llmfit Hardware Model Matcher

Skill by ara.so — Daily 2026 Skills collection.

llmfit detects your system's RAM, CPU, and GPU then scores hundreds of LLM models across quality, speed, fit, and context dimensions — telling you exactly which models will run well on your hardware. It ships with an interactive TUI and a CLI, supports multi-GPU, MoE architectures, dynamic quantization, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner).

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Installation

macOS / Linux (Homebrew)

brew install llmfit

Quick install script

curl -fsSL https://llmfit.axjns.dev/install.sh | sh

# Without sudo, installs to ~/.local/bin
curl -fsSL https://llmfit.axjns.dev/install.sh | sh -s -- --local

Windows (Scoop)

scoop install llmfit

Docker / Podman

docker run ghcr.io/alexsjones/llmfit

# With jq for scripting
podman run ghcr.io/alexsjones/llmfit recommend --use-case coding | jq '.models[].name'

From source (Rust)

git clone https://github.com/AlexsJones/llmfit.git
cd llmfit
cargo build --release
# binary at target/release/llmfit

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Core Concepts

• Fit tiers: perfect (runs great), good (runs well), marginal (runs but tight), too_tight (won't run)
• Scoring dimensions: quality, speed (tok/s estimate), fit (memory headroom), context capacity
• Run modes: GPU, CPU+GPU offload, CPU-only, MoE
• Quantization: automatically selects best quant (e.g. Q4_K_M, Q5_K_S, mlx-4bit) for your hardware
• Providers: Ollama, llama.cpp, MLX, Docker Model Runner

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Key Commands

Launch Interactive TUI

llmfit

CLI Table Output

llmfit --cli

Show System Hardware Detection

llmfit system
llmfit --json system   # JSON output

List All Models

llmfit list

Search Models

llmfit search "llama 8b"
llmfit search "mistral"
llmfit search "qwen coding"

Fit Analysis

# All runnable models ranked by fit
llmfit fit

# Only perfect fits, top 5
llmfit fit --perfect -n 5

# JSON output
llmfit --json fit -n 10

Model Detail

llmfit info "Mistral-7B"
llmfit info "Llama-3.1-70B"

Recommendations

# Top 5 recommendations (JSON default)
llmfit recommend --json --limit 5

# Filter by use case: general, coding, reasoning, chat, multimodal, embedding
llmfit recommend --json --use-case coding --limit 3
llmfit recommend --json --use-case reasoning --limit 5

Hardware Planning (invert: what hardware do I need?)

llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192
llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192 --quant mlx-4bit
llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192 --target-tps 25 --json
llmfit plan "Qwen/Qwen2.5-Coder-0.5B-Instruct" --context 8192 --json

REST API Server (for cluster scheduling)

llmfit serve
llmfit serve --host 0.0.0.0 --port 8787

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Hardware Overrides

When autodetection fails (VMs, broken nvidia-smi, passthrough setups):

# Override GPU VRAM
llmfit --memory=32G
llmfit --memory=24G --cli
llmfit --memory=24G fit --perfect -n 5
llmfit --memory=24G recommend --json

# Megabytes
llmfit --memory=32000M

# Works with any subcommand
llmfit --memory=16G info "Llama-3.1-70B"

Accepted suffixes: G/GB/GiB, M/MB/MiB, T/TB/TiB (case-insensitive).

Context Length Cap

# Estimate memory fit at 4K context
llmfit --max-context 4096 --cli

# With subcommands
llmfit --max-context 8192 fit --perfect -n 5
llmfit --max-context 16384 recommend --json --limit 5

# Environment variable alternative
export OLLAMA_CONTEXT_LENGTH=8192
llmfit recommend --json

---

REST API Reference

Start the server:

llmfit serve --host 0.0.0.0 --port 8787

Endpoints

# Health check
curl http://localhost:8787/health

# Node hardware info
curl http://localhost:8787/api/v1/system

# Full model list with filters
curl "http://localhost:8787/api/v1/models?min_fit=marginal&runtime=llamacpp&sort=score&limit=20"

# Top runnable models for this node (key scheduling endpoint)
curl "http://localhost:8787/api/v1/models/top?limit=5&min_fit=good&use_case=coding"

# Search by model name/provider
curl "http://localhost:8787/api/v1/models/Mistral?runtime=any"

Query Parameters for /models and /models/top

Param	Values	Description
limit / n	integer	Max rows returned
min_fit	perfect|good|marginal|too_tight	Minimum fit tier
perfect	true|false	Force perfect-only
runtime	any|mlx|llamacpp	Filter by runtime
use_case	general|coding|reasoning|chat|multimodal|embedding	Use case filter
provider	string	Substring match on provider
search	string	Free-text across name/provider/size/use-case
sort	score|tps|params|mem|ctx|date|use_case	Sort column
include_too_tight	true|false	Include non-runnable models
max_context	integer	Per-request context cap

---

Scripting & Automation Examples

Bash: Get top coding models as JSON

#!/bin/bash
# Get top 3 coding models that fit perfectly
llmfit recommend --json --use-case coding --limit 3 | \
  jq -r '.models[] | "\(.name) (\(.score)) - \(.quantization)"'

Bash: Check if a specific model fits

#!/bin/bash
MODEL="Mistral-7B"
RESULT=$(llmfit info "$MODEL" --json 2>/dev/null)
FIT=$(echo "$RESULT" | jq -r '.fit')
if [[ "$FIT" == "perfect" || "$FIT" == "good" ]]; then
  echo "$MODEL will run well (fit: $FIT)"
else
  echo "$MODEL may not run well (fit: $FIT)"
fi

Bash: Auto-pull top Ollama model

#!/bin/bash
# Get the top fitting model name and pull it with Ollama
TOP_MODEL=$(llmfit recommend --json --limit 1 | jq -r '.models[0].name')
echo "Pulling: $TOP_MODEL"
ollama pull "$TOP_MODEL"

Python: Query the REST API

import requests

BASE_URL = "http://localhost:8787"

def get_system_info():
    resp = requests.get(f"{BASE_URL}/api/v1/system")
    return resp.json()

def get_top_models(use_case="coding", limit=5, min_fit="good"):
    params = {
        "use_case": use_case,
        "limit": limit,
        "min_fit": min_fit,
        "sort": "score"
    }
    resp = requests.get(f"{BASE_URL}/api/v1/models/top", params=params)
    return resp.json()

def search_models(query, runtime="any"):
    resp = requests.get(
        f"{BASE_URL}/api/v1/models/{query
how to use llmfit-hardware-model-matcher
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use llmfit-hardware-model-matcher on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add llmfit-hardware-model-matcher
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/aradotso/trending-skills --skill llmfit-hardware-model-matchercopy
The skills CLI fetches llmfit-hardware-model-matcher from GitHub repository aradotso/trending-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/llmfit-hardware-model-matcher
Reload or restart Cursor to activate llmfit-hardware-model-matcher. Access the skill through slash commands (e.g., /llmfit-hardware-model-matcher) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?