---
title: MiniCPM-V 4.6
metatags:
description: "Deploy OpenBMB MiniCPM-V 4.6 (Qwen3.5-style hybrid GDN backbone + NaViT vision encoder) on NVIDIA GPUs with SGLang — multimodal text + image + video, slicing for high-resolution images."
---
## 1. Model Introduction
MiniCPM-V 4.6 is the next-generation multimodal model from [OpenBMB](https://huggingface.co/openbmb), the team behind the MiniCPM-V series. The model combines a **Qwen3.5-style hybrid LLM backbone** (Gated Delta Net + full attention) with a **NaViT-packed vision encoder** that handles arbitrary aspect ratios and high-resolution slicing natively, plus end-to-end video support.
OpenBMB ships two variants on HuggingFace:
- [`openbmb/MiniCPM-V-4.6`](https://huggingface.co/openbmb/MiniCPM-V-4.6) — base instruct model. Use this for general multimodal serving; thinking mode is still available per-request via `chat_template_kwargs.enable_thinking=true`.
- [`openbmb/MiniCPM-V-4.6-Thinking`](https://huggingface.co/openbmb/MiniCPM-V-4.6-Thinking) — thinking-tuned variant with stronger chain-of-thought behavior. Pair with the same `--reasoning-parser qwen3` flag.
**Key Features:**
- **Hybrid LLM backbone**: Qwen3.5-style mix of Gated Delta Net (linear-attention) layers and full-attention layers, providing long-context efficiency without giving up modeling power.
- **Native variable-resolution vision**: NaViT-packed vision encoder with mid-ViT merger and per-image window attention. Images of any aspect ratio are processed without forced letterboxing.
- **High-resolution slicing**: Source image plus a configurable grid of slice tiles (up to 9 tiles in the open test variant) lets the model reason over fine detail in 1280×720+ images.
- **Video**: Frame-by-frame multi-modal data items routed through the same vision encoder; any number of frames per request.
- **Reasoning Parser**: switchable thinking mode (Qwen3.5 lineage), exposed via `chat_template_kwargs.enable_thinking` per request and SGLang's `--reasoning-parser qwen3` on the server side.
- **Tool Calling**: Qwen3.5-style `…` XML format, surfaced as OpenAI-compatible `message.tool_calls` via SGLang's `--tool-call-parser qwen3_coder`. Composes with thinking mode and with image / video inputs.
**License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
## 2. SGLang Installation
Pull the nightly Docker image (rolling tag, tracks `main`):
```bash
# CUDA 13 (Hopper / Blackwell, default)
docker pull lmsysorg/sglang:dev
# CUDA 12 (Ampere or older drivers). Final build: the CUDA 12 lane is retired,
# so this tag no longer tracks main.
docker pull lmsysorg/sglang:dev-cu12
```
For the general SGLang installation guide (PyPI, source, Docker) see the [official SGLang installation guide](../../../docs/get-started/install).
## 3. Model Deployment
### 3.1 Basic Configuration
**Interactive Command Generator**: Use the configuration selector below to generate the appropriate deployment command. The `Variant` toggle switches between `openbmb/MiniCPM-V-4.6` (base) and `openbmb/MiniCPM-V-4.6-Thinking`. The `Reasoning Parser` and `Tool Call Parser` toggles add `--reasoning-parser qwen3` and `--tool-call-parser qwen3_coder` respectively; see §4.4 for usage details.
import { MiniCPMV46Deployment } from '/src/snippets/autoregressive/minicpm-v-4_6-deployment.jsx'
### 3.2 Configuration Tips
- **Mamba Radix Cache**: Qwen3.5's hybrid Gated Delta Networks architecture supports two mamba scheduling strategies via `--mamba-radix-cache-strategy`:
- **V1 (`no_buffer`)**: Default. No overlap scheduler, lower memory usage. Required for AMD MI GPUs.
- **V2 (`extra_buffer`)**: Enables overlap scheduling and branching point caching with `--mamba-radix-cache-strategy extra_buffer --page-size 64`. Requires FLA kernel backend (NVIDIA GPUs only). Trades higher mamba state memory for better throughput. Strictly superior in non-KV-cache-bound scenarios; in KV-cache-bound cases, weigh the overlap scheduling benefit against reduced max concurrency. `--page-size` must satisfy `FLA_CHUNK_SIZE % page_size == 0` or `page_size % FLA_CHUNK_SIZE == 0` (`FLA_CHUNK_SIZE` is currently 64).
- The `--mem-fraction-static` flag is recommended for optimal memory utilization, adjust it based on your hardware and workload.
- Context length defaults to 262,144 tokens. If you encounter OOM errors, consider reducing it, but maintain at least 128K to preserve thinking capabilities.
- To speed up weight loading for this large model, add `--model-loader-extra-config='{"enable_multithread_load": "true","num_threads": 64}'` to the launch command.
- **CUDA IPC Transport**: Add `SGLANG_USE_CUDA_IPC_TRANSPORT=1` as an environment variable to use CUDA IPC for transferring multimodal features, significantly improving TTFT (Time To First Token). Note: this consumes additional memory proportional to image size, so you may need to lower `--mem-fraction-static` or `--max-running-requests`.
- **Multimodal Attention Backend**: Use `--mm-attention-backend fa3` on H100/H200 for better vision performance, or `--mm-attention-backend fa4` on B200/B300.
- For processing large images or videos, you may need to lower `--mem-fraction-static` to leave room for image feature tensors.
- Multi-image and high-resolution images: the image processor produces one source patch plus per-slice tile patches; each is its own `MultimodalDataItem`. No special server-side flag needed.
- Video: decoded frame-by-frame through the same image-style slicer. No extra flag needed; pass `video_url` in the OpenAI chat completion request.
- **Chunked Prefill**: For high-concurrency vision benchmarking with many large/sliced images, pass `--chunked-prefill-size -1` to disable prefill chunking. The default chunked-prefill path can mis-split a request across an image boundary in `mm_utils.embed_mm_inputs` and crash the server; disabling chunking sidesteps this at the cost of higher TTFT under concurrency. For interactive serving leave the default on.
## 4. Model Invocation
Deploy the model on an H200:
```bash Command
sglang serve --model-path openbmb/MiniCPM-V-4.6 \
--trust-remote-code \
--dtype bfloat16 \
--mem-fraction-static 0.15 \
--mamba-radix-cache-strategy extra_buffer \
--page-size 64 \
--host 0.0.0.0 --port 30000
```
### 4.1 Basic Usage (Image)
```python Example
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:30000/v1",
api_key="EMPTY",
)
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4.6",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://www.ilankelman.org/stopsigns/australia.jpg",
},
},
{"type": "text", "text": "Describe this image in one sentence."},
],
}
],
max_tokens=200,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(response.choices[0].message.content)
```
**Output Example:**
```text Output
A black SUV drives past a Chinese-style gate with a red stop sign and traditional architecture, while storefronts and street signs line the sidewalk.
```
### 4.2 High-Resolution / Sliced Images
The image processor automatically picks a slice grid (up to 9 tiles) for high-resolution inputs. A 1280×720 source produces grid `[2, 3]`
+ 7 patches with `tgt_sizes=[(24, 44), 6×(28, 36)]`, byte-for-byte matching the HF reference implementation.
```python Example
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4.6",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg",
},
},
{"type": "text", "text": "Describe this image in one sentence."},
],
}
],
max_tokens=200,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(response.choices[0].message.content)
```
**Output Example:**
```text Output
The Statue of Liberty stands tall against a cloudy sky, holding a torch aloft and a document in her left hand, symbolizing freedom and enlightenment.
```
### 4.3 Video Input
```python Example
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4.6",
messages=[
{
"role": "user",
"content": [
{
"type": "video_url",
"video_url": {"url": ""},
},
{"type": "text", "text": "Describe what happens in this video in one sentence."},
],
}
],
max_tokens=200,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(response.choices[0].message.content)
```
**Output Example** (run against an 8-frame synthetic test mp4 of shifting colored squares):
```text Output
The video shows a grid of colored squares moving in a random pattern.
```
### 4.4 Advanced Usage
#### 4.4.1 Reasoning Parser
Pass `--reasoning-parser qwen3` to the server (toggle "Reasoning Parser" on in §3.1, default) so SGLang splits each response on the `` / `` boundaries: the pre-`` block goes to `reasoning_content`, the post-`` text to `content`. Per-request, the chat template's `enable_thinking` flag toggles whether the model actually emits reasoning.
- **Thinking mode** (default, `enable_thinking=true`): assistant prompt ends with `\n`; the model writes reasoning, closes with ``, then the answer. `reasoning_content` and `content` are both populated.
- **Instruct mode** (`enable_thinking=false`): the chat template injects an empty `` placeholder so the model emits no thinking tokens; `reasoning_content` ends up empty.
```python Example (thinking mode)
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4.6",
messages=[{"role": "user", "content": "Reply with the single word 'hi'. No explanation."}],
max_tokens=200,
)
msg = response.choices[0].message
print("reasoning_content:", msg.reasoning_content)
print("content :", msg.content)
```
```text Output
reasoning_content: Got it, let's see. The user wants a reply with "hi" and no explanation. So I need to just say "hi" as the response. ...
content : hi
```
```python Example (instruct mode)
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4.6",
messages=[{"role": "user", "content": "Reply with the single word 'hi'. No explanation."}],
max_tokens=200,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
msg = response.choices[0].message
print("reasoning_content:", msg.reasoning_content)
print("content :", msg.content)
```
```text Output
reasoning_content:
content : hi
```
#### 4.4.2 Tool Calling
Pass `--tool-call-parser qwen3_coder` to the server (toggle "Tool Call Parser" on in §3.1) so SGLang extracts `` blocks from the model output into the OpenAI-style `message.tool_calls` field (with `finish_reason="tool_calls"`). The model speaks the Qwen3.5 XML tool-call format (`v`); the `qwen3_coder` parser is the right one. Tool calls compose with both reasoning modes and with image / video inputs.
Do **not** use `--tool-call-parser qwen` for MiniCPM-V 4.6 — that parser expects the older Qwen2.5 JSON format `{"name":..., "arguments":...}`, but both public 4.6 variants emit the Qwen3.5-style XML format with nested `` and `` tags. With `qwen` the outer `` markers match but the inner JSON parse fails, so `tool_calls` returns empty and the raw markup is left in `content`.
```python Example
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a city.",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
},
},
]
response = client.chat.completions.create(
model="openbmb/MiniCPM-V-4.6",
messages=[{"role": "user", "content": "What is the weather in San Francisco? Use the tool."}],
tools=tools,
max_tokens=200,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
choice = response.choices[0]
print("finish_reason:", choice.finish_reason)
for tc in choice.message.tool_calls or []:
print(f" {tc.function.name}({tc.function.arguments})")
```
```text Output
finish_reason: tool_calls
get_weather({"location": "San Francisco", "unit": "celsius"})
```
To get the final natural-language answer, feed the tool's result back as a `tool` role message and call the API again with the same `tools` list — the model emits `finish_reason="stop"` with the answer in `content`.
## 5. Benchmark
**Common Test Environment (all benchmarks below):**
- Hardware: 1× NVIDIA H200 (141 GB), single GPU (no TP / DP)
- Docker Image: `lmsysorg/sglang:dev` (transformers 5.6.0, sgl-kernel 0.4.2.post1)
- Precision: BF16
**Common Server Launch Command:**
```bash Command
CUDA_VISIBLE_DEVICES=0 python -m sglang.launch_server \
--model-path openbmb/MiniCPM-V-4.6 \
--trust-remote-code \
--dtype bfloat16 \
--mem-fraction-static 0.5 \
--mamba-radix-cache-strategy extra_buffer \
--chunked-prefill-size -1 \
--host 0.0.0.0 --port 30000
```
(`--chunked-prefill-size -1` is required for the vision throughput run; see §3.2.)
### 5.1 Accuracy Benchmark
#### 5.1.1 MMMU Benchmark
- Benchmark Command
```bash Command
python3 benchmark/mmmu/bench_sglang.py --port 30000 --concurrency 48 --max-new-tokens 2048
```
- Test Result
```
{'Accounting': {'acc': 0.767, 'num': 30},
'Agriculture': {'acc': 0.533, 'num': 30},
'Architecture_and_Engineering': {'acc': 0.4, 'num': 30},
'Art': {'acc': 0.6, 'num': 30},
'Art_Theory': {'acc': 0.667, 'num': 30},
'Basic_Medical_Science': {'acc': 0.533, 'num': 30},
'Biology': {'acc': 0.333, 'num': 30},
'Chemistry': {'acc': 0.333, 'num': 30},
'Clinical_Medicine': {'acc': 0.467, 'num': 30},
'Computer_Science': {'acc': 0.333, 'num': 30},
'Design': {'acc': 0.533, 'num': 30},
'Diagnostics_and_Laboratory_Medicine': {'acc': 0.333, 'num': 30},
'Economics': {'acc': 0.633, 'num': 30},
'Electronics': {'acc': 0.5, 'num': 30},
'Energy_and_Power': {'acc': 0.633, 'num': 30},
'Finance': {'acc': 0.533, 'num': 30},
'Geography': {'acc': 0.367, 'num': 30},
'History': {'acc': 0.533, 'num': 30},
'Literature': {'acc': 0.7, 'num': 30},
'Manage': {'acc': 0.367, 'num': 30},
'Marketing': {'acc': 0.733, 'num': 30},
'Materials': {'acc': 0.367, 'num': 30},
'Math': {'acc': 0.567, 'num': 30},
'Mechanical_Engineering': {'acc': 0.333, 'num': 30},
'Music': {'acc': 0.267, 'num': 30},
'Overall': {'acc': 0.527, 'num': 900},
'Overall-Art and Design': {'acc': 0.517, 'num': 120},
'Overall-Business': {'acc': 0.607, 'num': 150},
'Overall-Health and Medicine': {'acc': 0.553, 'num': 150},
'Overall-Humanities and Social Science': {'acc': 0.617, 'num': 120},
'Overall-Science': {'acc': 0.473, 'num': 150},
'Overall-Tech and Engineering': {'acc': 0.443, 'num': 210},
'Pharmacy': {'acc': 0.667, 'num': 30},
'Physics': {'acc': 0.767, 'num': 30},
'Psychology': {'acc': 0.567, 'num': 30},
'Public_Health': {'acc': 0.767, 'num': 30},
'Sociology': {'acc': 0.667, 'num': 30}}
eval out saved to ./val_sglang.json
Overall accuracy: 0.527
```
### 5.2 Speed Benchmark
We use SGLang's built-in `bench_serving` tool with random text prompts (1000 input / 1000 output tokens) to characterize text-only serving performance.
#### 5.2.1 Latency Benchmark
```bash Command
python3 -m sglang.bench_serving \
--backend sglang \
--model openbmb/MiniCPM-V-4.6 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 10 \
--max-concurrency 1 \
--request-rate inf
```
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 7.47
Total input tokens: 6101
Total input text tokens: 6101
Total generated tokens: 4220
Total generated tokens (retokenized): 3554
Request throughput (req/s): 1.34
Input token throughput (tok/s): 816.44
Output token throughput (tok/s): 564.73
Peak output token throughput (tok/s): 690.00
Peak concurrent requests: 4
Total token throughput (tok/s): 1381.17
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 746.20
Median E2E Latency (ms): 590.05
P90 E2E Latency (ms): 1446.13
P99 E2E Latency (ms): 1709.38
---------------Time to First Token----------------
Mean TTFT (ms): 138.12
Median TTFT (ms): 103.70
P99 TTFT (ms): 330.79
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 1.44
Median TPOT (ms): 1.44
P99 TPOT (ms): 1.45
---------------Inter-Token Latency----------------
Mean ITL (ms): 1.44
Median ITL (ms): 1.45
P95 ITL (ms): 1.49
P99 ITL (ms): 1.57
Max ITL (ms): 5.79
==================================================
```
#### 5.2.2 Throughput Benchmark
```bash Command
python3 -m sglang.bench_serving \
--backend sglang \
--model openbmb/MiniCPM-V-4.6 \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 1000 \
--max-concurrency 100 \
--request-rate inf
```
```text Output
============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max request concurrency: 100
Successful requests: 1000
Benchmark duration (s): 47.07
Total input tokens: 502493
Total input text tokens: 502493
Total generated tokens: 500251
Total generated tokens (retokenized): 469844
Request throughput (req/s): 21.24
Input token throughput (tok/s): 10675.32
Output token throughput (tok/s): 10627.69
Peak output token throughput (tok/s): 25911.00
Peak concurrent requests: 130
Total token throughput (tok/s): 21303.01
Concurrency: 97.24
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 4576.94
Median E2E Latency (ms): 4331.97
P90 E2E Latency (ms): 8634.07
P99 E2E Latency (ms): 9636.44
---------------Time to First Token----------------
Mean TTFT (ms): 206.50
Median TTFT (ms): 184.72
P99 TTFT (ms): 624.23
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 8.73
Median TPOT (ms): 9.16
P99 TPOT (ms): 13.63
---------------Inter-Token Latency----------------
Mean ITL (ms): 8.75
Median ITL (ms): 0.05
P95 ITL (ms): 29.95
P99 ITL (ms): 108.91
Max ITL (ms): 448.40
==================================================
```
### 5.3 Vision Speed Benchmark
We use SGLang's built-in `bench_serving` tool with random images. Each request has 128 input text tokens, one 720p image, and 1024 output tokens.
#### 5.3.1 Latency Benchmark
```bash Command
python3 -m sglang.bench_serving \
--backend sglang-oai-chat \
--host 127.0.0.1 \
--port 30000 \
--model openbmb/MiniCPM-V-4.6 \
--dataset-name image \
--image-count 1 \
--image-resolution 720p \
--random-input-len 128 \
--random-output-len 1024 \
--num-prompts 10 \
--max-concurrency 1 \
--request-rate inf
```
```text Output
============ Serving Benchmark Result ============
Backend: sglang-oai-chat
Traffic request rate: inf
Max request concurrency: 1
Successful requests: 10
Benchmark duration (s): 10.26
Total input tokens: 767
Total input text tokens: 750
Total input vision tokens: 17
Total generated tokens: 4220
Total generated tokens (retokenized): 4220
Request throughput (req/s): 0.97
Input token throughput (tok/s): 74.77
Output token throughput (tok/s): 411.39
Peak output token throughput (tok/s): 654.00
Peak concurrent requests: 2
Total token throughput (tok/s): 486.16
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 1024.04
Median E2E Latency (ms): 897.99
P90 E2E Latency (ms): 1584.25
P99 E2E Latency (ms): 1781.78
---------------Time to First Token----------------
Mean TTFT (ms): 416.94
Median TTFT (ms): 403.18
P99 TTFT (ms): 477.49
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 1.44
Median TPOT (ms): 1.44
P99 TPOT (ms): 1.45
---------------Inter-Token Latency----------------
Mean ITL (ms): 1.44
Median ITL (ms): 1.44
P95 ITL (ms): 1.48
P99 ITL (ms): 1.56
Max ITL (ms): 2.89
==================================================
```
#### 5.3.2 Throughput Benchmark
```bash Command
python3 -m sglang.bench_serving \
--backend sglang-oai-chat \
--host 127.0.0.1 \
--port 30000 \
--model openbmb/MiniCPM-V-4.6 \
--dataset-name image \
--image-count 1 \
--image-resolution 720p \
--random-input-len 128 \
--random-output-len 1024 \
--num-prompts 1000 \
--max-concurrency 100 \
--request-rate inf
```
```text Output
============ Serving Benchmark Result ============
Backend: sglang-oai-chat
Traffic request rate: inf
Max request concurrency: 100
Successful requests: 1000
Benchmark duration (s): 360.01
Total input tokens: 79925
Total input text tokens: 78283
Total input vision tokens: 1642
Total generated tokens: 510855
Total generated tokens (retokenized): 430289
Request throughput (req/s): 2.78
Input token throughput (tok/s): 222.01
Output token throughput (tok/s): 1419.01
Peak output token throughput (tok/s): 19620.00
Peak concurrent requests: 105
Total token throughput (tok/s): 1641.02
Concurrency: 99.69
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 35888.57
Median E2E Latency (ms): 35321.48
P90 E2E Latency (ms): 41017.37
P99 E2E Latency (ms): 60343.22
---------------Time to First Token----------------
Mean TTFT (ms): 35096.32
Median TTFT (ms): 34301.37
P99 TTFT (ms): 59966.25
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 1.63
Median TPOT (ms): 1.45
P99 TPOT (ms): 10.15
---------------Inter-Token Latency----------------
Mean ITL (ms): 1.58
Median ITL (ms): 0.12
P95 ITL (ms): 0.23
P99 ITL (ms): 0.77
Max ITL (ms): 2086.12
==================================================
```