What is local-inference-optimizer?

A troubleshooting guide for running language models on your own computer with tools such as Ollama, LM Studio, or vLLM.

It is for checking memory bandwidth, graphics memory, model size, and compression settings when choosing or tuning local models.

It helps explain slow responses, GPU not being used, and whether a model will fit in available memory.

local-inference-optimizer — Skill by yamaru-eu/hardware-probe

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add yamaru-eu/hardware-probe --skill local-inference-optimizer

Clone the repo

git clone --depth 1 https://github.com/yamaru-eu/hardware-probe

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/yamaru-eu/hardware-probe/local-inference-optimizer/github.svg)](https://agentmods.dev/skills/yamaru-eu/hardware-probe/local-inference-optimizer)

Your own site

<a href="https://agentmods.dev/skills/yamaru-eu/hardware-probe/local-inference-optimizer"><img src="https://agentmods.dev/badge/skills/yamaru-eu/hardware-probe/local-inference-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/yamaru-eu/hardware-probe/local-inference-optimizer"><img src="https://agentmods.dev/badge/skills/yamaru-eu/hardware-probe/local-inference-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 714 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00023	$0.00714
Opus 5.5	$0.00009	$0.00286
Sonnet 5.5	$0.00005	$0.00143
Haiku 4.5	$0.00002	$0.00071

Measured 1mo ago against content hash ab731d5d14aa, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

local-inference-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/local-inference-optimizer/SKILL.md · 53 lines

How it starts

The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Local Inference Optimizer

Expert system for analyzing local hardware/software topology and providing actionable recommendations for LLM inference (Ollama, LM Studio, vLLM).

Context

Use this skill when a user wants to:

Optimize their machine for local LLMs.
Troubleshoot slow inference speeds.
Understand why a model isn't using the GPU.
Calculate if a specific model/quantization will fit.

Diagnostic Protocol

1. Memory Bandwidth Analysis

Rule: Inference speed is directly bound by Memory Bandwidth (GB/s).
Heuristics:
- < 20 GB/s: Slow (CPU/Single Channel DDR4). Recommend small models (3B) or hardware upgrade.
- 20-60 GB/s: Standard (Dual Channel DDR4/DDR5). Good for 7B-14B models.
- 100 GB/s: High Performance (Apple M-Series, Quad Channel, or High-end GPU VRAM).
Advice: If memory_bandwidth_gbs is significantly lower than theoretical specs, suggest checking RAM slots (Dual Channel) or BIOS XMP/EXPO profiles.

2. VRAM & Quantization Strategy

Rule: Model + KV Cache must fit in VRAM for 10x speedup.
Formulas:
- ModelSize = (Params * Quant) / 8 (e.g., 7B @ 4-bit ≈ 3.5GB).
- KVCache = ContextLength * Params * 0.0000006 (Rough estimate).
Optimization: If VRAM is tight, suggest reducing num_ctx (context length) before dropping quantization quality.

3. Runtime Troubleshooting (WSL/Docker/Ollama)

WSL2: If memory_limit_gb in .wslconfig is < 50% of total RAM, suggest increasing it.
Docker: If hasNvidiaRuntime is false, provide the nvidia-container-toolkit installation steps.
Environment: If OLLAMA_NUM_PARALLEL is missing, suggest setting it to 2 for multi-agent workflows.

4. Hardware Health (Thermals & Storage)

Thermals: If inference starts fast but slows down over time, call thermal_profile to check for frequency clipping or overheating.
Storage: If model loading is slow, call check_storage_health to verify if the model is on a slow HDD vs NVMe SSD.

Read the full file on GitHub · 53 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 53 lines · 23 tokens per session scan A ab731d5d14aa

Subscribe to this mod's changes

local-inference-optimizer is a skill published in the GitHub repository yamaru-eu/hardware-probe (7 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 714 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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