chore: import upstream snapshot with attribution

2026-07-13 12:38:16 +08:00
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+# GLM-4.6V / GLM-4.5V Usage
+
+## Launch commands for SGLang
+
+Below are suggested launch commands tailored for different hardware / precision modes
+
+### FP8 (quantised) mode
+
+For high memory-efficiency and latency optimized deployments (e.g., on H100, H200) where FP8 checkpoint is supported:
+
+```bash
+python3 -m sglang.launch_server \
+  --model-path zai-org/GLM-4.6V-FP8 \
+  --tp 2 \
+  --ep 2 \
+  --host 0.0.0.0 \
+  --port 30000 \
+  --keep-mm-feature-on-device
+```
+
+### Non-FP8 (BF16 / full precision) mode
+For deployments on A100/H100 where BF16 is used (or FP8 snapshot not used):
+```bash
+python3 -m sglang.launch_server \
+  --model-path zai-org/GLM-4.6V \
+  --tp 4 \
+  --ep 4 \
+  --host 0.0.0.0 \
+  --port 30000
+```
+
+## Hardware-specific notes / recommendations
+
+- On H100 with FP8: Use the FP8 checkpoint for best memory efficiency.
+- On A100 / H100 with BF16 (non-FP8): It’s recommended to use `--mm-max-concurrent-calls` to control parallel throughput and GPU memory usage during image/video inference.
+- On H200 & B200: The model can be run “out of the box”, supporting full context length plus concurrent image + video processing.
+
+## Sending Image/Video Requests
+
+### Image input:
+
+```python
+import requests
+
+url = f"http://localhost:30000/v1/chat/completions"
+
+data = {
+    "model": "zai-org/GLM-4.6V",
+    "messages": [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What’s in this image?"},
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": "https://github.com/sgl-project/sglang/blob/main/examples/assets/example_image.png?raw=true"
+                    },
+                },
+            ],
+        }
+    ],
+    "max_tokens": 300,
+}
+
+response = requests.post(url, json=data)
+print(response.text)
+```
+
+### Video Input:
+
+```python
+import requests
+
+url = f"http://localhost:30000/v1/chat/completions"
+
+data = {
+    "model": "zai-org/GLM-4.6V",
+    "messages": [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What’s happening in this video?"},
+                {
+                    "type": "video_url",
+                    "video_url": {
+                        "url": "https://github.com/sgl-project/sgl-test-files/raw/refs/heads/main/videos/jobs_presenting_ipod.mp4"
+                    },
+                },
+            ],
+        }
+    ],
+    "max_tokens": 300,
+}
+
+response = requests.post(url, json=data)
+print(response.text)
+```
+
+## Important Server Parameters and Flags
+
+When launching the model server for **multimodal support**, you can use the following command-line arguments to fine-tune performance and behavior:
+
+- `--mm-attention-backend`: Specify multimodal attention backend. Eg. `fa3`(Flash Attention 3)
+- `--mm-max-concurrent-calls <value>`: Specifies the **maximum number of concurrent asynchronous multimodal data processing calls** allowed on the server. Use this to control parallel throughput and GPU memory usage during image/video inference.
+- `--mm-per-request-timeout <seconds>`: Defines the **timeout duration (in seconds)** for each multimodal request. If a request exceeds this time limit (e.g., for very large video inputs), it will be automatically terminated.
+- `--keep-mm-feature-on-device`: Instructs the server to **retain multimodal feature tensors on the GPU** after processing. This avoids device-to-host (D2H) memory copies and improves performance for repeated or high-frequency inference workloads.
+- `--mm-enable-dp-encoder`: Placing the ViT in data parallel while keeping the LLM in tensor parallel consistently lowers TTFT and boosts end-to-end throughput.
+- `SGLANG_USE_CUDA_IPC_TRANSPORT=1`: Shared memory pool based CUDA IPC for multi-modal data transport. For significantly improving e2e latency.
+
+### Example usage with the above optimizations:
+```bash
+SGLANG_USE_CUDA_IPC_TRANSPORT=1 \
+SGLANG_VLM_CACHE_SIZE_MB=0 \
+python -m sglang.launch_server \
+  --model-path zai-org/GLM-4.6V \
+  --host 0.0.0.0 \
+  --port 30000 \
+  --trust-remote-code \
+  --tp-size 8 \
+  --enable-cache-report \
+  --log-level info \
+  --max-running-requests 64 \
+  --mem-fraction-static 0.65 \
+  --chunked-prefill-size 8192 \
+  --attention-backend fa3 \
+  --mm-attention-backend fa3 \
+  --mm-enable-dp-encoder \
+  --enable-metrics
+```
+
+### Thinking Budget for GLM-4.5V / GLM-4.6V
+
+In SGLang, we can implement thinking budget with `CustomLogitProcessor`.
+
+Launch a server with the `--enable-custom-logit-processor` flag. Then, use `Glm4MoeThinkingBudgetLogitProcessor` in the request, similar to the `GLM-4.6` example in [glm45.md](./glm45.md).