NVFP4 vs FP8 vs MXFP4 vs EXL3: The Quantization Format Guide for Local LLMs
The same 320B model is 328 GB in FP8 and near 135 GB at 4 bits. Bytes-per-parameter math, per-format tradeoffs, and how to size a box that actually fits.
· 13 minCovers AI application security, model risk, and agentic system abuse.
The same 320B model is 328 GB in FP8 and near 135 GB at 4 bits. Bytes-per-parameter math, per-format tradeoffs, and how to size a box that actually fits.
· 13 minA stability-first SGLang deployment serves DeepSeek V4.1 Flash across three NVIDIA DGX Sparks: 256k context limit, 750k-token KV pool, ~38 tok/s single-stream decode.
· 3 minFrom one Spark to four: the open-weight model to serve at each node count, with measured decode, prefill and context figures from Mia's AI Lab recipes.
· 10 minPrompt injection resists a model-layer fix. Inside the agent control plane: workload identity, brokered credentials and blast-radius caps at the tool call.
· 12 minA practical systems-level explanation of Claude Code as a local agent runtime, covering startup, authentication, tools, permissions, sessions, MCP, plugins, and the Agent SDK.
· 13 minCVE-2026-33017 exposes Langflow AI workflow builders to unauthenticated remote code execution through the public flow build endpoint.
· 6 minQwen3.6-35B-A3B leads 2026 open-weight AI picks for 128GB Apple Silicon Macs, balancing coding, reasoning, long context and local speed for developers.
· 6 minA 48GB Apple Silicon Mac is a strong local AI machine for quantized 24B-32B open-weight models, led by Qwen3-Coder-30B-A3B for coding and Gemma 4 for reasoning and RAG.
· 7 min