### Define Model and Hardware Configuration Presets

Source: https://context7.com/erans/selfhostllm/llms.txt

This HTML structure defines model options with associated memory requirements and quantization levels. It includes support for Mixture-of-Experts (MoE) active memory specifications and standard dropdown presets for quantization and context length.

```html
<!-- Standard model configuration -->
<option value="70" data-memory="140" data-quant="1.0">Llama 3 70B (~140GB)</option>

<!-- MoE model with active memory (uses ~74GB instead of full 1342GB) -->
<option value="37" data-memory="1342" data-active-memory="74" data-quant="0.5">
    DeepSeek V3 671B (~1342GB total, ~74GB active)
</option>

<!-- Quantization options -->
<select id="quantization">
    <option value="1.0">FP16/BF16 (Full Precision)</option>
    <option value="0.75">INT8 (~25% reduction)</option>
    <option value="0.5">INT4 (~50% reduction)</option>
    <option value="0.3">MXFP4 (~70% reduction)</option>
    <option value="0.25">Extreme Quant (~75% reduction)</option>
</select>

<!-- Context length presets -->
<select id="context-preset">
    <option value="4096">4K tokens</option>
    <option value="8192">8K tokens</option>
    <option value="32768">32K tokens</option>
    <option value="131072">128K tokens</option>
    <option value="1048576">1M tokens</option>
</select>
```

--------------------------------

### Manage URL Configuration Sharing

Source: https://context7.com/erans/selfhostllm/llms.txt

Functions to serialize hardware and model settings into URL query parameters and restore them upon page load. This enables users to share specific calculator configurations via direct links without requiring a backend database.

```javascript
function updateURL() {
    const params = new URLSearchParams();
    const gpuType = document.getElementById('gpu-type').value;
    if (gpuType) params.set('gpu', gpuType);
    params.set('gpu_count', document.getElementById('gpu-count').value);
    params.set('sys_overhead', document.getElementById('system-overhead').value);
    const modelInputType = document.querySelector('input[name="model-input-type"]:checked').value;
    params.set('model_type', modelInputType);
    if (modelInputType === 'preset') {
        params.set('model', document.getElementById('model-preset').value);
    } else if (modelInputType === 'parameters') {
        params.set('model_params', document.getElementById('model-parameters').value);
    } else if (modelInputType === 'memory') {
        params.set('model_memory', document.getElementById('model-memory-input').value);
    }
    params.set('quant', document.getElementById('quantization').value);
    params.set('context', document.getElementById('context-preset').value);
    params.set('kv_cache', document.getElementById('kv-cache-overhead').value);
    const newURL = window.location.pathname + '?' + params.toString();
    window.history.replaceState({}, '', newURL);
}

function loadFromURL() {
    const params = new URLSearchParams(window.location.search);
    if (params.has('gpu')) document.getElementById('gpu-type').value = params.get('gpu');
    if (params.has('gpu_count')) document.getElementById('gpu-count').value = params.get('gpu_count');
    if (params.has('quant')) document.getElementById('quantization').value = params.get('quant');
    calculate();
}
```

--------------------------------

### Calculate LLM Memory Requirements

Source: https://context7.com/erans/selfhostllm/llms.txt

This snippet demonstrates the core mathematical formula used to determine the maximum number of concurrent requests an LLM deployment can handle based on GPU memory and KV cache overhead.

```javascript
const totalVRAM = gpuCount * vramPerGpu;
const adjustedModelMemory = modelMemory * quantization;
const kvCachePerRequest = (contextLength * adjustedModelMemory * kvCacheOverhead) / 1000;
const availableMemory = totalVRAM - systemOverhead - adjustedModelMemory;
const maxConcurrentRequests = availableMemory / kvCachePerRequest;
```

--------------------------------

### Implement Searchable Select Component

Source: https://context7.com/erans/selfhostllm/llms.txt

A custom UI component that wraps standard inputs with filtering logic and keyboard navigation support. It improves user experience by allowing quick selection of hardware or model presets from large lists.

```javascript
function setupSearchableSelect(config) {
    const select = document.getElementById(config.id);
    const input = document.createElement('input');
    input.type = 'text';
    input.className = 'integrated-select-input';
    input.placeholder = config.placeholder || 'Select or type to filter...';
    input.setAttribute('role', 'combobox');
    input.addEventListener('input', () => {
        renderOptions(input.value);
        openMenu();
    });
    input.addEventListener('keydown', (event) => {
        if (event.key === 'ArrowDown') {
            highlightedIndex = Math.min(filteredOptions.length - 1, highlightedIndex + 1);
        }
        if (event.key === 'ArrowUp') {
            highlightedIndex = Math.max(0, highlightedIndex - 1);
        }
        if (event.key === 'Enter') {
            pickHighlighted();
        }
        if (event.key === 'Escape') {
            closeMenu();
        }
    });
}
```

--------------------------------

### Estimate GPU Performance for LLM Inference

Source: https://context7.com/erans/selfhostllm/llms.txt

Calculates the estimated tokens per second for a given GPU configuration. It accounts for memory bandwidth, model size, quantization levels, context length, and multi-GPU scaling factors.

```javascript
function calculatePerformance(modelMemory, quantization, contextLength, gpuModel, gpuCount) {
    const bandwidth = getGPUBandwidth(gpuModel) * gpuCount;
    if (!bandwidth) return null;

    const bandwidthMap = {
        'rtx4090': 1008,
        'rtx4080': 736,
        'rtx3090': 936,
        'a100': 1600,
        'a100-80': 2000,
        'h100': 3000,
        'rx7900xtx': 960
    };

    let efficiency = modelParams <= 7 ? 0.85 : modelParams <= 30 ? 0.7 : modelParams <= 70 ? 0.5 : 0.3;
    let quantBoost = quantization <= 0.25 ? 2.5 : quantization <= 0.3 ? 2.2 : quantization <= 0.5 ? 1.8 : quantization <= 0.75 ? 1.3 : 1.0;
    let contextImpact = contextLength >= 131072 ? 0.3 : contextLength >= 32768 ? 0.6 : contextLength >= 8192 ? 0.85 : 1.0;
    let multiGpuScaling = gpuCount > 1 ? 0.85 + (0.15 / gpuCount) : 1.0;

    const tokensPerSecond = (bandwidth / modelMemory) * efficiency * quantBoost * contextImpact * multiGpuScaling * 0.6;
    return { tokensPerSecond, bandwidth, efficiency, quantBoost, contextImpact };
}
```

--------------------------------

### Configure Cloudflare Worker for Static Assets

Source: https://context7.com/erans/selfhostllm/llms.txt

A serverless script for Cloudflare Workers that serves static assets from KV storage. It includes logic for URL redirection, aggressive caching headers, and CORS support for cross-origin requests.

```javascript
import { getAssetFromKV } from '@cloudflare/kv-asset-handler';

addEventListener('fetch', (event) => {
    event.respondWith(handleRequest(event));
});

async function handleRequest(event) {
    const url = new URL(event.request.url);
    const path = url.pathname;
    try {
        if (path === '/mac') {
            return Response.redirect(`${url.origin}/mac/`, 301);
        }
        const response = await getAssetFromKV(event, {
            cacheControl: {
                browserTTL: 31536000,
                edgeTTL: 86400,
            },
        });
        const headers = new Headers(response.headers);
        headers.set('Access-Control-Allow-Origin', '*');
        return new Response(response.body, {
            status: response.status,
            headers: headers,
        });
    } catch (e) {
        return new Response('Not Found', { status: 404 });
    }
}
```

--------------------------------

### Check Apple Silicon Mac Compatibility

Source: https://context7.com/erans/selfhostllm/llms.txt

Evaluates whether a specific LLM can run on an Apple Silicon Mac by comparing total memory requirements (model + KV cache) against available system RAM. It also provides a helper to retrieve memory bandwidth for various M-series chips.

```javascript
function checkCompatibility() {
    const macRam = parseFloat(document.getElementById('mac-ram').value) || 16;
    const systemOverhead = parseFloat(document.getElementById('system-overhead').value) || 4;
    const quantization = parseFloat(document.getElementById('quantization').value) || 1.0;
    const contextLength = parseInt(document.getElementById('context-length').value) || 8192;

    const selectedOption = modelSelect.options[modelSelect.selectedIndex];
    const activeMemory = selectedOption.getAttribute('data-active-memory');
    const baseMemory = activeMemory ? parseFloat(activeMemory) : parseFloat(selectedOption.getAttribute('data-memory'));

    const modelMemory = baseMemory * quantization;
    const kvCachePerToken = modelMemory * 0.00002;
    const kvCacheMemory = contextLength * kvCachePerToken;
    const totalMemoryNeeded = modelMemory + kvCacheMemory;
    const availableRam = macRam - systemOverhead;
    const memoryMargin = availableRam - totalMemoryNeeded;

    return memoryMargin;
}

function getMacBandwidth(macModel) {
    const bandwidthMap = {
        'm5': 153, 'm4-max': 546, 'm4-pro': 273, 'm4': 120, 
        'm3-ultra': 819, 'm3-max': 400, 'm2-ultra': 800, 'm1-max': 400
    };
    return bandwidthMap[macModel] || 153;
}
```

--------------------------------

### Implement GPU Memory Calculation Function

Source: https://context7.com/erans/selfhostllm/llms.txt

This function retrieves user inputs from the DOM, calculates the required memory for a specific model configuration, and determines the concurrent request capacity. It handles various input types including model presets, parameter counts, and direct memory values.

```javascript
function calculate() {
    const gpuCount = parseInt(document.getElementById('gpu-count').value) || 1;
    const vramPerGpu = parseFloat(document.getElementById('vram-per-gpu').value) || 0;
    const systemOverhead = parseFloat(document.getElementById('system-overhead').value) || 2;
    let modelMemory;
    const modelInputType = document.querySelector('input[name="model-input-type"]:checked').value;
    if (modelInputType === 'preset') {
        const modelSelect = document.getElementById('model-preset');
        const selectedOption = modelSelect.options[modelSelect.selectedIndex];
        const activeMemory = selectedOption.getAttribute('data-active-memory');
        modelMemory = activeMemory ? parseFloat(activeMemory) : parseFloat(selectedOption.getAttribute('data-memory'));
    } else if (modelInputType === 'parameters') {
        const paramCount = parseFloat(document.getElementById('model-parameters').value) || 7;
        modelMemory = paramCount * 2;
    } else {
        modelMemory = parseFloat(document.getElementById('model-memory-input').value) || 14;
    }
    const quantization = parseFloat(document.getElementById('quantization').value);
    const kvCacheOverhead = parseFloat(document.getElementById('kv-cache-overhead').value) / 100;
    const totalVRAM = gpuCount * vramPerGpu;
    const adjustedModelMemory = modelMemory * quantization;
    const kvCachePerRequest = (contextLength * adjustedModelMemory * kvCacheOverhead) / 1000;
    const availableMemory = totalVRAM - systemOverhead - adjustedModelMemory;
    const maxConcurrentRequests = availableMemory / kvCachePerRequest;
}
```

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