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The Context-Length Tax: What Going 2K to 32K Actually Costs
Going from 2K to 32K context cost essentially 0 tok/s but +1.7 GB of VRAM. We swept Qwen2.5-Coder-7B across five context sizes — the tax is memory, not speed.
More CPU Threads Made My LLM Slower: A Thread-Scaling Test
Throughput peaked near the 6 physical cores on an i7-8700, and 12 threads ran slower than 8. Here's why memory bandwidth — not core count — decides how fast a CPU runs a local model.
Flash Attention in llama.cpp: -fa Is Free Because It's Already On
We swept llama.cpp's -fa flag across 4K, 16K, and 32K context on an RTX 5060 Ti. Speed and VRAM were identical on and off — because this build already defaults flash attention on.
How Low Can You Quantize a GGUF Model Before Quality Breaks?
We swept Qwen2.5-Coder-7B from Q2 to Q8 on an RTX 5060 Ti. Output fidelity goes 21% at Q2, 57% at Q4, 80% at Q6 — the cliff is below Q4, and the sweet spot is Q4–Q5.
I Capped My GPU to 150W and Barely Lost Any Speed
We cut an RTX 5060 Ti's power limit by 17% and lost essentially zero tokens per second while gaining 16% efficiency. Local LLM decoding is memory-bandwidth-bound, not compute-bound — so watts are mostly wasted.
KV-Cache Quantization: The q4_0 Cliff Your Logs Won't Warn You About
We benchmarked f16 vs q8_0 vs q4_0 KV caches on the same model. q8_0 KV is nearly lossless (81.6% similar). q4_0 KV wrecks output quality (8.3%) while saving VRAM — and your throughput dashboard stays green the whole time.
Local LLM Benchmarks: 12 Findings From Consumer Hardware
NVIDIA publishes its inference wins on 8× DGX B300s. Almost nobody has those. So we ran the same ideas on a single consumer RTX 5060 Ti, a 2017 GTX 1080 Ti, and a CPU — and measured what actually happens. Here are all 12 experiments.
MoE on CPU: 13B-Class Answers at 3B Speed
A 30B-A3B Mixture-of-Experts model runs at dense-3B speed on an 8-year-old i7 CPU (10.6 tok/s) yet scores 8/10 on our graded set versus the 3B's 1/10. Here's why, and how to size your own box for it.
Ollama vs llama.cpp vs LM Studio: The Speed Tax, Measured
LM Studio, Ollama, and raw llama.cpp all run the same engine on the same GPU. We measured what the convenience layer costs: LM Studio adds 0.3%, Ollama adds 10%.
How Small Can a Local LLM Get Before It Can't Reason?
We swept Qwen2.5-Coder from 0.5B to 14B on one GPU. Graded pass-rate climbs 1 to 6 of 10 — but you need about 7B before a model can actually chain reasoning steps.
Bigger Draft Model = Faster? A Speculative Decoding Sweep
We swept 0.5B → 3B draft models against a fixed 14B target. The 0.5B won at 1.37× — despite the lowest acceptance rate of the three. Here's why bigger drafts lose.
The VRAM Cliff: 15× Slower the Moment Layers Spill to CPU
We swept -ngl from 0 to 99 on a 14B model: 2.89 → 43 tok/s as it moves onto the GPU. Partial offload is a cliff, not a slope — and the last 8 layers matter most.
AI Agent Protocols Explained: MCP vs A2A vs ACP and the Agent Interoperability Stack
MCP and A2A are not rivals — they are complementary layers of the same stack: MCP connects an agent to tools and data, A2A connects agents to each other. Here is the whole interoperability landscape, with ACP, ANP, and AGNTCY put in their place.
Clustering Machines for Local AI: Running Big Models Across Your Network
When no single machine can hold the model — or you just have spare hardware lying around — you can cluster. Here's how distributed inference works with tools like exo and llama.cpp RPC, and where it helps versus where it doesn't.
diskpart Commands: Manage Disks and Partitions (2026)
Master diskpart commands to list disk, select disk, clean, create partition, and format fs=ntfs. Complete 2026 reference for Windows 10, 11 & Server — with hard safety warnings.
Edge Caching for LLM Requests: Stop Paying to Answer the Same Question Twice
A surprising share of LLM traffic is repeats — identical prompts re-run from scratch. Caching responses at the edge serves those instantly for near-zero cost. Here's how LLM caching works, what to cache, and the pitfalls.
gpupdate /force & Group Policy Commands: Refresh, Report & Remote (2026)
Force a Group Policy refresh from the command line with gpupdate /force, generate RSoP reports with gpresult, and push updates to remote computers with Invoke-GPUpdate. Complete 2026 reference for Windows 10, 11, and Server.
How Much VRAM Do You Need to Run an LLM? (The Memory Math, Explained)
The formula that tells you whether a model will fit on your GPU: parameters × quantization, plus the KV cache for your context, plus overhead. Worked examples for 8B, 13B, and 70B models — and the GPUs they fit on.
How to Run an LLM Locally: A Step-by-Step Guide for Beginners
Run a large language model on your own computer in about ten minutes — no cloud, no API keys, no per-token fees. Pick a runtime, download a model, and chat privately on hardware you own.
llama.cpp Speculative Decoding: Does It Work on Cheap GPUs?
We tested speculative decoding in llama.cpp on an RTX 5060 Ti, a GTX 1080 Ti, and a bare CPU. Real benchmarks: where the draft-model trick helps, and where it backfires.
The Hidden Memory Cost of Long Context: KV Cache and VRAM Explained
On a hosted API, a long context window costs you dollars. On your own GPU, it costs you VRAM — and it grows fast. Here's how the KV cache works, why doubling context can double your memory, and how to tame it.
LLM Quantization Explained: How to Shrink Models Without Wrecking Quality
Quantization is the dial that lets a 70B model fit on a consumer GPU. Here's what FP16, INT8, and 4-bit actually mean, what you lose at each level, and how to decode those cryptic Q4_K_M filenames.
Local LLM Performance: What Tokens-Per-Second to Expect From Your Hardware
Why local inference is memory-bandwidth bound, what tokens/sec you'll realistically get from a 4090, a 5090, an H100, or an M-series Mac, and how model size, quantization, and context change the numbers.
MCP Security Risks: A Practical Threat Model for Teams Connecting AI Agents to Tools
MCP isn't uniquely unsafe, but every server you connect widens your attack surface. A risk catalogue, the trust model you're actually accepting, and the governance controls MSPs and security teams should put in place.