkill for anything under 70B.

Yes, we welcome community submissions. Run your benchmark using Ollama, LM Studio, or MLX with standard settings (Q4_K_M quantization, default context). Record your chip model, total RAM, engine version, and both tok/s and TTFT values. Submit via our GitHub repository or by email. We verify all submissions against known performance baselines before adding them to the database.

According to the LLMCheck index as of July 2026, Gemma 4 E2B is the fastest at approximately 158 tokens per second on M5 Max via MLX. Phi-4 Mini follows at ~135 tok/s. Among larger models, Qwen 4.1 32B-A3B (the #1 ranked Mac model, 80% SWE-V) generates ~62 tok/s on M4 Pro, and GLM 5.2 Air (106B-A12B MoE) achieves ~30 tok/s on a 64 GB Mac with near-frontier reasoning quality.

Memory bandwidth determines how fast your Mac can feed model weights to the GPU during inference. The LLMCheck index shows a near-linear relationship: the M5 Max (~600 GB/s bandwidth) generates tokens roughly 3x faster than a base M3 (~200 GB/s). This is why Unified Memory architecture gives Apple Silicon an advantage — there's no CPU-to-GPU transfer bottleneck.

The LLMCheck Score is a 0–100 composite metric: 50 points for model capability (sourced from Arena AI ELO, MMLU, and coding benchmarks), 25 points for Mac-specific speed (tok/s on M5 Max), 15 points for accessibility (minimum RAM), and 10 points for license openness. Full formula and per-model sources at /methodology.html.