#llm

Posts on X2Q tagged llm. Updated as new posts are published.

• Jul 13, 2026

  First look at the NVIDIA DGX Spark — what the box actually reports (2026)

  The NVIDIA DGX Spark reports itself as a 20-core Arm machine (10× Cortex-X925 + 10× Cortex-A725), 121 GiB of unified LPDDR5x memory, a single GB10 Blackwell GPU on CUDA 13, and a 4 TB Samsung NVMe — all in a gold box the size of a thick paperback. This first-look post reads the whole machine back from the command line (lscpu, free -h, nvidia-smi, lspci, lsblk) and shows it doing the one thing no consumer GPU can: holding a 67 GB llama4:scout and a 65 GB gpt-oss:120b model in memory at the same time.

• 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.