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| """ | |
| Aether -- Pure Engine Speed Comparison | |
| Same model. Same prompt. Same tokens. Different engine. | |
| PyTorch CPU vs Aether WASM-SIMD. Let the ms/tok speak. | |
| """ | |
| import gradio as gr | |
| import torch | |
| import json | |
| import time | |
| import subprocess | |
| import urllib.request | |
| import urllib.error | |
| import select | |
| from concurrent.futures import ThreadPoolExecutor, as_completed | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| print("[Aether] Starting Aether sidecar...", flush=True) | |
| aether_proc = subprocess.Popen( | |
| ["node", "aether-server.mjs"], | |
| env={**__import__('os').environ, "AETHER_PORT": "7861"}, | |
| stdout=subprocess.PIPE, stderr=subprocess.STDOUT, | |
| ) | |
| print("[Aether] Loading PyTorch model...", flush=True) | |
| tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct") | |
| pytorch_model = AutoModelForCausalLM.from_pretrained( | |
| "HuggingFaceTB/SmolLM2-360M-Instruct", | |
| torch_dtype=torch.float32, device_map="cpu", | |
| ) | |
| print("[Aether] PyTorch ready.", flush=True) | |
| print("[Aether] Waiting for Aether engine...", flush=True) | |
| for attempt in range(180): | |
| try: | |
| req = urllib.request.Request("http://127.0.0.1:7861/health") | |
| resp = urllib.request.urlopen(req, timeout=2) | |
| health = json.loads(resp.read()) | |
| if health.get("status") == "ok" and health.get("model") == "loaded": | |
| print(f"[Aether] Engine ready ({health.get('loadTime')}ms, SIMD: {health.get('simd')})", flush=True) | |
| break | |
| except Exception: | |
| pass | |
| if aether_proc.stdout and select.select([aether_proc.stdout], [], [], 0)[0]: | |
| line = aether_proc.stdout.readline() | |
| if line: print(f" {line.decode().strip()}", flush=True) | |
| time.sleep(1) | |
| def gen_pytorch(prompt, max_tokens): | |
| messages = [{"role": "user", "content": prompt}] | |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
| inputs = tokenizer(text, return_tensors="pt") | |
| t0 = time.perf_counter() | |
| with torch.no_grad(): | |
| outputs = pytorch_model.generate( | |
| **inputs, max_new_tokens=max_tokens, temperature=0.7, top_p=0.9, | |
| do_sample=True, pad_token_id=tokenizer.eos_token_id, | |
| ) | |
| elapsed = time.perf_counter() - t0 | |
| n = outputs.shape[1] - inputs["input_ids"].shape[1] | |
| text = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip() | |
| return text, elapsed, n, (elapsed * 1000 / n) if n > 0 else 0 | |
| def gen_aether(prompt, max_tokens): | |
| try: | |
| data = json.dumps({"prompt": prompt, "max_tokens": max_tokens}).encode() | |
| req = urllib.request.Request("http://127.0.0.1:7861/generate", data=data, | |
| headers={"Content-Type": "application/json"}) | |
| resp = urllib.request.urlopen(req, timeout=600) | |
| r = json.loads(resp.read()) | |
| return r["text"], r["totalTimeMs"] / 1000, r["tokens"], r["avgTokenMs"] | |
| except urllib.error.HTTPError as e: | |
| body = e.read().decode() if e.fp else str(e) | |
| try: detail = json.loads(body).get("error", body[:300]) | |
| except Exception: detail = body[:300] | |
| return f"[Error: {detail}]", 0, 0, 0 | |
| except Exception as e: | |
| return f"[Error: {e}]", 0, 0, 0 | |
| def compare(prompt, max_tokens): | |
| empty = ("", "", "", "") | |
| if not prompt or not prompt.strip(): | |
| yield empty | |
| return | |
| max_tokens = int(max_tokens) | |
| pt_result = [None] | |
| ae_result = [None] | |
| def run_pt(): | |
| pt_result[0] = gen_pytorch(prompt, max_tokens) | |
| def run_ae(): | |
| ae_result[0] = gen_aether(prompt, max_tokens) | |
| def fmt(r): | |
| if not r: return "running..." | |
| return f"{r[2]} tokens in {r[1]:.1f}s ({r[3]:.0f}ms/tok)" | |
| def build(): | |
| pt, ae = pt_result[0], ae_result[0] | |
| return ( | |
| pt[0] if pt else "generating...", | |
| ae[0] if ae else "generating...", | |
| fmt(pt), fmt(ae), | |
| ) | |
| with ThreadPoolExecutor(max_workers=2) as pool: | |
| futures = {pool.submit(run_pt): "pt", pool.submit(run_ae): "ae"} | |
| for future in as_completed(futures): | |
| future.result() | |
| yield build() | |
| yield build() | |
| CSS = """ | |
| .gradio-container { max-width: 1060px !important; margin: 0 auto !important; } | |
| .gradio-container, .dark { background: #09090b !important; } | |
| #hero { text-align: center; padding: 2rem 0 1rem; } | |
| #hero h1 { font-size: 2.5rem; font-weight: 300; letter-spacing: -0.02em; color: #fafafa; margin: 0; } | |
| #hero .accent { color: #06b6d4; } | |
| #hero .subtitle { color: #71717a; font-size: 0.95rem; margin-top: 0.5rem; } | |
| .response-card { background: #0c0c0f !important; border: 1px solid #1f1f23 !important; border-radius: 8px !important; } | |
| .response-card textarea { background: #0c0c0f !important; border: none !important; color: #e4e4e7 !important; font-size: 0.95rem !important; line-height: 1.6 !important; } | |
| .pt-label { color: #71717a !important; font-size: 0.8rem !important; text-transform: uppercase !important; letter-spacing: 0.05em !important; font-weight: 500 !important; } | |
| .ae-label { color: #06b6d4 !important; font-size: 0.8rem !important; text-transform: uppercase !important; letter-spacing: 0.05em !important; font-weight: 500 !important; } | |
| .stats-text { font-family: 'SF Mono', 'Fira Code', monospace !important; font-size: 0.85rem !important; color: #52525b !important; } | |
| #prompt-input > label > span { display: none !important; } | |
| #prompt-input textarea { background: #111114 !important; border: 1px solid #1f1f23 !important; border-radius: 8px !important; color: #fafafa !important; font-size: 1rem !important; padding: 1rem !important; } | |
| #prompt-input textarea:focus { border-color: #06b6d4 !important; } | |
| #gen-btn { background: #06b6d4 !important; border: none !important; border-radius: 8px !important; font-weight: 500 !important; font-size: 0.9rem !important; padding: 0.75rem 2rem !important; color: #09090b !important; } | |
| .prompt-chip { background: #111114 !important; border: 1px solid #1f1f23 !important; border-radius: 6px !important; color: #a1a1aa !important; font-size: 0.85rem !important; } | |
| .prompt-chip:hover { border-color: #06b6d4 !important; color: #fafafa !important; } | |
| #footer { text-align: center; padding: 2rem 0; border-top: 1px solid #1f1f23; margin-top: 2rem; } | |
| #footer p { color: #52525b; font-size: 0.8rem; } | |
| #footer a { color: #06b6d4; text-decoration: none; } | |
| footer.svelte-1ax1toq { display: none !important; } | |
| .built-with { display: none !important; } | |
| """ | |
| with gr.Blocks(css=CSS, theme=gr.themes.Base(primary_hue="cyan", neutral_hue="zinc"), title="Aether") as demo: | |
| gr.HTML(""" | |
| <div id="hero"> | |
| <h1><span class="accent">Aether</span></h1> | |
| <p class="subtitle">Pure engine speed comparison. Same model (SmolLM2-360M-Instruct). Same prompt. Same tokens.<br/> | |
| Left: PyTorch CPU (2.8GB runtime, CUDA/MKL optimized).<br/> | |
| Right: Aether (14KB WASM binary, pure JS + SIMD128, zero ML dependencies).<br/> | |
| Both generate in parallel. Whichever finishes first shows first.</p> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| prompt = gr.Textbox(elem_id="prompt-input", placeholder="What is the shape of failure?", lines=2, label="Prompt", show_label=False, interactive=True, scale=4) | |
| max_tok = gr.Slider(minimum=8, maximum=8192, value=64, step=1, label="Max tokens", scale=1) | |
| btn = gr.Button("Generate", elem_id="gen-btn", variant="primary") | |
| with gr.Row(equal_height=True): | |
| with gr.Column(): | |
| gr.HTML('<p class="pt-label">PyTorch CPU (standard)</p>') | |
| pt_out = gr.Textbox(lines=10, show_label=False, interactive=False, elem_classes=["response-card"]) | |
| pt_stats = gr.HTML('<p class="stats-text">--</p>') | |
| with gr.Column(min_width=30): | |
| gr.HTML('<p style="color:#27272a; text-align:center; padding-top:4rem; font-size:0.75rem; letter-spacing:0.1em;">VS</p>') | |
| with gr.Column(): | |
| gr.HTML('<p class="ae-label">Aether WASM-SIMD (14KB)</p>') | |
| ae_out = gr.Textbox(lines=10, show_label=False, interactive=False, elem_classes=["response-card"]) | |
| ae_stats = gr.HTML('<p class="stats-text">--</p>') | |
| outputs = [pt_out, ae_out, pt_stats, ae_stats] | |
| inputs = [prompt, max_tok] | |
| def run(p, mt): | |
| for pt, ae, ps, aes in compare(p, mt): | |
| yield pt, ae, f'<p class="stats-text">{ps}</p>', f'<p class="stats-text">{aes}</p>' | |
| btn.click(run, inputs, outputs) | |
| prompt.submit(run, inputs, outputs) | |
| gr.HTML('<p style="color:#52525b; font-size:0.8rem; margin-top:1.5rem; margin-bottom:0.5rem;">Try these:</p>') | |
| with gr.Row(): | |
| for p in ["hello", "What is the shape of failure?", "Write a haiku about parallel universes.", "Explain entropy to a five-year-old."]: | |
| gr.Button(p, size="sm", elem_classes=["prompt-chip"]).click( | |
| fn=lambda x=p: x, outputs=[prompt] | |
| ).then(fn=run, inputs=inputs, outputs=outputs) | |
| gr.HTML(""" | |
| <div id="footer"> | |
| <p style="color:#a1a1aa; font-size:0.85rem; margin-bottom:0.5rem;"> | |
| SmolLM2-360M-Instruct · Q8_0 GGUF · Same weights, different engines | |
| </p> | |
| <p> | |
| PyTorch: ~2.8GB runtime, C++/CUDA/MKL optimized, Python bindings<br/> | |
| Aether: 14KB WASM + JS, SIMD128 vectorized, zero dependencies, runs anywhere | |
| </p> | |
| <p style="margin-top:1rem;"> | |
| <a href="https://forkracefold.com/">Whitepaper</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/aether">Aether</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/aether-browser">Edge Mesh</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/the-void">The Void</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/buleyean-rl">Buleyean RL</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/glossolalia">Glossolalia</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/metacog">Metacog</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/five-bules">Five Bules</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/void-attention">Void Attention</a> · | |
| <a href="https://huggingface.co/spaces/forkjoin-ai/quark-personality">Quark Personality</a> | |
| </p> | |
| <p style="margin-top:1rem;">Copyright 2026 forkjoin.ai</p> | |
| </div> | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |