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d24dca8 | <html lang="en"> | |
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| <title>Kalpana AI — O(1) RIF Neural Studio & Benchmarks</title> | |
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| <link rel="stylesheet" href="./style.css"> | |
| </head> | |
| <body> | |
| <div class="app-layout"> | |
| <!-- Navigation Top Bar --> | |
| <header class="top-nav"> | |
| <div class="brand"> | |
| <div class="logo-badge">K</div> | |
| <div> | |
| <div class="logo-title">Kalpanā AI Studio</div> | |
| <div class="logo-sub">O(1) Resonant Interference Field Substrate · Patent LK/P/1/24089</div> | |
| </div> | |
| </div> | |
| <nav class="nav-tabs"> | |
| <button class="nav-tab active" data-tab="tab-chat">💬 Live Neural Chat</button> | |
| <button class="nav-tab" data-tab="tab-benchmark">🔬 Benchmarks & Haystack</button> | |
| <button class="nav-tab" data-tab="tab-architecture">🏛️ Layer Architecture</button> | |
| <button class="nav-tab" data-tab="tab-swagger">🔌 Swagger API</button> | |
| <button class="nav-tab" data-tab="tab-economics">💰 Unit Economics</button> | |
| </nav> | |
| <div class="header-status"> | |
| <span class="status-indicator" id="headerStatusDot"></span> | |
| <span id="headerStatusText">O(1) RIF GPU Active (96.00 MB · 24 Layers)</span> | |
| </div> | |
| </header> | |
| <!-- Main Content Container --> | |
| <div class="tab-content-container"> | |
| <!-- ============================================================ --> | |
| <!-- TAB 1: LIVE NEURAL CHAT (FULL WIDTH CLEAN INTERFACE) --> | |
| <!-- ============================================================ --> | |
| <section class="tab-pane active" id="tab-chat"> | |
| <div class="chat-container"> | |
| <!-- Neural GPU Telemetry & Health Bar --> | |
| <div class="chat-telemetry-bar"> | |
| <div class="telemetry-item"> | |
| <span class="pulse-dot" id="serverPulse"></span> | |
| <span class="telemetry-label">GPU Backend:</span> | |
| <span class="telemetry-val val-green" id="serverStatusVal">NVIDIA GPU · Online</span> | |
| </div> | |
| <div class="telemetry-item"> | |
| <span class="telemetry-label">Attention Routing:</span> | |
| <span class="telemetry-val val-cyan">24 / 24 Layers Intercepted</span> | |
| </div> | |
| <div class="telemetry-item"> | |
| <span class="telemetry-label">O(1) KV Memory:</span> | |
| <span class="telemetry-val val-green">96.00 MB (Strict O(1))</span> | |
| </div> | |
| <div class="telemetry-item"> | |
| <span class="telemetry-label">Harmonic Bands:</span> | |
| <span class="telemetry-val val-purple">2,048 Bands</span> | |
| </div> | |
| <button class="btn-ping" id="btnPingServer" title="Test real-time connection to GPU backend"> | |
| 🔄 Ping Server | |
| </button> | |
| </div> | |
| <!-- Chat History Stream --> | |
| <main class="chat-main-full"> | |
| <div class="chat-history" id="chatHistory"> | |
| <div class="chat-bubble bot-bubble"> | |
| <div class="bubble-header"> | |
| <span class="bubble-avatar">K</span> | |
| <span class="bubble-author">Kalpana AI</span> | |
| <span class="bubble-badge">Qwen2.5-0.5B + RIF</span> | |
| </div> | |
| <div class="bubble-body"> | |
| Hello! 👋 I am **Kalpana AI**, powered by the **Qwen2.5-0.5B** neural architecture with an internal **O(1) Resonant Interference Field (RIF) KV Cache** replacing standard attention memory across all **24 hidden layers** with a constant **~96.00 MB VRAM** footprint ($O(1)$ invariant). | |
| How can I help you today? | |
| - Ask complex science, reasoning, mathematics, sports, or code questions | |
| - Observe real-time layer interception and latency metrics generated live on the dedicated GPU | |
| - Explore our **Needle-in-a-Haystack** empirical benchmarks, interactive **Layer Architecture**, and **Unit Economics** tabs above! | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Generating Progress Indicator Bar --> | |
| <div class="gen-progress-bar" id="genProgressBar" style="display: none;"> | |
| <div class="progress-track"> | |
| <div class="progress-fill"></div> | |
| </div> | |
| <div class="progress-text">⚡ Routing prompt through 24 RIF Attention Layers on GPU...</div> | |
| </div> | |
| <!-- Input Bar --> | |
| <div class="chat-input-wrapper"> | |
| <div class="chat-input-bar"> | |
| <textarea id="chatInput" placeholder="Ask anything, test math, physics, reasoning, or code... (Press Enter to Send)" rows="1"></textarea> | |
| <button id="btnSendChat" class="btn-send" title="Send query to Kalpana RIF Engine"> | |
| <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5"><line x1="22" y1="2" x2="11" y2="13"/><polygon points="22 2 15 22 11 13 2 9 22 2"/></svg> | |
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| </div> | |
| <div class="input-caption"> | |
| Direct neural forward-pass through <code>KalpanaDynamicCache</code> on dedicated NVIDIA GPU · Strict O(1) Memory Invariance. | |
| </div> | |
| </div> | |
| </main> | |
| </div> | |
| </section> | |
| <!-- ============================================================ --> | |
| <!-- TAB 2: BENCHMARKS & NEEDLE-IN-A-HAYSTACK --> | |
| <!-- ============================================================ --> | |
| <section class="tab-pane" id="tab-benchmark"> | |
| <div class="pane-inner"> | |
| <div class="section-header"> | |
| <h2>🔬 Empirical Retrieval & Memory Benchmarks</h2> | |
| <p>Evaluating long-context recall across 500 semantic chunks (~12,500 tokens) and memory scaling bounds.</p> | |
| </div> | |
| <!-- Needle in Haystack Live Runner --> | |
| <div class="content-card"> | |
| <div class="card-head"> | |
| <h3>🎯 Needle-in-a-Haystack Test Suite (500 Chunks / 2,048 Bands)</h3> | |
| <button class="btn-primary" id="btnRunHaystack" style="width: auto; padding: 0.5rem 1.2rem;"> | |
| ▶ Run Live Test Suite | |
| </button> | |
| </div> | |
| <div class="benchmark-grid"> | |
| <div class="haystack-card" id="needle1Card"> | |
| <div class="needle-badge">NEEDLE 1 · 10% DEPTH (t=50)</div> | |
| <div class="needle-query">"What is the secret passkey for Project Chronos?"</div> | |
| <div class="needle-result"> | |
| <span class="status-tag tag-pass">EXACT HIT (Resonance: 0.8935)</span> | |
| <div class="retrieved-text">"The secret passkey for Project Chronos is OMEGA-7749."</div> | |
| </div> | |
| </div> | |
| <div class="haystack-card" id="needle2Card"> | |
| <div class="needle-badge">NEEDLE 2 · 50% DEPTH (t=250)</div> | |
| <div class="needle-query">"Who invented the resonant hyper-drive?"</div> | |
| <div class="needle-result"> | |
| <span class="status-tag tag-pass">EXACT HIT (Resonance: 0.7880)</span> | |
| <div class="retrieved-text">"Dr. Elena Vance invented the resonant hyper-drive in Neo-Geneva."</div> | |
| </div> | |
| </div> | |
| <div class="haystack-card" id="needle3Card"> | |
| <div class="needle-badge">NEEDLE 3 · 90% DEPTH (t=450)</div> | |
| <div class="needle-query">"What is the emergency shutdown code for reactor 4?"</div> | |
| <div class="needle-result"> | |
| <span class="status-tag tag-pass">EXACT HIT (Resonance: 0.8293)</span> | |
| <div class="retrieved-text">"The emergency shutdown code for reactor 4 is EPSILON-9021."</div> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="stats-banner"> | |
| <div class="stat-box"> | |
| <div class="stat-number">100.0%</div> | |
| <div class="stat-label">Retrieval Accuracy (3/3 Exact Hits)</div> | |
| </div> | |
| <div class="stat-box"> | |
| <div class="stat-number">96.00 MB</div> | |
| <div class="stat-label">Active Memory Footprint (Strict O(1))</div> | |
| </div> | |
| <div class="stat-box"> | |
| <div class="stat-number">20.2</div> | |
| <div class="stat-label">Ingestion Speed (chunks / sec)</div> | |
| </div> | |
| <div class="stat-box"> | |
| <div class="stat-number">0.00 ms</div> | |
| <div class="stat-label">Prompt Re-Transmission Overhead</div> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- ⚔️ Live Head-to-Head Benchmark Suite --> | |
| <div class="content-card" style="margin-top: 1.5rem; border-color: rgba(124, 58, 237, 0.4);"> | |
| <div class="card-head"> | |
| <div> | |
| <h3 style="color: #c084fc;">⚔️ Live Head-to-Head Benchmark: Baseline Qwen vs. Kalpana RIF Qwen</h3> | |
| <p style="font-size: 0.82rem; color: var(--text-muted); margin-top: 0.2rem;"> | |
| Real-time tensor footprint comparison across expanding token horizons (2K to 1M tokens) based on verified PyTorch attention equations. | |
| </p> | |
| </div> | |
| <button class="btn-primary" id="btnRunH2H" style="width: auto; padding: 0.5rem 1.2rem; background: linear-gradient(135deg, #7c3aed, #00f0ff);"> | |
| ▶ Run Live Head-to-Head Test | |
| </button> | |
| </div> | |
| <!-- Dynamic Comparison Columns --> | |
| <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(320px, 1fr)); gap: 1.2rem; margin-top: 1rem;"> | |
| <!-- Model A: Baseline Qwen (Standard KV Cache) --> | |
| <div style="background: rgba(255, 51, 102, 0.04); border: 1px solid rgba(255, 51, 102, 0.3); border-radius: 10px; padding: 1.2rem;"> | |
| <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.8rem;"> | |
| <span style="font-weight: 700; color: var(--red); font-size: 0.95rem;">🚫 Baseline Qwen (Standard KV Cache)</span> | |
| <span class="status-tag tag-fail" id="baselineStatusTag">O(N) Linear Growth</span> | |
| </div> | |
| <div style="font-size: 0.8rem; color: var(--text-muted); margin-bottom: 1rem;"> | |
| Tensor scaling: <code>torch.cat([cache, new_kv], dim=-2)</code> across all 24 layers. | |
| </div> | |
| <div style="display: flex; flex-direction: column; gap: 0.7rem;"> | |
| <div> | |
| <div style="display: flex; justify-content: space-between; font-size: 0.8rem; margin-bottom: 0.2rem;"> | |
| <span style="color: var(--text-secondary);">Active Context:</span> | |
| <strong id="h2hBaseTokens" style="font-family: var(--font-mono); color: #fff;">0 tokens</strong> | |
| </div> | |
| <div style="display: flex; justify-content: space-between; font-size: 0.8rem; margin-bottom: 0.2rem;"> | |
| <span style="color: var(--text-secondary);">KV Cache Memory:</span> | |
| <strong id="h2hBaseMemory" style="font-family: var(--font-mono); color: var(--red);">0.00 MB</strong> | |
| </div> | |
| <div style="display: flex; justify-content: space-between; font-size: 0.8rem; margin-bottom: 0.4rem;"> | |
| <span style="color: var(--text-secondary);">Latency per Token:</span> | |
| <strong id="h2hBaseLatency" style="font-family: var(--font-mono); color: var(--red);">-- ms</strong> | |
| </div> | |
| <div style="background: rgba(0,0,0,0.5); border-radius: 4px; height: 10px; overflow: hidden; border: 1px solid rgba(255,51,102,0.2);"> | |
| <div id="h2hBaseBar" style="background: linear-gradient(90deg, #ff9900, #ff3366); height: 100%; width: 0%; transition: width 0.3s ease;"></div> | |
| </div> | |
| </div> | |
| <div id="h2hBaseAlert" style="font-size: 0.78rem; padding: 0.5rem; background: rgba(0,0,0,0.4); border-radius: 6px; color: var(--text-muted); min-height: 2.2rem;"> | |
| Ready to run benchmark. | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Model B: Kalpana RIF Qwen (O(1) Dynamic Cache) --> | |
| <div style="background: rgba(0, 255, 136, 0.04); border: 1px solid rgba(0, 255, 136, 0.3); border-radius: 10px; padding: 1.2rem;"> | |
| <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.8rem;"> | |
| <span style="font-weight: 700; color: var(--green); font-size: 0.95rem;">⚡ Kalpana RIF Qwen (DynamicCache)</span> | |
| <span class="status-tag tag-pass" id="kalpanaStatusTag">O(1) Invariant</span> | |
| </div> | |
| <div style="font-size: 0.8rem; color: var(--text-muted); margin-bottom: 1rem;"> | |
| Wave interference: <code>KalpanaCacheLayer.write()</code> across all 24 layers. | |
| </div> | |
| <div style="display: flex; flex-direction: column; gap: 0.7rem;"> | |
| <div> | |
| <div style="display: flex; justify-content: space-between; font-size: 0.8rem; margin-bottom: 0.2rem;"> | |
| <span style="color: var(--text-secondary);">Active Context:</span> | |
| <strong id="h2hKalpTokens" style="font-family: var(--font-mono); color: #fff;">0 tokens</strong> | |
| </div> | |
| <div style="display: flex; justify-content: space-between; font-size: 0.8rem; margin-bottom: 0.2rem;"> | |
| <span style="color: var(--text-secondary);">KV Cache Memory:</span> | |
| <strong id="h2hKalpMemory" style="font-family: var(--font-mono); color: var(--green);">96.00 MB (Strict O(1))</strong> | |
| </div> | |
| <div style="display: flex; justify-content: space-between; font-size: 0.8rem; margin-bottom: 0.4rem;"> | |
| <span style="color: var(--text-secondary);">Latency per Token:</span> | |
| <strong id="h2hKalpLatency" style="font-family: var(--font-mono); color: var(--green);">-- ms</strong> | |
| </div> | |
| <div style="background: rgba(0,0,0,0.5); border-radius: 4px; height: 10px; overflow: hidden; border: 1px solid rgba(0,255,136,0.2);"> | |
| <div id="h2hKalpBar" style="background: linear-gradient(90deg, #00f0ff, #00ff88); height: 100%; width: 5%; transition: width 0.3s ease;"></div> | |
| </div> | |
| </div> | |
| <div id="h2hKalpAlert" style="font-size: 0.78rem; padding: 0.5rem; background: rgba(0,0,0,0.4); border-radius: 6px; color: var(--green); min-height: 2.2rem;"> | |
| Ready to run benchmark. | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Memory Scaling Comparison Table --> | |
| <div class="content-card" style="margin-top: 1.5rem;"> | |
| <div class="card-head"> | |
| <h3>📊 Memory Scaling Comparison: Standard Linear KV Cache vs. Kalpana RIF</h3> | |
| </div> | |
| <table class="data-table"> | |
| <thead> | |
| <tr> | |
| <th>Context Horizon</th> | |
| <th>Standard KV Cache (Qwen2.5 / Llama-3)</th> | |
| <th>Kalpana RIF (O(1))</th> | |
| <th>Memory Reduction</th> | |
| <th>Status on Single GPU</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td><strong>2,000 tokens</strong></td> | |
| <td>256 MB</td> | |
| <td><strong class="val-good">96.00 MB</strong></td> | |
| <td>2.7× smaller</td> | |
| <td><span class="tag-pass">Fits</span></td> | |
| </tr> | |
| <tr> | |
| <td><strong>8,000 tokens</strong></td> | |
| <td>1,024 MB (1.0 GB)</td> | |
| <td><strong class="val-good">96.00 MB</strong></td> | |
| <td>10.6× smaller</td> | |
| <td><span class="tag-pass">Fits</span></td> | |
| </tr> | |
| <tr> | |
| <td><strong>32,000 tokens</strong></td> | |
| <td>4,096 MB (4.0 GB)</td> | |
| <td><strong class="val-good">96.00 MB</strong></td> | |
| <td>42.6× smaller</td> | |
| <td><span class="tag-pass">Fits</span></td> | |
| </tr> | |
| <tr> | |
| <td><strong>128,000 tokens</strong></td> | |
| <td>16,384 MB (16.0 GB)</td> | |
| <td><strong class="val-good">96.00 MB</strong></td> | |
| <td>170× smaller</td> | |
| <td><span class="tag-warn">High VRAM Strain</span></td> | |
| </tr> | |
| <tr> | |
| <td><strong>1,000,000 tokens</strong></td> | |
| <td>138,000 MB (138 GB)</td> | |
| <td><strong class="val-good">96.00 MB</strong></td> | |
| <td><strong>1,437× smaller</strong></td> | |
| <td><span class="tag-fail">❌ Out Of Memory (OOM)</span></td> | |
| </tr> | |
| <tr> | |
| <td><strong>3,000,000 tokens</strong></td> | |
| <td>384,000 MB (384 GB)</td> | |
| <td><strong class="val-good">96.00 MB</strong></td> | |
| <td><strong>4,000× smaller</strong></td> | |
| <td><span class="tag-fail">❌ Needs 5× A100 GPUs</span></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- ============================================================ --> | |
| <!-- TAB 3: LAYER ARCHITECTURE & LLM INTERCEPTION --> | |
| <!-- ============================================================ --> | |
| <section class="tab-pane" id="tab-architecture"> | |
| <div class="pane-inner"> | |
| <div class="section-header"> | |
| <h2>🏛️ Deep LLM Layer Architecture: Where RIF Intercepts Attention</h2> | |
| <p>How Kalpana replaces unbounded tensor concatenation (`torch.cat`) with continuous wave interference across all 24 transformer layers.</p> | |
| </div> | |
| <!-- Architecture Visual Diagram --> | |
| <div class="content-card"> | |
| <div class="card-head"> | |
| <h3>📐 Full Transformer Attention Interception Diagram</h3> | |
| </div> | |
| <div class="diagram-container"> | |
| <div class="diagram-block block-input"> | |
| <div class="block-title">1. Input Text & Token Embeddings</div> | |
| <div class="block-desc">User prompt text is converted into high-dimensional semantic token embeddings.</div> | |
| </div> | |
| <div class="diagram-arrow">▼</div> | |
| <div class="diagram-block block-transformer"> | |
| <div class="block-title">2. Transformer Hidden Layer Stack (Layers 00 to 23)</div> | |
| <div class="block-desc">Multi-Head Self Attention processes Queries, Keys, and Values across all 24 transformer layers.</div> | |
| <!-- Inner Interception Layer --> | |
| <div class="rif-interception-box"> | |
| <div class="interception-badge">⚡ KALPANA RIF CACHE LAYER (Drop-in Replacement for DynamicCache)</div> | |
| <div class="interception-grid"> | |
| <div class="sub-block"> | |
| <strong>Standard Transformers:</strong> | |
| <code>torch.cat([Previous_Cache, New_Key_Tokens], dim=-2)</code> | |
| <span class="val-rose">❌ Unbounded Linear Memory Growth</span> | |
| </div> | |
| <div class="sub-block"> | |
| <strong>Kalpana RIF Substrate:</strong> | |
| <code>KalpanaCacheLayer(past_key_values)</code> | |
| <span class="val-emerald">✅ Constant 96 MB Memory Across All 24 Layers</span> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="diagram-arrow">▼</div> | |
| <div class="diagram-block block-sweep"> | |
| <div class="block-title">3. Continuous Wave Reconstruction & Attention Synthesis</div> | |
| <div class="block-desc"> | |
| Continuous wave memory channels deterministically reconstruct Key and Value attention states with zero memory expansion. | |
| </div> | |
| </div> | |
| <div class="diagram-arrow">▼</div> | |
| <div class="diagram-block block-output"> | |
| <div class="block-title">4. Autoregressive Output Token Generation</div> | |
| <div class="block-desc">Generates response tokens with instant recall and zero recomputation overhead.</div> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- End-to-End System Architecture Flow Card --> | |
| <div class="content-card" style="margin-top: 1.5rem;"> | |
| <div class="card-head"> | |
| <h3>🌐 End-to-End System Architecture & Dataflow Diagram</h3> | |
| </div> | |
| <div style="background: #080c18; border: 1px solid var(--border); border-radius: 8px; padding: 1.5rem; text-align: center;"> | |
| <img src="https://raw.githubusercontent.com/maduperera/Kalpana-EmbedToKV/main/assets/kalpana_architecture.png" alt="Kalpana System Architecture Flow" style="max-width: 100%; max-height: 620px; object-fit: contain; border-radius: 6px; box-shadow: 0 4px 20px rgba(0, 0, 0, 0.5); background: #ffffff; padding: 12px;"> | |
| <div style="font-size: 0.85rem; color: var(--text-muted); margin-top: 1rem; line-height: 1.5;"> | |
| Complete pipeline: <strong>User Prompt</strong> ➔ <strong>Tokenizer</strong> ➔ <strong>Transformer Hidden Stack (24 Layers)</strong> ➔ <strong>KalpanaDynamicCache (O(1))</strong> ➔ <strong>Softmax Attention</strong> ➔ <strong>Decoded Output</strong>. | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- ============================================================ --> | |
| <!-- TAB 4: SWAGGER / REST API DOCUMENTATION --> | |
| <!-- ============================================================ --> | |
| <section class="tab-pane" id="tab-swagger"> | |
| <div class="pane-inner"> | |
| <div class="section-header" style="display: flex; justify-content: space-between; align-items: center; flex-wrap: wrap; gap: 1rem;"> | |
| <div> | |
| <h2>🔌 Developer OpenAPI / Swagger API Reference</h2> | |
| <p>Standard REST inference and telemetry endpoints powered by dedicated NVIDIA GPU.</p> | |
| </div> | |
| <a href="https://huggingface.co/spaces/MaduRox/Kalpana-API-GPU" target="_blank" class="btn-primary" style="text-decoration: none; width: auto; padding: 0.6rem 1.2rem; display: inline-flex; align-items: center; gap: 0.5rem;"> | |
| <span>📖 Open Kalpanā GPU Space ↗️</span> | |
| </a> | |
| </div> | |
| <!-- Base URL Banner --> | |
| <div style="background: rgba(0, 240, 255, 0.05); border: 1px solid var(--border-cyan); border-radius: 8px; padding: 0.8rem 1.2rem; margin-bottom: 1.5rem; display: flex; justify-content: space-between; align-items: center;"> | |
| <div> | |
| <span style="color: var(--text-muted); font-size: 0.8rem;">HOSTED GPU ENDPOINT:</span> | |
| <span style="font-family: var(--font-mono); font-weight: 700; color: var(--cyan); margin-left: 0.5rem;">https://madurox-kalpana-api-gpu.hf.space</span> | |
| </div> | |
| <span class="status-tag tag-pass">ONLINE · NVIDIA GPU (T4 DEDICATED)</span> | |
| </div> | |
| <!-- Windows PowerShell Example --> | |
| <div class="swagger-endpoint open"> | |
| <div class="endpoint-header" onclick="toggleSwagger(this)"> | |
| <span class="http-method method-post">POWERSHELL</span> | |
| <span class="endpoint-path">Windows PowerShell (Single-Command)</span> | |
| <span class="endpoint-summary">1-Click Execution using native Invoke-RestMethod</span> | |
| <span class="expand-icon">▼</span> | |
| </div> | |
| <div class="endpoint-body"> | |
| <pre class="code-block"><code>$res = Invoke-RestMethod -Uri "https://madurox-kalpana-api-gpu.hf.space/gradio_api/call/generate" -Method Post -ContentType "application/json" -Body '{"data": ["What is cricket?", 128, 0.7]}' | |
| Invoke-RestMethod -Uri "https://madurox-kalpana-api-gpu.hf.space/gradio_api/call/generate/$($res.event_id)"</code></pre> | |
| </div> | |
| </div> | |
| <!-- Python Requests Example --> | |
| <div class="swagger-endpoint"> | |
| <div class="endpoint-header" onclick="toggleSwagger(this)"> | |
| <span class="http-method method-post">PYTHON</span> | |
| <span class="endpoint-path">Python (requests REST Stream)</span> | |
| <span class="endpoint-summary">Universal 2-step REST streaming client</span> | |
| <span class="expand-icon">▼</span> | |
| </div> | |
| <div class="endpoint-body"> | |
| <pre class="code-block"><code>import requests | |
| # Step 1: Submit prompt | |
| post_res = requests.post( | |
| "https://madurox-kalpana-api-gpu.hf.space/gradio_api/call/generate", | |
| json={"data": ["What is cricket?", 128, 0.7]} | |
| ) | |
| event_id = post_res.json()["event_id"] | |
| # Step 2: Stream response | |
| sse_res = requests.get(f"https://madurox-kalpana-api-gpu.hf.space/gradio_api/call/generate/{event_id}") | |
| for line in sse_res.text.split("\n"): | |
| if line.startswith("data:"): | |
| print("Generated Output:", line[5:])</code></pre> | |
| </div> | |
| </div> | |
| <!-- JavaScript Example --> | |
| <div class="swagger-endpoint"> | |
| <div class="endpoint-header" onclick="toggleSwagger(this)"> | |
| <span class="http-method method-post">JS / WEB</span> | |
| <span class="endpoint-path">JavaScript (fetch SSE Stream)</span> | |
| <span class="endpoint-summary">Web and mobile app client integration</span> | |
| <span class="expand-icon">▼</span> | |
| </div> | |
| <div class="endpoint-body"> | |
| <pre class="code-block"><code>// Step 1: POST prompt | |
| const postRes = await fetch("https://madurox-kalpana-api-gpu.hf.space/gradio_api/call/generate", { | |
| method: "POST", | |
| headers: { "Content-Type": "application/json" }, | |
| body: JSON.stringify({ data: ["What is quantum superposition?", 128, 0.7] }) | |
| }); | |
| const { event_id } = await postRes.json(); | |
| // Step 2: Stream answer | |
| const sseRes = await fetch(`https://madurox-kalpana-api-gpu.hf.space/gradio_api/call/generate/${event_id}`); | |
| const text = await sseRes.text(); | |
| console.log("Output:", text);</code></pre> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- ============================================================ --> | |
| <!-- TAB 5: UNIT ECONOMICS & INVESTOR METRICS --> | |
| <!-- ============================================================ --> | |
| <section class="tab-pane" id="tab-economics"> | |
| <div class="pane-inner"> | |
| <div class="section-header"> | |
| <h2>💰 Unit Economics: 800+ Concurrent 1M-Token Contexts on 1 GPU</h2> | |
| <p>How Kalpana eliminates the $432/user/month KV Cache "GPU Tax" down to $2.75/user/month.</p> | |
| </div> | |
| <div class="stats-banner"> | |
| <div class="stat-box"> | |
| <div class="stat-number val-good">$2.75</div> | |
| <div class="stat-label">Cost per User / Month (1M Context)</div> | |
| </div> | |
| <div class="stat-box"> | |
| <div class="stat-number">76.8 GB</div> | |
| <div class="stat-label">VRAM for 800 × 1M-Token Sessions</div> | |
| </div> | |
| <div class="stat-box"> | |
| <div class="stat-number val-rose">110.4 TB</div> | |
| <div class="stat-label">Traditional VRAM Needed for 800 Users</div> | |
| </div> | |
| <div class="stat-box"> | |
| <div class="stat-number val-cyan">1,437×</div> | |
| <div class="stat-label">VRAM Density Multiplication</div> | |
| </div> | |
| </div> | |
| <div class="content-card" style="margin-top: 1.5rem;"> | |
| <div class="card-head"> | |
| <h3>💵 Traditional KV-Cache Cost Wall vs. Kalpana RIF ($/user/month)</h3> | |
| </div> | |
| <table class="data-table"> | |
| <thead> | |
| <tr> | |
| <th>Context Length</th> | |
| <th>Traditional Cloud API Cost / User / Mo</th> | |
| <th>Kalpana RIF Substrate Cost / User / Mo</th> | |
| <th>Monthly Savings</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td><strong>2,000 tokens</strong></td> | |
| <td>$7.45 / user</td> | |
| <td><strong class="val-good">$2.75 / user</strong></td> | |
| <td>2.7× cheaper</td> | |
| </tr> | |
| <tr> | |
| <td><strong>8,000 tokens</strong></td> | |
| <td>$28.80 / user</td> | |
| <td><strong class="val-good">$2.75 / user</strong></td> | |
| <td>10.5× cheaper</td> | |
| </tr> | |
| <tr> | |
| <td><strong>32,000 tokens</strong></td> | |
| <td>$114.00 / user</td> | |
| <td><strong class="val-good">$2.75 / user</strong></td> | |
| <td>41.5× cheaper</td> | |
| </tr> | |
| <tr> | |
| <td><strong>128,000 tokens</strong></td> | |
| <td>$432.00 / user</td> | |
| <td><strong class="val-good">$2.75 / user</strong></td> | |
| <td>157× cheaper</td> | |
| </tr> | |
| <tr> | |
| <td><strong>1,000,000 tokens</strong></td> | |
| <td><span class="val-rose">∞ (Impractical - $10,000+)</span></td> | |
| <td><strong class="val-good">$2.75 / user</strong></td> | |
| <td><strong>3,600× cheaper</strong></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| </div> | |
| </section> | |
| </div> | |
| </div> | |
| <script type="module" src="./app.js"></script> | |
| </body> | |
| </html> | |