diff --git a/studio/backend/core/inference/audio_codecs.py b/studio/backend/core/inference/audio_codecs.py
index 3a418d921d..f033e6c77c 100644
--- a/studio/backend/core/inference/audio_codecs.py
+++ b/studio/backend/core/inference/audio_codecs.py
@@ -302,6 +302,22 @@ class AudioCodecManager:
         waveform = audio.squeeze().cpu().numpy()
         return _numpy_to_wav_bytes(waveform, 24000), 24000
 
+    def decode(
+        self,
+        audio_type: str,
+        device: str,
+        token_ids: Optional[list] = None,
+        text: Optional[str] = None,
+    ) -> Tuple[bytes, int]:
+        """Unified decode — dispatches to the right codec decoder."""
+        if audio_type == "snac":
+            return self.decode_snac(torch.tensor([token_ids], dtype = torch.long), device)
+        elif audio_type == "bicodec":
+            return self.decode_bicodec(text, device)
+        elif audio_type == "dac":
+            return self.decode_dac(text, device)
+        raise ValueError(f"Cannot decode audio_type: {audio_type}")
+
     # ── Cleanup ──────────────────────────────────────────────────
 
     def unload(self) -> None:
diff --git a/studio/backend/core/inference/llama_cpp.py b/studio/backend/core/inference/llama_cpp.py
index 3ed4589dc0..ba32a81fad 100644
--- a/studio/backend/core/inference/llama_cpp.py
+++ b/studio/backend/core/inference/llama_cpp.py
@@ -981,3 +981,110 @@ class LlamaCppBackend:
             if cancel_event is not None and cancel_event.is_set():
                 return
             raise
+
+    # ── TTS support ────────────────────────────────────────────
+
+    def detect_audio_type(self) -> Optional[str]:
+        """Detect audio/TTS codec by probing the loaded model's vocabulary."""
+        if not self.is_loaded:
+            return None
+        try:
+            with httpx.Client(timeout = 10) as client:
+                def _detok(tid: int) -> str:
+                    r = client.post(f"{self.base_url}/detokenize", json = {"tokens": [tid]})
+                    return r.json().get("content", "") if r.status_code == 200 else ""
+
+                def _tok(text: str) -> list[int]:
+                    r = client.post(f"{self.base_url}/tokenize", json = {"content": text, "add_special": False})
+                    return r.json().get("tokens", []) if r.status_code == 200 else []
+
+                # Check codec-specific tokens (not generic ones that may exist in non-audio models)
+                if "<custom_token_" in _detok(128258) and "<custom_token_" in _detok(128259):
+                    return "snac"
+                if len(_tok("<|AUDIO|>")) == 1 and len(_tok("<|audio_eos|>")) == 1:
+                    return "csm"
+                if len(_tok("<|startoftranscript|>")) == 1:
+                    return "whisper"
+                if len(_tok("<|bicodec_semantic_0|>")) == 1 and len(_tok("<|bicodec_global_0|>")) == 1:
+                    return "bicodec"
+                if len(_tok("<|c1_0|>")) == 1 and len(_tok("<|c2_0|>")) == 1:
+                    return "dac"
+        except Exception as e:
+            logger.debug(f"Audio type detection failed: {e}")
+        return None
+
+    # Prompt format per codec: (template, stop_tokens, needs_token_ids)
+    # Matches prompts in InferenceBackend._generate_snac/bicodec/dac
+    _TTS_PROMPTS = {
+        "snac":    ("<custom_token_3>{text}<|eot_id|><custom_token_4>", ["<custom_token_2>"], True),
+        "bicodec": ("<|task_tts|><|start_content|>{text}<|end_content|><|start_global_token|>", ["<|im_end|>", "</s>"], False),
+        "dac":     ("<|im_start|>\n<|text_start|>{text}<|text_end|>\n<|audio_start|><|global_features_start|>\n", ["<|im_end|>", "<|audio_end|>"], False),
+    }
+
+    _codec_mgr = None  # Shared AudioCodecManager instance
+
+    def init_audio_codec(self, audio_type: str) -> None:
+        """Load the audio codec at model load time (mirrors non-GGUF path)."""
+        import torch
+        from core.inference.audio_codecs import AudioCodecManager
+
+        if LlamaCppBackend._codec_mgr is None:
+            LlamaCppBackend._codec_mgr = AudioCodecManager()
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        model_repo_path = None
+
+        # BiCodec needs a repo with BiCodec/ weights — download canonical SparkTTS
+        if audio_type == "bicodec":
+            from huggingface_hub import snapshot_download
+            import os
+            repo_path = snapshot_download("unsloth/Spark-TTS-0.5B", local_dir = "Spark-TTS-0.5B")
+            model_repo_path = os.path.abspath(repo_path)
+
+        LlamaCppBackend._codec_mgr.load_codec(audio_type, device, model_repo_path = model_repo_path)
+        logger.info(f"Loaded audio codec for GGUF TTS: {audio_type}")
+
+    def generate_audio_response(
+        self,
+        text: str,
+        audio_type: str,
+        temperature: float = 0.6,
+        top_p: float = 0.95,
+        top_k: int = 50,
+        max_new_tokens: int = 2048,
+        repetition_penalty: float = 1.1,
+    ) -> tuple:
+        """
+        Generate TTS audio via llama-server /completion + codec decoding.
+        Returns (wav_bytes, sample_rate).
+        """
+        if audio_type not in self._TTS_PROMPTS:
+            raise RuntimeError(f"GGUF TTS does not support '{audio_type}' codec.")
+
+        tpl, stop, need_ids = self._TTS_PROMPTS[audio_type]
+
+        payload: dict = {
+            "prompt": tpl.format(text = text),
+            "stream": False,
+            "n_predict": max_new_tokens,
+            "temperature": temperature,
+            "top_p": top_p,
+            "top_k": top_k if top_k >= 0 else 0,
+            "repeat_penalty": repetition_penalty,
+        }
+        if stop:
+            payload["stop"] = stop
+        if need_ids:
+            payload["n_probs"] = 1
+
+        with httpx.Client(timeout = httpx.Timeout(300, connect = 10)) as client:
+            resp = client.post(f"{self.base_url}/completion", json = payload)
+            if resp.status_code != 200:
+                raise RuntimeError(f"llama-server returned {resp.status_code}: {resp.text}")
+
+        data = resp.json()
+        token_ids = [p["id"] for p in data.get("completion_probabilities", []) if "id" in p] if need_ids else None
+
+        import torch
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        return LlamaCppBackend._codec_mgr.decode(audio_type, device, token_ids = token_ids, text = data.get("content", ""))
diff --git a/studio/backend/routes/inference.py b/studio/backend/routes/inference.py
index c7fa9ed720..ea497101a7 100644
--- a/studio/backend/routes/inference.py
+++ b/studio/backend/routes/inference.py
@@ -155,6 +155,16 @@ async def load_model(
 
             logger.info(f"Loaded GGUF model via llama-server: {config.identifier}")
 
+            # Detect TTS audio by probing the loaded model's vocabulary
+            from utils.models import is_audio_input_type
+            _gguf_audio = llama_backend.detect_audio_type()
+            _gguf_is_audio = _gguf_audio is not None and _gguf_audio not in ("audio_vlm",)
+            llama_backend._is_audio = _gguf_is_audio
+            llama_backend._audio_type = _gguf_audio
+            if _gguf_is_audio:
+                logger.info(f"GGUF model detected as audio: audio_type={_gguf_audio}")
+                await asyncio.to_thread(llama_backend.init_audio_codec, _gguf_audio)
+
             inference_config = load_inference_config(config.identifier)
 
             return LoadResponse(
@@ -164,6 +174,9 @@ async def load_model(
                 is_vision = config.is_vision,
                 is_lora = False,
                 is_gguf = True,
+                is_audio = _gguf_is_audio,
+                audio_type = _gguf_audio,
+                has_audio_input = is_audio_input_type(_gguf_audio),
                 inference = inference_config,
             )
 
@@ -461,6 +474,8 @@ async def get_status(
                 is_vision = llama_backend.is_vision,
                 is_gguf = True,
                 gguf_variant = llama_backend.hf_variant,
+                is_audio = getattr(llama_backend, "_is_audio", False),
+                audio_type = getattr(llama_backend, "_audio_type", None),
                 loading = [],
                 loaded = [llama_backend.model_identifier],
             )
@@ -509,78 +524,68 @@ async def generate_audio(
     """
     Generate audio (TTS) from the latest user message.
     Returns a JSON response with base64-encoded WAV audio.
-    Only works when an audio model is loaded.
+    Works with both GGUF (llama-server) and Unsloth/transformers backends.
     """
     import base64
 
-    backend = get_inference_backend()
-    if not backend.active_model_name:
-        raise HTTPException(status_code = 400, detail = "No model loaded.")
-
-    model_info = backend.models.get(backend.active_model_name, {})
-    if not model_info.get("is_audio"):
-        raise HTTPException(
-            status_code = 400, detail = "Active model is not an audio model."
-        )
-
     # Extract text from the last user message
     _, chat_messages, _ = _extract_content_parts(payload.messages)
     if not chat_messages:
         raise HTTPException(status_code = 400, detail = "No messages provided.")
-
     last_user_msg = next(
         (m for m in reversed(chat_messages) if m["role"] == "user"), None
     )
     if not last_user_msg:
         raise HTTPException(status_code = 400, detail = "No user message found.")
-
     text = last_user_msg["content"]
 
+    # Pick backend — both return (wav_bytes, sample_rate)
+    llama_backend = get_llama_cpp_backend()
+    if llama_backend.is_loaded and getattr(llama_backend, "_is_audio", False):
+        model_name = llama_backend.model_identifier
+        gen = lambda: llama_backend.generate_audio_response(
+            text = text, audio_type = llama_backend._audio_type,
+            temperature = payload.temperature, top_p = payload.top_p,
+            top_k = payload.top_k, max_new_tokens = payload.max_tokens or 2048,
+            repetition_penalty = payload.repetition_penalty,
+        )
+    else:
+        backend = get_inference_backend()
+        if not backend.active_model_name:
+            raise HTTPException(status_code = 400, detail = "No model loaded.")
+        model_info = backend.models.get(backend.active_model_name, {})
+        if not model_info.get("is_audio"):
+            raise HTTPException(status_code = 400, detail = "Active model is not an audio model.")
+        model_name = backend.active_model_name
+        gen = lambda: backend.generate_audio_response(
+            text = text, temperature = payload.temperature,
+            top_p = payload.top_p, top_k = payload.top_k,
+            min_p = payload.min_p, max_new_tokens = payload.max_tokens or 2048,
+            repetition_penalty = payload.repetition_penalty,
+            use_adapter = payload.use_adapter,
+        )
+
     try:
-        wav_bytes, sample_rate = await asyncio.get_event_loop().run_in_executor(
-            None,
-            lambda: backend.generate_audio_response(
-                text = text,
-                temperature = payload.temperature,
-                top_p = payload.top_p,
-                top_k = payload.top_k,
-                min_p = payload.min_p,
-                max_new_tokens = payload.max_tokens or 2048,
-                repetition_penalty = payload.repetition_penalty,
-                use_adapter = payload.use_adapter,
-            ),
-        )
-
-        audio_b64 = base64.b64encode(wav_bytes).decode("ascii")
-        completion_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
-
-        return JSONResponse(
-            content = {
-                "id": completion_id,
-                "object": "chat.completion.audio",
-                "model": backend.active_model_name,
-                "audio": {
-                    "data": audio_b64,
-                    "format": "wav",
-                    "sample_rate": sample_rate,
-                },
-                "choices": [
-                    {
-                        "index": 0,
-                        "message": {
-                            "role": "assistant",
-                            "content": f'[Generated audio from: "{text[:100]}"]',
-                        },
-                        "finish_reason": "stop",
-                    }
-                ],
-            }
-        )
-
+        wav_bytes, sample_rate = await asyncio.get_event_loop().run_in_executor(None, gen)
     except Exception as e:
         logger.error(f"Audio generation error: {e}", exc_info = True)
         raise HTTPException(status_code = 500, detail = str(e))
 
+    audio_b64 = base64.b64encode(wav_bytes).decode("ascii")
+    return JSONResponse(
+        content = {
+            "id": f"chatcmpl-{uuid.uuid4().hex[:12]}",
+            "object": "chat.completion.audio",
+            "model": model_name,
+            "audio": {"data": audio_b64, "format": "wav", "sample_rate": sample_rate},
+            "choices": [{
+                "index": 0,
+                "message": {"role": "assistant", "content": f'[Generated audio from: "{text[:100]}"]'},
+                "finish_reason": "stop",
+            }],
+        }
+    )
+
 
 # =====================================================================
 # OpenAI-Compatible Chat Completions  (/chat/completions)
@@ -702,6 +707,8 @@ async def openai_chat_completions(
     # ── Determine which backend is active ─────────────────────
     if using_gguf:
         model_name = llama_backend.model_identifier or payload.model
+        if getattr(llama_backend, "_is_audio", False):
+            return await generate_audio(payload, request)
     else:
         backend = get_inference_backend()
         if not backend.active_model_name:
diff --git a/studio/backend/routes/models.py b/studio/backend/routes/models.py
index 586b18718e..d914ceaa02 100644
--- a/studio/backend/routes/models.py
+++ b/studio/backend/routes/models.py
@@ -304,6 +304,18 @@ async def list_models(
             )
             loaded_models.append(model_info)
 
+        # Include active GGUF model (loaded via llama-server)
+        from routes.inference import get_llama_cpp_backend
+        llama_backend = get_llama_cpp_backend()
+        if llama_backend.is_loaded and llama_backend.model_identifier:
+            loaded_models.append(ModelDetails(
+                id = llama_backend.model_identifier,
+                name = llama_backend.model_identifier.split("/")[-1],
+                is_gguf = True, is_vision = llama_backend.is_vision,
+                is_audio = getattr(llama_backend, "_is_audio", False),
+                audio_type = getattr(llama_backend, "_audio_type", None),
+            ))
+
         # Combine default and loaded models
         all_models = []
         seen_ids = set()
diff --git a/studio/frontend/src/features/chat/api/chat-adapter.ts b/studio/frontend/src/features/chat/api/chat-adapter.ts
index 0935728e89..04cd0a5098 100644
--- a/studio/frontend/src/features/chat/api/chat-adapter.ts
+++ b/studio/frontend/src/features/chat/api/chat-adapter.ts
@@ -79,10 +79,17 @@ function toOpenAIMessage(message: RunMessage): {
     return null;
   }
 
-  return {
-    role: message.role,
-    content: collectTextParts(message).join("\n"),
-  };
+  let content = collectTextParts(message).join("\n");
+  // Strip inline audio base64 from prior assistant messages to avoid
+  // inflating token counts (e.g. audio-player responses with embedded WAV).
+  if (message.role === "assistant") {
+    content = content.replace(
+      /data:audio\/[a-z0-9.+-]+;base64,[A-Za-z0-9+/=]+/g,
+      "[audio]",
+    );
+  }
+
+  return { role: message.role, content };
 }
 
 function extractImageBase64(input: string): string | undefined {