diff --git a/scripts/chat_web_cpu.py b/scripts/chat_web_cpu.py
new file mode 100644
index 0000000..0d7ed82
--- /dev/null
+++ b/scripts/chat_web_cpu.py
@@ -0,0 +1,479 @@
+#!/usr/bin/env python3
+"""
+CPU-compatible web chat server - serves both UI and API from a single FastAPI instance.
+Run with: python chat_web_cpu.py --model-dir /path/to/model
+Then open http://localhost:8000 in your browser.
+"""
+
+import argparse
+import json
+import os
+import glob
+import pickle
+import math
+import torch
+from contextlib import asynccontextmanager
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse, HTMLResponse, FileResponse
+from pydantic import BaseModel
+from typing import List, Optional, AsyncGenerator
+from dataclasses import dataclass
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+# -----------------------------------------------------------------------------
+# Minimal GPT implementation (copied from generate_cpu.py)
+
+@dataclass
+class GPTConfig:
+    sequence_len: int = 1024
+    vocab_size: int = 50304
+    n_layer: int = 12
+    n_head: int = 6
+    n_kv_head: int = 6
+    n_embd: int = 768
+
+
+def norm(x):
+    return F.rms_norm(x, (x.size(-1),))
+
+
+def apply_rotary_emb(x, cos, sin):
+    assert x.ndim == 4
+    d = x.shape[3] // 2
+    x1, x2 = x[..., :d], x[..., d:]
+    y1 = x1 * cos + x2 * sin
+    y2 = x1 * (-sin) + x2 * cos
+    out = torch.cat([y1, y2], 3)
+    out = out.to(x.dtype)
+    return out
+
+
+def repeat_kv(x, n_rep):
+    if n_rep == 1:
+        return x
+    bs, n_kv_heads, slen, head_dim = x.shape
+    return (
+        x[:, :, None, :, :]
+        .expand(bs, n_kv_heads, n_rep, slen, head_dim)
+        .reshape(bs, n_kv_heads * n_rep, slen, head_dim)
+    )
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.n_head = config.n_head
+        self.n_kv_head = config.n_kv_head
+        self.n_embd = config.n_embd
+        self.head_dim = self.n_embd // self.n_head
+        assert self.n_embd % self.n_head == 0
+        assert self.n_kv_head <= self.n_head and self.n_head % self.n_kv_head == 0
+        self.c_q = nn.Linear(self.n_embd, self.n_head * self.head_dim, bias=False)
+        self.c_k = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
+        self.c_v = nn.Linear(self.n_embd, self.n_kv_head * self.head_dim, bias=False)
+        self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False)
+
+    def forward(self, x, cos_sin, kv_cache):
+        B, T, C = x.size()
+        q = self.c_q(x).view(B, T, self.n_head, self.head_dim)
+        k = self.c_k(x).view(B, T, self.n_kv_head, self.head_dim)
+        v = self.c_v(x).view(B, T, self.n_kv_head, self.head_dim)
+        cos, sin = cos_sin
+        q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
+        q, k = norm(q), norm(k)
+        q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
+        if kv_cache is not None:
+            k, v = kv_cache.insert_kv(self.layer_idx, k, v)
+        Tq = q.size(2)
+        Tk = k.size(2)
+        nrep = self.n_head // self.n_kv_head
+        k, v = repeat_kv(k, nrep), repeat_kv(v, nrep)
+        if kv_cache is None or Tq == Tk:
+            y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        elif Tq == 1:
+            y = F.scaled_dot_product_attention(q, k, v, is_causal=False)
+        else:
+            attn_mask = torch.zeros((Tq, Tk), dtype=torch.bool, device=q.device)
+            prefix_len = Tk - Tq
+            if prefix_len > 0:
+                attn_mask[:, :prefix_len] = True
+            attn_mask[:, prefix_len:] = torch.tril(torch.ones((Tq, Tq), dtype=torch.bool, device=q.device))
+            y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        y = y.transpose(1, 2).contiguous().view(B, T, -1)
+        y = self.c_proj(y)
+        return y
+
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False)
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=False)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = F.relu(x).square()
+        x = self.c_proj(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.attn = CausalSelfAttention(config, layer_idx)
+        self.mlp = MLP(config)
+
+    def forward(self, x, cos_sin, kv_cache):
+        x = x + self.attn(norm(x), cos_sin, kv_cache)
+        x = x + self.mlp(norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.transformer = nn.ModuleDict({
+            "wte": nn.Embedding(config.vocab_size, config.n_embd),
+            "h": nn.ModuleList([Block(config, layer_idx) for layer_idx in range(config.n_layer)]),
+        })
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.rotary_seq_len = config.sequence_len * 10
+        head_dim = config.n_embd // config.n_head
+        cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
+        self.register_buffer("cos", cos, persistent=False)
+        self.register_buffer("sin", sin, persistent=False)
+
+    def init_weights(self):
+        self.apply(self._init_weights)
+        torch.nn.init.zeros_(self.lm_head.weight)
+        for block in self.transformer.h:
+            torch.nn.init.zeros_(block.mlp.c_proj.weight)
+            torch.nn.init.zeros_(block.attn.c_proj.weight)
+        head_dim = self.config.n_embd // self.config.n_head
+        cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim)
+        self.cos, self.sin = cos, sin
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            fan_out = module.weight.size(0)
+            fan_in = module.weight.size(1)
+            std = 1.0 / math.sqrt(fan_in) * min(1.0, math.sqrt(fan_out / fan_in))
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=1.0)
+
+    def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=None):
+        if device is None:
+            device = self.transformer.wte.weight.device
+        channel_range = torch.arange(0, head_dim, 2, dtype=torch.float32, device=device)
+        inv_freq = 1.0 / (base ** (channel_range / head_dim))
+        t = torch.arange(seq_len, dtype=torch.float32, device=device)
+        freqs = torch.outer(t, inv_freq)
+        cos, sin = freqs.cos(), freqs.sin()
+        cos, sin = cos[None, :, None, :], sin[None, :, None, :]
+        return cos, sin
+
+    def forward(self, idx, targets=None, kv_cache=None):
+        B, T = idx.size()
+        assert T <= self.cos.size(1)
+        T0 = 0 if kv_cache is None else kv_cache.get_pos()
+        cos_sin = self.cos[:, T0:T0+T], self.sin[:, T0:T0+T]
+        x = self.transformer.wte(idx)
+        x = norm(x)
+        for block in self.transformer.h:
+            x = block(x, cos_sin, kv_cache)
+        x = norm(x)
+        softcap = 15
+        logits = self.lm_head(x)
+        logits = softcap * torch.tanh(logits / softcap)
+        return logits
+
+
+# -----------------------------------------------------------------------------
+# Simple tokenizer wrapper
+
+class SimpleTokenizer:
+    def __init__(self, enc):
+        self.enc = enc
+        try:
+            self.bos_token_id = enc.encode_single_token("<|bos|>")
+        except:
+            try:
+                self.bos_token_id = enc.encode_single_token("<|endoftext|>")
+            except:
+                self.bos_token_id = 0
+        
+        # Get special tokens
+        try:
+            self.user_start = enc.encode_single_token("<|user_start|>")
+            self.user_end = enc.encode_single_token("<|user_end|>")
+            self.assistant_start = enc.encode_single_token("<|assistant_start|>")
+            self.assistant_end = enc.encode_single_token("<|assistant_end|>")
+        except:
+            # Fallback if special tokens don't exist
+            self.user_start = 0
+            self.user_end = 0
+            self.assistant_start = 0
+            self.assistant_end = 0
+    
+    def get_bos_token_id(self):
+        return self.bos_token_id
+    
+    def encode_special(self, token):
+        try:
+            return self.enc.encode_single_token(token)
+        except:
+            return 0
+    
+    def encode(self, text):
+        return self.enc.encode_ordinary(text)
+    
+    def decode(self, tokens):
+        return self.enc.decode(tokens)
+
+
+# -----------------------------------------------------------------------------
+# Simple generator (no Engine class needed)
+
+def generate_tokens(model, input_tokens, max_tokens=512, temperature=0.8, top_k=50, device='cpu'):
+    """Generate tokens one at a time."""
+    x = torch.tensor([input_tokens], dtype=torch.long, device=device)
+    generated = []
+    
+    with torch.inference_mode():
+        for _ in range(max_tokens):
+            logits = model(x)
+            logits = logits[:, -1, :] / temperature
+            
+            if top_k > 0:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1)
+            
+            generated.append(next_token.item())
+            x = torch.cat([x, next_token], dim=1)
+            
+            yield next_token.item()
+
+
+# -----------------------------------------------------------------------------
+# FastAPI app
+
+parser = argparse.ArgumentParser(description='NanoChat Web Server (CPU)')
+parser.add_argument('--model-dir', type=str, required=True, help='Path to model directory containing model_*.pt, meta_*.json, and tokenizer.pkl')
+parser.add_argument('-t', '--temperature', type=float, default=0.8, help='Default temperature for generation')
+parser.add_argument('-k', '--top-k', type=int, default=50, help='Default top-k sampling parameter')
+parser.add_argument('-m', '--max-tokens', type=int, default=512, help='Default max tokens for generation')
+parser.add_argument('-p', '--port', type=int, default=8000, help='Port to run the server on')
+parser.add_argument('--host', type=str, default='0.0.0.0', help='Host to bind the server to')
+args = parser.parse_args()
+
+device = torch.device("cpu")
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    messages: List[ChatMessage]
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    top_k: Optional[int] = None
+    stream: Optional[bool] = True
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Load model on startup."""
+    print(f"Loading model from {args.model_dir}...")
+    
+    # Find model and meta files
+    model_files = glob.glob(os.path.join(args.model_dir, "model_*.pt"))
+    if not model_files:
+        raise FileNotFoundError(f"No model files found in {args.model_dir}")
+    model_file = model_files[0]
+    
+    meta_files = glob.glob(os.path.join(args.model_dir, "meta_*.json"))
+    if not meta_files:
+        raise FileNotFoundError(f"No meta files found in {args.model_dir}")
+    meta_file = meta_files[0]
+    
+    # Load metadata
+    with open(meta_file, 'r') as f:
+        meta = json.load(f)
+    
+    model_config_kwargs = meta["model_config"]
+    print(f"Model config: {model_config_kwargs}")
+    
+    # Build the model
+    model_config = GPTConfig(**model_config_kwargs)
+    with torch.device("meta"):
+        model = GPT(model_config)
+    
+    # Load model weights
+    print("Loading model weights...")
+    model_data = torch.load(model_file, map_location=device, weights_only=False)
+    model_data = {k.lstrip("_orig_mod."): v for k, v in model_data.items()}
+    
+    # Convert bfloat16 to float32 for CPU
+    print("Converting model to float32 for CPU...")
+    model_data = {k: v.float() if v.dtype == torch.bfloat16 else v for k, v in model_data.items()}
+    
+    model.to_empty(device=device)
+    model.init_weights()
+    model.load_state_dict(model_data, strict=True, assign=True)
+    model.eval()
+    
+    # Load tokenizer
+    print("Loading tokenizer...")
+    tokenizer_path = os.path.join(args.model_dir, "tokenizer.pkl")
+    if not os.path.exists(tokenizer_path):
+        raise FileNotFoundError(f"Tokenizer not found at {tokenizer_path}")
+    
+    with open(tokenizer_path, "rb") as f:
+        import tiktoken
+        enc = pickle.load(f)
+    
+    tokenizer = SimpleTokenizer(enc)
+    
+    app.state.model = model
+    app.state.tokenizer = tokenizer
+    
+    print(f"✓ Model loaded successfully!")
+    print(f"✓ Server ready at http://localhost:{args.port}")
+    yield
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/")
+async def root():
+    """Serve the chat UI."""
+    ui_html_path = os.path.join("nanochat", "ui.html")
+    with open(ui_html_path, "r") as f:
+        html_content = f.read()
+    # Replace the API_URL to use the same origin
+    html_content = html_content.replace(
+        "const API_URL = `http://${window.location.hostname}:8000`;",
+        "const API_URL = '';"
+    )
+    return HTMLResponse(content=html_content)
+
+
+@app.get("/logo.svg")
+async def logo():
+    """Serve the NanoChat logo for favicon and header."""
+    logo_path = os.path.join("nanochat", "logo.svg")
+    return FileResponse(logo_path, media_type="image/svg+xml")
+
+async def generate_stream(
+    model,
+    tokenizer,
+    tokens,
+    temperature=None,
+    max_new_tokens=None,
+    top_k=None
+) -> AsyncGenerator[str, None]:
+    """Generate assistant response with streaming."""
+    temperature = temperature if temperature is not None else args.temperature
+    max_new_tokens = max_new_tokens if max_new_tokens is not None else args.max_tokens
+    top_k = top_k if top_k is not None else args.top_k
+
+    assistant_end = tokenizer.encode_special("<|assistant_end|>")
+    bos = tokenizer.get_bos_token_id()
+
+    for token in generate_tokens(model, tokens, max_new_tokens, temperature, top_k, device):
+        if token == assistant_end or token == bos:
+            break
+
+        token_text = tokenizer.decode([token])
+        yield f"data: {json.dumps({'token': token_text})}\n\n"
+
+    yield f"data: {json.dumps({'done': True})}\n\n"
+
+@app.post("/chat/completions")
+async def chat_completions(request: ChatRequest):
+    """Chat completion endpoint with streaming."""
+    model = app.state.model
+    tokenizer = app.state.tokenizer
+
+    # Build conversation tokens
+    bos = tokenizer.get_bos_token_id()
+    user_start = tokenizer.encode_special("<|user_start|>")
+    user_end = tokenizer.encode_special("<|user_end|>")
+    assistant_start = tokenizer.encode_special("<|assistant_start|>")
+    assistant_end = tokenizer.encode_special("<|assistant_end|>")
+
+    conversation_tokens = [bos]
+    for message in request.messages:
+        if message.role == "user":
+            conversation_tokens.append(user_start)
+            conversation_tokens.extend(tokenizer.encode(message.content))
+            conversation_tokens.append(user_end)
+        elif message.role == "assistant":
+            conversation_tokens.append(assistant_start)
+            conversation_tokens.extend(tokenizer.encode(message.content))
+            conversation_tokens.append(assistant_end)
+
+    conversation_tokens.append(assistant_start)
+
+    if request.stream:
+        return StreamingResponse(
+            generate_stream(
+                model,
+                tokenizer,
+                conversation_tokens,
+                temperature=request.temperature,
+                max_new_tokens=request.max_tokens,
+                top_k=request.top_k
+            ),
+            media_type="text/event-stream"
+        )
+    else:
+        # Non-streaming response
+        temperature = request.temperature if request.temperature is not None else args.temperature
+        max_tokens = request.max_tokens if request.max_tokens is not None else args.max_tokens
+        top_k = request.top_k if request.top_k is not None else args.top_k
+
+        generated_tokens = list(generate_tokens(model, conversation_tokens, max_tokens, temperature, top_k, device))
+        response_text = tokenizer.decode(generated_tokens)
+        
+        return {
+            "choices": [{
+                "message": {
+                    "role": "assistant",
+                    "content": response_text
+                },
+                "finish_reason": "stop"
+            }]
+        }
+
+@app.get("/health")
+async def health():
+    """Health check endpoint."""
+    return {
+        "status": "ok",
+        "ready": hasattr(app.state, 'model') and app.state.model is not None
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    print(f"Starting NanoChat Web Server (CPU mode)")
+    print(f"Temperature: {args.temperature}, Top-k: {args.top_k}, Max tokens: {args.max_tokens}")
+    uvicorn.run(app, host=args.host, port=args.port)
+