#apple-silicon

Posts on X2Q tagged apple-silicon. Updated as new posts are published.

• Jun 22, 2026

  Apple M1 Pro vs RTX 4070 Ti for local AI — GPU matmul and LLM benchmarks (2026)

  RTX 4070 Ti hits 75 TFLOPS at fp16 via tensor cores; M1 Pro MPS reaches 4.2 TFLOPS — an 18× compute gap. But on real LLM inference (minicpm-v4.6, qwen3.5, gemma4:12b via ollama), the RTX is only 3–4× faster. LLM inference at batch=1 is memory-bandwidth-bound, not compute-bound, and the M1's unified-memory architecture closes most of the gap. Apple's AMX extensions also give its CPU 2.7× better matmul throughput than an Intel i5 with the same thread count.

• Jun 22, 2026

  Ollama complete guide — run LLMs locally (2026)

  Ollama lets you run large language models locally with a single command. This guide covers installation on Mac and Linux, how to pick the right model (with a size table), a CLI cheat sheet, the REST API with curl and Python, customisation with Modelfile and system prompts, and performance tricks that halve VRAM usage.

• Jun 22, 2026

  Rust vs Go vs C on Apple Silicon and Linux — benchmarks on real hardware (2026)

  Four benchmarks on two machines — Apple M1 Pro (macOS/clang) and Intel i5-13400F (Linux/gcc). Rust ≈ C on the M1; on x86 Linux, Rust is 1.5× slower than gcc on recursive fib and Go is 2.9× slower. Go's matrix multiply gap is 4.7× on Linux (vs 3.3× on arm64). The surprise: Rust's concurrent sum on x86 is 2.8× faster than C pthreads — LLVM likely folds the range into a closed-form sum.