feat: update to Q5_K_P @ 128K ctx, non-blocking proxy sessions, and full multi-GPU benchmark suite

README.md

@@ -78,7 +78,9 @@ Pick **`qwen:fast`** for instant answers (0 thinking delay) or **`qwen:think`**
 
 ---
 
-## 📜 Documentation Links
+## 📜 Documentation & Benchmark Links
+* [Comprehensive Multi-GPU Benchmark Results (Q4/Q5/Q6/Gemma4 MoE)](benchmarks/BENCHMARK_RESULTS.md)
+* [Detailed Client Usage Guide (OpenWebUI, Ollama, OpenAI, Anthropic, Continue.dev)](docs/USAGE-CLIENTS.md)
 * [Detailed Step-by-Step Setup Guide](docs/SETUP_GUIDE.md)
 * [Proxmox LXC Passthrough & Permissions Guide](docs/PROXMOX_LXC_GUIDE.md)
 * [Hardware Architecture & CMP Learnings](docs/HARDWARE_LEARNINGS.md)

benchmarks/BENCHMARK_RESULTS.md

@@ -0,0 +1,58 @@
+# Multi-GPU Quantization Benchmark: Qwen3.8-27B vs Gemma4-26B-A4B (Turing SM75)
+
+Comprehensive performance benchmark, memory footprint comparison, context window scaling, and architecture analysis across 3x NVIDIA Turing GPUs (RTX 2060 12GB + 2x CMP 50HX 10GB).
+
+---
+
+## 1. Hardware & System Architecture
+
+- **Host Processor**: Intel Xeon E5-2697 v2 (12C/24T, Ivy Bridge @ 2.70 GHz base, AVX1 support)
+- **Active GPUs**: 3x NVIDIA Turing GPUs (**32,768 MiB physical VRAM total**)
+  - `CUDA0` (Bus `21:00.0`): **NVIDIA GeForce RTX 2060 12GB** (11,841 MiB usable) @ PCIe 3.0 x16
+  - `CUDA1` (Bus `24:00.0`): **NVIDIA CMP 50HX 10GB** (9,798 MiB usable) @ PCIe 3.0 x16
+  - `CUDA2` (Bus `27:00.0`): **NVIDIA CMP 50HX 10GB** (9,798 MiB usable) @ PCIe 3.0 x16
+- **Interconnect**: NVIDIA NCCL / Layer Split pipeline across 3 PCIe buses
+
+---
+
+## 2. Head-to-Head Benchmark Matrix: Dense vs MoE
+
+| Metric / Benchmark Stage | **Gemma4-26B-A4B-QAT @ 256K Ctx** *(Active Now)* | **Qwen3.8-27B (Q5_K_P @ 192K)** | **Qwen3.8-27B (Q4_K_P @ 256K)** |
+| :--- | :--- | :--- | :--- |
+| **Architecture Type** | **Sparse MoE (128 experts / 8 active)** | Dense (64 layers) | Dense (64 layers) |
+| **Active Params / Total Params** | **4.0B active / 26B total** | 27.5B active / 27.5B total | 27.5B active / 27.5B total |
+| **Model Disk Size** | **15.64 GB** (`Q4_K_M`) | 20.22 GB (`Q5_K_P`) | 17.92 GB (`Q4_K_P`) |
+| **KV Cache Precision** | **`q5_0` (5.5-bit)** | `q5_0` (5.5-bit) | `q4_0` (4.5-bit) |
+| **Max Context Window** | **256K** (`262,144 tokens`) | 192K (`196,608 tokens`) | 256K (`262,144 tokens`) |
+| **Multimodal Support** | **Text + Vision + Video (`mmproj-BF16`)** | Text + Vision (`mmproj-f16`) | Text + Vision (`mmproj-f16`) |
+| **Split Mode** | **Layer Split (`--split-mode layer`)** | NCCL Tensor Split | NCCL Tensor Split |
+| **Active Memory Footprint** | **9.09 GB / 9.25 GB / 7.67 GB** | 11.60 GB / 9.07 GB / 9.05 GB | 11.75 GB / 8.89 GB / 8.87 GB |
+| **Total VRAM Allocated** | **26.00 GB (83%)** | 29.72 GB (95%) | 29.51 GB (94%) |
+| **Free Headroom / GPU** | **+2.75 GB / +0.55 GB / +2.13 GB** | +240 MB / +720 MB / +740 MB | +90 MB / +905 MB / +923 MB |
+| **1. Short Prompt (100 tok)** | | | |
+| • Prefill Speed | **95.87 tok/s** | 53.66 tok/s | 52.46 tok/s |
+| • Decode Speed | **46.00 tok/s** | 37.14 tok/s | 38.12 tok/s |
+| • Total Wall Time | **2.44 s** | 3.76 s | 3.47 s |
+| **2. Medium Context (~5k tok)** | | | |
+| • Prefill Speed | **2,279.22 tok/s** *(5.8x faster)* | 388.85 tok/s | 391.23 tok/s |
+| • Decode Speed | **45.42 tok/s** | 33.81 tok/s | 41.42 tok/s |
+| • Total Wall Time | **4.40 s** *(3.8x faster)* | 16.79 s | 16.33 s |
+| **3. Long Context (~6k tok)** | | | |
+| • Prefill Speed | **2,374.57 tok/s** *(6.0x faster)* | 396.52 tok/s | 398.61 tok/s |
+| • Decode Speed | **43.67 tok/s** | 35.99 tok/s | 41.00 tok/s |
+| • Total Wall Time | **4.97 s** *(3.9x faster)* | 19.60 s | 19.47 s |
+| **4. Sustained Decode (256 tok)** | | | |
+| • Decode Speed | **47.50 tok/s** *(+58% faster)* | 30.10 tok/s | 31.97 tok/s |
+| • Total Wall Time | **5.82 s** | 10.71 s | 10.17 s |
+
+---
+
+## 3. Context Scaling Matrix on Gemma4-26B-A4B
+
+| Context Window | KV Cache Quant Type | Total VRAM Across Rig | RTX 2060 12GB | CMP 50HX #1 10GB | CMP 50HX #2 10GB | Status |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| **64K** (`65,536`) | `q5_0` (5.5-bit) | 19.5 GB | 6.8 GB | 7.1 GB | 5.6 GB | Rock Solid |
+| **128K** (`131,072`) | `q5_0` (5.5-bit) | 22.0 GB | 7.7 GB | 7.9 GB | 6.5 GB | Rock Solid |
+| **192K** (`196,608`) | `q5_0` (5.5-bit) | 24.0 GB | 8.4 GB | 8.6 GB | 7.0 GB | Stable |
+| **256K** (`262,144`) | `q5_0` (5.5-bit) | **26.0 GB** | **9.09 GB** | **9.25 GB** | **7.67 GB** | **Full Native Ceiling** (+550MB margin) |
+| **192K** (`196,608`) | `q8_0` (8.5-bit) | 27.8 GB | 9.8 GB | 9.6 GB | 8.4 GB | Max Precision Ceiling |

docs/USAGE-CLIENTS.md

@@ -0,0 +1,303 @@
+# Connecting clients to the llama.cpp server
+
+This guide covers wiring the Qwen3.8-27B llama.cpp server (OpenAI-compatible
+API on `:8080`) into Open WebUI, opencode, Claude Code, and other tools.
+
+Server: `http://<HOST>:8080`
+Base URL: `http://<HOST>:8080/v1`  ← use this for OpenAI-style clients
+
+> Note: llama.cpp exposes an **OpenAI-compatible** API. It is *not* an
+> Ollama-protocol server (no `GET /api/tags`, `/api/chat`, etc.). Most tools
+> accept OpenAI-style endpoints, so that's fine. If you need a true Ollama
+> clone API, see §6.
+
+---
+
+## 0. Quick reference — endpoints
+
+| Endpoint | Purpose |
+|---|---|
+| `GET /v1/models` | list models |
+| `POST /v1/chat/completions` | chat (reasoning model: returns `reasoning_content`) |
+| `POST /v1/completions` | raw completions |
+| `POST /v1/embeddings` | embeddings |
+| `GET /health` | liveness |
+| `GET /props` | server params (context size, etc.) |
+
+Auth is optional (no `--api-key` set). If you set one later, pass it as
+`Authorization: Bearer <key>`.
+
+---
+
+## 1. Open WebUI
+
+Open WebUI (openwebui.com) connects fine via its **OpenAI API** connection.
+
+### 1.1 Docker (recommended)
+```bash
+docker run -d -p 3000:8080 \
+  -v open-webui:/app/backend/data \
+  --name open-webui \
+  --restart always \
+  ghcr.io/open-webui/open-webui:main
+```
+
+### 1.2 Point it at the llama.cpp server
+1. Open `http://<HOST>:3000` and create an admin account.
+2. **Admin panel → Settings → Connections → OpenAI API.**
+   - **API Base URL:** `http://<HOST>:8080/v1`
+   - **API Key:** anything non-empty, e.g. `local`
+   - Enable if you want: "Enable Reasoning Content" (shows `thinking` blocks).
+3. Click refresh/save. The model `Qwen3.8-27B-Uncensored-...` should appear in
+   the model picker.
+
+Notes:
+- Open WebUI's **Ollama** connection type will *not* see this server (different
+  protocol). Use the **OpenAI API** connection type.
+- Optional: set `OPENAI_API_BASE_URL` / `OPENAI_API_KEY` env vars instead of the
+  UI form.
+
+---
+
+## 2. opencode
+
+opencode supports arbitrary OpenAI-compatible providers via the
+`@ai-sdk/openai-compatible` driver.
+
+### 2.1 `opencode.json` in your project
+```json
+{
+  "$schema": "https://opencode.ai/config.json",
+  "provider": {
+    "qwenlocal": {
+      "npm": "@ai-sdk/openai-compatible",
+      "name": "Qwen3.8-27B (local)",
+      "options": {
+        "baseURL": "http://<HOST>:8080/v1",
+        "apiKey": "local"
+      },
+      "models": {
+        "Qwen3.8-27B-Uncensored": {
+          "name": "Qwen3.8-27B Uncensored"
+        }
+      }
+    }
+  }
+}
+```
+Then start with:
+```bash
+opencode
+# model picker -> qwenlocal/Qwen3.8-27B-Uncensored
+```
+or force it per run:
+```bash
+opencode --model qwenlocal/Qwen3.8-27B-Uncensored
+```
+
+### 2.2 Global config (optional)
+Put the same `provider` block in `~/.config/opencode/opencode.json` to make it
+available in every project.
+
+Note: the model is a **reasoning** model — opencode will show the
+`reasoning_content` stream as thinking output.
+
+---
+
+## 3. Claude Code
+
+Claude Code speaks the Anthropic Messages protocol, so it needs a small
+translation layer to talk to llama.cpp's OpenAI API.
+
+### 3.1 Use claude-code-router (CCR)
+```bash
+npm install -g @musistudio/claude-code-router
+ccr --set-base-url http://<HOST>:8080/v1
+ccr --set-provider openai
+ccr
+```
+Point `ANTHROPIC_BASE_URL` at the router and run Claude Code as usual.
+
+### 3.2 Alternative: a generic OpenAI→Anthropic proxy
+Any tool that translates `/v1/chat/completions` (OpenAI) to the Anthropic
+Messages shape, e.g. LiteLLM, works:
+```bash
+pip install litellm[proxy]
+litellm --model openai/qwen3.8-27b --api_base http://<HOST>:8080/v1 --port 4000
+# then: export ANTHROPIC_BASE_URL=http://localhost:4000
+```
+
+Caveat: the uncensored Q4_K_P model has no tools/functions baked in beyond
+basic chat — agentic tool-calling may be unreliable.
+
+---
+
+## 4. Other OpenAI-compatible clients
+
+### 4.1 curl
+```bash
+curl http://<HOST>:8080/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "qwen",
+    "messages": [{"role":"user","content":"Hello"}],
+    "max_tokens": 200
+  }'
+```
+
+### 4.2 Python (openai SDK)
+```bash
+pip install openai
+```
+```python
+from openai import OpenAI
+client = OpenAI(base_url="http://<HOST>:8080/v1", api_key="local")
+
+resp = client.chat.completions.create(
+    model="qwen",
+    messages=[{"role": "user", "content": "Hello"}],
+    max_tokens=200,
+)
+print(resp.choices[0].message.content)
+# reasoning available as: resp.choices[0].message.reasoning_content
+```
+
+### 4.3 Node.js
+```bash
+npm i openai
+```
+```js
+import OpenAI from "openai";
+const client = new OpenAI({ baseURL: "http://<HOST>:8080/v1", apiKey: "local" });
+const r = await client.chat.completions.create({
+  model: "qwen",
+  messages: [{ role: "user", content: "Hello" }],
+});
+console.log(r.choices[0].message.content);
+```
+
+### 4.4 Local web UIs that accept an OpenAI endpoint
+- **AnythingLLM** — Settings → LLM → "OpenAI" → custom base URL.
+- **LM Studio / Jan** — treat the llama.cpp server as a remote OpenAI endpoint.
+- **SillyTavern** — Chat Completion → Custom OpenAI → set base URL.
+- **Continue.dev (VS Code)** — `models.yaml` with an openai provider + base URL.
+
+---
+
+## 5. Using the reasoning model properly
+
+Qwen3.8-27B emits `thinking` internally before its `response`.
+
+- OpenAI-compatible clients that surface `reasoning_content` (Open WebUI,
+  opencode) will show it automatically.
+- To disable/trim reasoning (lower latency, shorter answers), the chat template
+  supports `reasoning_effort`: `"low"` | `"medium"` | `"xhigh"` (default).
+  Most clients pass extra `chat_template_kwargs`; alternatively send it via the
+  `chat_template_kwargs` field or set `enable_thinking: false`:
+  ```json
+  "chat_template_kwargs": {"reasoning_effort": "low"}
+  ```
+- If a client shows raw `<thinking>` / `<response>` tags, strip them, e.g. in
+  Python: `re.sub(r"</?thinking>", "", text)`.
+
+---
+
+## 6. "I want an Ollama clone API"
+
+**Done — a built-in proxy provides one.** `/opt/llama-server/ollama-proxy.py`
+fakes both the **Ollama API** (`/api/chat`, `/api/generate`, `/api/tags`,
+`/api/ps`, `/api/show`, `/api/embed`, …) **and the Anthropic Messages API**
+(`/v1/messages`, `/v1/messages/count_tokens`) on port **11434**, translating
+every request to the llama.cpp OpenAI backend on `:8080`. It runs as a systemd
+service (`ollama-proxy.service`), pure Python stdlib, no third-party deps.
+
+### 6.1 What the proxy exposes
+
+| Endpoint | Purpose | Status |
+|---|---|---|
+| `GET /api/version` | version string | ✅ |
+| `GET /api/tags` | list models (`Qwen3.8-Uncensored`) | ✅ |
+| `GET /api/ps` | running models | ✅ |
+| `GET /api/status` | cloud status (launcher) | ✅ |
+| `GET /api/experimental/model-recommendations` | launcher hint | ✅ |
+| `POST /api/show` | model details | ✅ |
+| `POST /api/chat` | chat (stream + non-stream) | ✅ |
+| `POST /api/generate` | single-prompt completion | ✅ |
+| `POST /api/embed` / `/api/embeddings` | embeddings | ✅ |
+| `POST /v1/messages` | **Anthropic Messages API** (Claude Code) | ✅ |
+| `POST /v1/messages/count_tokens` | rough token estimate | ✅ |
+| `GET /v1/models` | OpenAI-style model list | ✅ |
+| `POST /v1/chat/completions` | OpenAI passthrough | ✅ |
+
+Reasoning output from Qwen is exposed as `reasoning_content` on the Ollama
+shape and `thinking`/`text` blocks on the Anthropic shape.
+
+**Context length:** all four discovery endpoints (`/api/tags`, `/api/ps`,
+`/api/show`, `/v1/models`) report `context_length: 262144` so tools don't
+down-scale to a default (e.g. 128k).
+
+### 6.2 Usage
+
+```bash
+# any Ollama-native client, point it at this box
+OLLAMA_HOST=http://192.168.1.198:11434 ollama run Qwen3.8-Uncensored
+
+curl http://192.168.1.198:11434/api/chat -d '{
+  "model": "Qwen3.8-Uncensored",
+  "messages": [{"role": "user", "content": "hi"}]
+}'
+```
+
+### 6.3 Claude Code via `ollama launch claude`
+
+`ollama launch claude` makes Claude Code talk to the **Anthropic `/v1/messages`**
+endpoint at `OLLAMA_HOST`. The proxy implements it, so:
+
+```bash
+OLLAMA_HOST="http://192.168.1.198:11434" \
+CLAUDE_CODE_MAX_CONTEXT_TOKENS=65536 \
+ollama launch claude --model Qwen3.8-Uncensored
+```
+
+Important:
+- **Use `192.168.1.198`**, this box's LAN IP — not a different address. Earlier
+  guidance referenced `.165`, which is not this host.
+- The proxy presents `ANTHROPIC_BASE_URL` = `OLLAMA_HOST`, so Claude Code talks
+  directly to `:11434/v1/messages`.
+- If you don't use `ollama launch`, the equivalent manual setup is:
+  ```bash
+  export ANTHROPIC_AUTH_TOKEN=ollama
+  export ANTHROPIC_API_KEY=
+  export ANTHROPIC_BASE_URL=http://192.168.1.198:11434
+  claude --model Qwen3.8-Uncensored
+  ```
+- **Tool calling: supported.** Anthropic `tools`, `tool_use`, and `tool_result`
+  blocks translate to/from llama.cpp OpenAI function calls (verified: the model
+  returns proper `tool_use` blocks, and multi-turn tool results are answered
+  correctly). Streaming emits `input_json_delta` events for tool_use blocks.
+
+### 6.4 Service management
+
+```bash
+systemctl status llama-server    # backend (llama.cpp, :8080)
+systemctl status ollama-proxy    # API faker (:11434)
+journalctl -u ollama-proxy -f    # proxy logs
+```
+
+Both are enabled at boot. The proxy needs the backend up; the unit has
+`After=llama-server.service`.
+
+---
+
+## 7. Troubleshooting
+
+| Symptom | Fix |
+|---|---|
+| Connection refused | Server stopped (`systemctl start llama-server`); wrong host/port |
+| `401` | A `--api-key` was set; add `Authorization: Bearer <key>` |
+| Model not in client list | Client cached models; hit refresh, or `curl /v1/models` to confirm |
+| Slow first token | Reasoning model thinking; set `reasoning_effort: "low"` |
+| OOM / VRAM errors | Reduce `-c`, or drop `--cache-type-k/v q4_0` trade-offs (see README §11) |
+| Client needs Ollama API | Use the built-in proxy on `:11434` (§6) |
+| `ollama launch <tool>` fails "something went wrong" | Check `journalctl -u ollama-proxy -f`; the launcher does `HEAD /` (heartbeat) + `GET /api/tags` first — both must return 200 |
+| Tool calls return empty / no tool_use | Confirm the model supports tools via `POST /api/chat` with a `tools` array; the Q4_K_P GGUF does |

scripts/ollama-proxy.py

@@ -92,39 +92,39 @@ def extract_session_id(headers, payload=None):
 
     return "default"
 
+import traceback
+
 def ensure_session(session_id):
-    """Ensure slot 0 contains the KV cache for session_id, saving/restoring as needed."""
+    """Ensure slot 0 contains the KV cache for explicit named sessions, safely and non-blockingly."""
-    global CURRENT_SESSION
+    try:
-    if not session_id:
+        global CURRENT_SESSION
-        session_id = "default"
+        if not session_id:
-
-    clean_id = re.sub(r'[^a-zA-Z0-9_\-\.]', '_', str(session_id))[:64]
-
-    with SESSION_LOCK:
-        if clean_id == CURRENT_SESSION:
             return
 
-        # 1. Save old session if not default
+        clean_id = re.sub(r'[^a-zA-Z0-9_\-\.]', '_', str(session_id))[:64]
-        if CURRENT_SESSION and CURRENT_SESSION != "default":
-            save_file = f"{CURRENT_SESSION}.bin"
-            print(f"[ollama-proxy] Saving slot 0 for session '{CURRENT_SESSION}' -> {save_file}", flush=True)
-            _slot_action("save", save_file)
 
-        # 2. Restore new session or erase
+        # Only manage disk snapshots for explicit user session IDs (ignore transient sys- hashes)
-        target_file = f"{clean_id}.bin"
+        if clean_id == "default" or clean_id.startswith("sys-"):
-        target_path = os.path.join(SLOT_SAVE_PATH, target_file)
+            return
 
-        if os.path.exists(target_path):
+        with SESSION_LOCK:
-            print(f"[ollama-proxy] Restoring slot 0 for session '{clean_id}' <- {target_file}", flush=True)
+            if clean_id == CURRENT_SESSION:
-            res = _slot_action("restore", target_file)
+                return
-            if not res:
-                print(f"[ollama-proxy] Restore failed for '{clean_id}', falling back to erase", flush=True)
-                _slot_action("erase")
-        else:
-            print(f"[ollama-proxy] Starting fresh slot for session '{clean_id}'", flush=True)
-            _slot_action("erase")
 
-        CURRENT_SESSION = clean_id
+            if CURRENT_SESSION and CURRENT_SESSION != "default" and not CURRENT_SESSION.startswith("sys-"):
+                save_file = f"{CURRENT_SESSION}.bin"
+                print(f"[ollama-proxy] Saving slot 0 for session '{CURRENT_SESSION}' -> {save_file}", flush=True)
+                _slot_action("save", save_file)
+
+            target_file = f"{clean_id}.bin"
+            target_path = os.path.join(SLOT_SAVE_PATH, target_file)
+            if os.path.exists(target_path):
+                print(f"[ollama-proxy] Restoring slot 0 for session '{clean_id}' <- {target_file}", flush=True)
+                _slot_action("restore", target_file)
+
+            CURRENT_SESSION = clean_id
+    except Exception as e:
+        print(f"[ollama-proxy] Warning: ensure_session({session_id}) error (non-fatal): {e}", flush=True)
 
 MODEL_TAGS  = [
     "Qwen3.8-Uncensored:latest",
@@ -1444,6 +1444,8 @@ class Handler(http.server.BaseHTTPRequestHandler):
                 detail = ""
             self._send(e.code, {"error": detail or str(e)})
         except Exception as e:
+            print(f"[ollama-proxy] Exception in POST {path}: {e}", flush=True)
+            traceback.print_exc()
             self._send(500, {"error": f"{type(e).__name__}: {e}"})
 
     def do_DELETE(self):

scripts/start-server.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# llama-server launcher for Qwen3.8-27B on 3x Turing GPUs (RTX 2060 12GB + 2x CMP 50HX 10GB)
+# llama-server launcher for Qwen3.8-27B on Turing GPUs (RTX 2060 12GB + CMP 50HX 10GB)
 # Built with NVIDIA NCCL for hardware-accelerated multi-GPU tensor parallelism.
 # Accelerated with HauhauCS FastMTP 32K draft sidecar and official thinking parameters.
 export LD_LIBRARY_PATH=/opt/llama.cpp-xrip/build-nccl/bin:/opt/minicpm-venv/lib/python3.13/site-packages/nvidia/nccl/lib:/lib/x86_64-linux-gnu
@@ -12,16 +12,20 @@ export NCCL_P2P_DISABLE=0
 export NCCL_ALGO=RING
 export NCCL_PROTO=SIMPLE
 
+# Dynamically target the 12GB RTX 2060 for mmproj and FastMTP draft model
+RTX_DEV=$(/opt/llama.cpp-xrip/build-nccl/bin/llama-cli --list-devices | grep -i "RTX 2060" | awk '{print $1}' | tr -d ':')
+RTX_DEV=${RTX_DEV:-CUDA0}
+
 exec /opt/llama.cpp-xrip/build-nccl/bin/llama-server \
-  -m /opt/models/gguf/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
+  -m /opt/models/gguf/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf \
   --spec-draft-model /opt/models/gguf/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-FastMTP-32K.gguf \
-  --spec-draft-device CUDA2 \
+  --spec-draft-device "$RTX_DEV" \
   --spec-draft-ngl all \
   --spec-type draft-mtp \
   --spec-draft-n-max 3 \
   --spec-draft-p-min 0 \
   --mmproj /opt/models/gguf/mmproj-Qwen3.8-27B-Uncensored-f16.gguf \
-  --mmproj-device CUDA2 \
+  --mmproj-device "$RTX_DEV" \
   --image-min-tokens 1024 \
   --temp 1.0 \
   --top-k 20 \
@@ -35,17 +39,17 @@ exec /opt/llama.cpp-xrip/build-nccl/bin/llama-server \
   --reasoning-format deepseek \
   --numa split \
   -ngl 99 \
-  -c 204800 \
+  -c 131072 \
   --parallel 1 \
   --slot-save-path /var/cache/llama-slots \
   --cache-ram 16384 \
   --split-mode tensor \
   --flash-attn on \
-  --batch-size 1024 \
+  --batch-size 2048 \
-  --ubatch-size 512 \
+  --ubatch-size 1024 \
   --jinja \
   --threads 12 \
-  --cache-type-k q4_0 \
+  --cache-type-k q5_0 \
-  --cache-type-v q4_0 \
+  --cache-type-v q5_0 \
   --host 0.0.0.0 \
   --port 8080