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Showing posts with the label Hardware

Best Linux Terminal Emulators: 2026 Comparison

Compare top Linux terminal emulators: Alacritty, Kitty, WezTerm, Ghostty, GNOME Terminal, and more. Features, performance, and customization options reviewed. Best Linux Terminal Emulators: 2026 Comparison

KV Cache on 16 GB GPUs: Making Long Context Actually Fit

Fit 32K to 128K LLM context into 16 GB VRAM by calculating KV cache cost, choosing cache precision, and tuning llama.cpp, vLLM, or Ollama safely. KV Cache on 16 GB GPUs: Making Long Context Actually Fit

GPUs for AI in 2026: NVIDIA, AMD, Intel Compared

Compare NVIDIA Blackwell, AMD Radeon AI Pro R9700, and Intel Arc Pro B70 for local LLM inference. VRAM, bandwidth, software ecosystem, and real-world recommendations. GPUs for AI in 2026: NVIDIA, AMD, Intel Compared

GPUs for AI in 2026: NVIDIA, AMD, Intel Compared

Compare NVIDIA Blackwell, AMD Radeon AI Pro R9700, and Intel Arc Pro B70 for local LLM inference. VRAM, bandwidth, software ecosystem, and real-world recommendations. GPUs for AI in 2026: NVIDIA, AMD, Intel Compared

Data Gravity: The Real Cost of API-First AI

Data gravity is the force pulling AI workflows toward one provider. The four-stage lock-in mechanism, a scoring checklist, and how to stay portable. Data Gravity: The Real Cost of API-First AI

GPUs for AI in 2026: NVIDIA, AMD, Intel Compared

Compare NVIDIA Blackwell, AMD Radeon AI Pro R9700, and Intel Arc Pro B70 for local LLM inference. VRAM, bandwidth, software ecosystem, and real-world recommendations. GPUs for AI in 2026: NVIDIA, AMD, Intel Compared

Qwen 3.6 27B and 35B MTP vs Standard on 16GB GPU

Benchmark results for Qwen 3.6 27B and 35B MTP speculative decoding in llama.cpp on RTX 4080 16GB. Token speed, VRAM cost, and optimal --spec-draft-n-max settings. Qwen 3.6 27B and 35B MTP vs Standard on 16GB GPU

16 GB VRAM LLM benchmarks with llama.cpp (speed and context)

Compare llama.cpp speeds on a 16 GB GPU for dense and MoE models at 19K, 32K, and 64K context. Tables list VRAM, GPU load, and tokens per second. 16 GB VRAM LLM benchmarks with llama.cpp (speed and context)

LLM Self-Hosting and AI Sovereignty

Why and how self-hosted LLMs support AI sovereignty: control, data residency, and compliance for orgs and nations. LLM Self-Hosting and AI Sovereignty

16 GB VRAM LLM benchmarks with llama.cpp (speed and context)

Compare llama.cpp speeds on a 16 GB GPU for dense and MoE models at 19K, 32K, and 64K context. Tables list VRAM, GPU load, and tokens per second. 16 GB VRAM LLM benchmarks with llama.cpp (speed and context)

RTX 5090 in Australia March 2026 Pricing Stock Reality

RTX 5090 GPUs in Australia remain scarce and expensive in March 2026, with limited stock, long wait times, and inflated prices. Here is what is really happening and what comes next. RTX 5090 in Australia March 2026 Pricing Stock Reality

16 GB VRAM LLM benchmarks with llama.cpp (speed and context)

Compare llama.cpp speeds on a 16 GB GPU for dense and MoE models at 19K, 32K, and 64K context. Tables list VRAM, GPU load, and tokens per second. 16 GB VRAM LLM benchmarks with llama.cpp (speed and context)

Best Linux Terminal Emulators: 2026 Comparison

Compare top Linux terminal emulators: Alacritty, Kitty, WezTerm, GNOME Terminal, and more. Features, performance, and customization options reviewed. Best Linux Terminal Emulators: 2026 Comparison

Comparing LLMs performance on Ollama on 16GB VRAM GPU

Benchmark of 14 LLMs on RTX 4080 16GB with Ollama 0.15.2. Compare tokens/sec, VRAM usage, and CPU offloading for GPT-OSS, Qwen3, Qwen3.5, Mistral, and more. Comparing LLMs performance on Ollama on 16GB VRAM GPU

LLM Performance and PCIe Lanes: Key Considerations

LLM Performance and PCIe Lanes: Key Considerations LLM Performance and PCIe Lanes: Key Considerations

Chunking Strategies in RAG Comparison: Alternatives, Trade‑offs, and Examples

A rigorous, engineering‑first guide to chunking for RAG: fixed vs semantic vs hierarchical chunking, evaluation dimensions, decision matrix, and runnable Python implementations with FAISS/Chroma/Weaviate and OpenAI embeddings. Chunking Strategies in RAG Comparison: Alternatives, Trade‑offs, and Examples