---
title: GLM-4.7
metatags:
    description: "Deploy GLM-4.7 with SGLang on NVIDIA Blackwell (B200, GB200) and AMD GPUs - state-of-the-art reasoning, robust tool calling, and NVFP4 weights for Blackwell."
---

## 1. Model Introduction

[GLM-4.7](https://huggingface.co/zai-org/GLM-4.7) is a powerful language model developed by Zhipu AI, featuring advanced capabilities in reasoning, function calling, and agent workflows.

GLM-4.7 brings improvements across all major domains:

- **Extended Context Window**: Expanded context window supporting even longer documents and complex multi-turn conversations
- **Enhanced Reasoning**: Improved reasoning capabilities with better chain-of-thought processing
- **Superior Coding**: Significantly improved code generation and understanding, with better real-world application performance
- **Advanced Tool Use**: More robust tool calling and agent capabilities for complex workflows
- **Optimized Performance**: Better throughput and latency characteristics across all hardware platforms

For more details, please refer to the [official GLM-4.7 documentation](https://docs.z.ai/guides/llm/glm-4.7).

**Key Features:**

- **State-of-the-Art Reasoning**: Enhanced reasoning capabilities for the most complex problem-solving tasks
- **Multiple Quantizations**: BF16, FP8, and NVFP4 variants for different performance/memory trade-offs
- **Hardware Optimization**: Tuned for NVIDIA Blackwell (B200, GB200) and AMD MI300X/MI325X/MI355X GPUs
- **High Performance**: Optimized for both throughput and latency scenarios

**Available Models:**

- **BF16 (Full precision)**: [zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7)
- **FP8 (8-bit quantized)**: [zai-org/GLM-4.7-FP8](https://huggingface.co/zai-org/GLM-4.7-FP8)
- **NVFP4 (4-bit, NVIDIA Blackwell)**: [nvidia/GLM-4.7-NVFP4](https://huggingface.co/nvidia/GLM-4.7-NVFP4)

**License:**

Please refer to the [official GLM-4.7 model card](https://huggingface.co/zai-org/GLM-4.7) for license details.

## 2. SGLang Installation

SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.

Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.

**Docker Images by Hardware Platform:**

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Hardware Platform</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Docker Image</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA H100 / H200 / B200</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:v0.5.12`</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA GB200 / B300 / GB300 (aarch64)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:v0.5.12-cu130`</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>AMD MI300X / MI325X</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:v0.5.12-rocm720-mi30x`</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>AMD MI355X</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:v0.5.12-rocm720-mi35x`</td>
    </tr>
  </tbody>
</table>


## 3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms and use cases.

### 3.1 Basic Configuration

**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, quantization method, deployment strategy, and thinking capabilities.

import { GLM47Deployment } from "/src/snippets/autoregressive/glm-47-deployment.jsx";

<GLM47Deployment />

### 3.2 Configuration Tips

Pick a weight format by hardware: **NVFP4** on NVIDIA Blackwell (B200, GB200), **FP8** on H100/H200/AMD, **BF16** as the full-precision fallback. The recommended tensor-parallel size per platform:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Hardware</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>NVFP4</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>FP8</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>BF16</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>B200 (8×, single node)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=2 / 4 / 8</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=4 / 8</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=8</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>GB200 (NVL72, 4× per tray)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=2 / 4</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=4</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>H200 (8×)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=8</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=8</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>AMD MI300X / MI325X / MI355X</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>—</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>tp=2 / 4 / 8</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>tp=4 / 8</td>
    </tr>
  </tbody>
</table>

- **EAGLE Speculative Decoding:** Supported for GLM-4.7. Add `--speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4`. The spec-v2 overlap scheduler is enabled by default; pass `--disable-overlap-schedule` to disable. Enable via the interactive command generator above.
- **Thinking Budget:** Use `--enable-custom-logit-processor` flag and pass `Glm4MoeThinkingBudgetLogitProcessor` in requests to cap the model's thinking token count (see section 4.2.3).

For general GLM-4.x family launch guidance (AMD ROCm notes and more), see [Launch GLM-4.5 / GLM-4.6 / GLM-4.7 with SGLang](/cookbook/autoregressive/GLM/GLM-4.5). Per-hardware bench commands and flags are inline in §5.1 below.

## 4. Model Invocation

### 4.1 Basic Usage

For basic API usage and request examples, please refer to:

- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)

### 4.2 Advanced Usage

#### 4.2.1 Reasoning Parser

GLM-4.7 supports Thinking mode by default. Enable the reasoning parser during deployment to separate the thinking and the content sections:

```shell Command
python -m sglang.launch_server \
  --model zai-org/GLM-4.7 \
  --reasoning-parser glm45 \
  --tp 8 \
  --host 0.0.0.0 \
  --port 8000
```

**Streaming with Thinking Process:**

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY"
)

# Enable streaming to see the thinking process in real-time
response = client.chat.completions.create(
    model="zai-org/GLM-4.7",
    messages=[
        {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
    ],
    temperature=0.7,
    max_tokens=2048,
    stream=True
)

# Process the stream
has_thinking = False
has_answer = False
thinking_started = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Print answer content
        if delta.content:
            # Close thinking section and add content header
            if has_thinking and not has_answer:
                print("\n=============== Content =================", flush=True)
                has_answer = True
            print(delta.content, end="", flush=True)

print()
```

**Output Example:**

```text Output
=============== Thinking =================
To solve this problem, I need to calculate 15% of 240.
Step 1: Convert 15% to decimal: 15% = 0.15
Step 2: Multiply 240 by 0.15
Step 3: 240 × 0.15 = 36
=============== Content =================

The answer is 36. To find 15% of 240, we multiply 240 by 0.15, which equals 36.
```

**Note:** The reasoning parser captures the model's step-by-step thinking process, allowing you to see how the model arrives at its conclusions.

#### 4.2.2 Tool Calling

<Note>
**Parser names by model:** GLM-4.5 and GLM-4.6 use `--tool-call-parser glm45`. GLM-4.7 and GLM-4.7-Flash use `--tool-call-parser glm47`. All GLM models use `--reasoning-parser glm45` regardless of generation.
</Note>

GLM-4.7 supports tool calling capabilities. Enable the tool call parser:

```shell Command
python -m sglang.launch_server \
  --model zai-org/GLM-4.7 \
  --reasoning-parser glm45 \
  --tool-call-parser glm47 \
  --tp 8 \
  --host 0.0.0.0 \
  --port 8000
```

**Python Example (with Thinking Process):**

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY"
)

# Define available tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Make request with streaming to see thinking process
response = client.chat.completions.create(
    model="zai-org/GLM-4.7",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools,
    temperature=0.7,
    stream=True
)

# Process streaming response
thinking_started = False
has_thinking = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        # Print thinking process
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        # Print tool calls
        if hasattr(delta, 'tool_calls') and delta.tool_calls:
            # Close thinking section if needed
            if has_thinking and thinking_started:
                print("\n=============== Content =================", flush=True)
                thinking_started = False

            for tool_call in delta.tool_calls:
                if tool_call.function:
                    print(f"Tool Call: {tool_call.function.name}")
                    print(f"   Arguments: {tool_call.function.arguments}")

        # Print content
        if delta.content:
            print(delta.content, end="", flush=True)

print()
```

**Output Example:**

```text Output
=============== Thinking =================
The user is asking about the weather in Beijing. I need to use the get_weather function to retrieve this information.
I should call the function with location="Beijing".
=============== Content =================

Tool Call: get_weather
   Arguments: {"location": "Beijing", "unit": "celsius"}
```

**Note:**

- The reasoning parser shows how the model decides to use a tool
- Tool calls are clearly marked with the function name and arguments
- You can then execute the function and send the result back to continue the conversation

**Handling Tool Call Results:**

```python Example
# After getting the tool call, execute the function
def get_weather(location, unit="celsius"):
    # Your actual weather API call here
    return f"The weather in {location} is 22°{unit[0].upper()} and sunny."

# Send tool result back to the model
messages = [
    {"role": "user", "content": "What's the weather in Beijing?"},
    {
        "role": "assistant",
        "content": None,
        "tool_calls": [{
            "id": "call_123",
            "type": "function",
            "function": {
                "name": "get_weather",
                "arguments": '{"location": "Beijing", "unit": "celsius"}'
            }
        }]
    },
    {
        "role": "tool",
        "tool_call_id": "call_123",
        "content": get_weather("Beijing", "celsius")
    }
]

final_response = client.chat.completions.create(
    model="zai-org/GLM-4.7",
    messages=messages,
    temperature=0.7
)

print(final_response.choices[0].message.content)
# Output: "The weather in Beijing is currently 22°C and sunny."
```

#### 4.2.3 Thinking Budget

Limit the number of thinking tokens using `CustomLogitProcessor`. Launch with `--enable-custom-logit-processor`:

```python Example
import openai
from sglang.srt.sampling.custom_logit_processor import Glm4MoeThinkingBudgetLogitProcessor

client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="*")
response = client.chat.completions.create(
    model="zai-org/GLM-4.7",
    messages=[{"role": "user", "content": "Is Paris the Capital of France?"}],
    max_tokens=1024,
    extra_body={
        "custom_logit_processor": Glm4MoeThinkingBudgetLogitProcessor().to_str(),
        "custom_params": {"thinking_budget": 512},
    },
)
print(response)
```

## 5. Benchmark

This section uses **industry-standard configurations** for comparable benchmark results.

### 5.1 Speed Benchmark

**Test Environment:**

- Hardware: NVIDIA B200, NVIDIA GB200, AMD MI300X/MI325X/MI355X (8x)
- Model: GLM-4.7-NVFP4 on NVIDIA Blackwell; GLM-4.7-FP8 or GLM-4.7 (BF16) on AMD
- SGLang Version: 0.5.12 (NVIDIA Blackwell), 0.5.6.post1 (AMD)
- Best per-GPU throughput config on B200: **TP=2 NVFP4 bf16-KV** (NVFP4 weights, no EP). Numbers below come from this config.

**Benchmark Methodology:**

We use industry-standard benchmark configurations to ensure results are comparable across frameworks and hardware platforms.

#### 5.1.1 Standard Test Scenarios

Four core scenarios reflect real-world usage patterns:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25%"}} />
    <col style={{width: "25%"}} />
    <col style={{width: "25%"}} />
    <col style={{width: "25%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Scenario</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Input Length</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Output Length</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Use Case</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Chat**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1K</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1K</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Most common conversational AI workload</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Reasoning**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1K</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>8K</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Long-form generation, complex reasoning tasks</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Summarization**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>8K</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1K</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Document summarization, RAG retrieval</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Throughput**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>4K</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1K</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Mixed RAG / agent / multi-turn conversation (used for the inline B200 / GB200 results below)</td>
    </tr>
  </tbody>
</table>

#### 5.1.2 Concurrency Levels

Test each scenario at three concurrency levels to capture the throughput vs. latency tradeoff (Pareto frontier):

- **Low Concurrency**: `--max-concurrency 1` (Latency-optimized)
- **Medium Concurrency**: `--max-concurrency 16` (Balanced)
- **High Concurrency**: `--max-concurrency 100` (Throughput-optimized) — the Throughput (4K/1K) scenario uses `--max-concurrency 128` to match the inline B200/GB200 results below.

#### 5.1.3 Number of Prompts

For each concurrency level, configure `num_prompts` to simulate realistic user loads:

- **Quick Test**: `num_prompts = concurrency × 1` (minimal test)
- **Recommended**: `num_prompts = concurrency × 5` (standard benchmark)
- **Stable Measurements**: `num_prompts = concurrency × 10` (production-grade)

---

#### 5.1.4 Benchmark Commands

**Scenario 1: Chat (1K/1K) - Most Important**

- **Model Deployment**
```bash Command
python -m sglang.launch_server \
  --model zai-org/GLM-4.7 \
  --tp 8
```


- Low Concurrency (Latency-Optimized)

```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 1000 \
  --random-output-len 1000 \
  --num-prompts 10 \
  --max-concurrency 1 \
  --request-rate inf
```

- Medium Concurrency (Balanced)
```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 1000 \
  --random-output-len 1000 \
  --num-prompts 80 \
  --max-concurrency 16 \
  --request-rate inf
```

- High Concurrency (Throughput-Optimized)
```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 1000 \
  --random-output-len 1000 \
  --num-prompts 500 \
  --max-concurrency 100 \
  --request-rate inf
```

**Scenario 2: Reasoning (1K/8K)**

- Low Concurrency

```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 1000 \
  --random-output-len 8000 \
  --num-prompts 10 \
  --max-concurrency 1 \
  --request-rate inf
```

- Medium Concurrency
```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 1000 \
  --random-output-len 8000 \
  --num-prompts 80 \
  --max-concurrency 16 \
  --request-rate inf
```

- High Concurrency
```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 1000 \
  --random-output-len 8000 \
  --num-prompts 320 \
  --max-concurrency 64 \
  --request-rate inf
```

**Scenario 3: Summarization (8K/1K)**

- Low Concurrency
```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 8000 \
  --random-output-len 1000 \
  --num-prompts 10 \
  --max-concurrency 1 \
  --request-rate inf
```

- Medium Concurrency
```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 8000 \
  --random-output-len 1000 \
  --num-prompts 80 \
  --max-concurrency 16 \
  --request-rate inf
```

- High Concurrency
```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-4.7 \
  --dataset-name random \
  --random-input-len 8000 \
  --random-output-len 1000 \
  --num-prompts 320 \
  --max-concurrency 64 \
  --request-rate inf
```

**Scenario 4: Throughput (4K/1K) — NVIDIA Blackwell with NVFP4**

The remaining sub-sections (§5.1.4.1 NVIDIA B200, §5.1.4.2 NVIDIA GB200) measure this scenario with `nvidia/GLM-4.7-NVFP4` weights and report the full `bench_serving` output verbatim. The same commands apply to other NVIDIA hardware after substituting the deployment line from §3.1.

> **Note**: These runs use EOS-enabled generation (no `--disable-ignore-eos`), so generated-token counts reflect natural model behavior rather than a strict fixed-OSL pin. Compare against other EOS-enabled runs at the same workload, not against fixed-output-length benchmarks.

#### 5.1.4.1 NVIDIA B200

**Model Deployment (NVIDIA B200, TP=2 NVFP4 — max tok/s/gpu config):**

```bash Command
python -m sglang.launch_server \
  --model nvidia/GLM-4.7-NVFP4 \
  --tp-size 2 \
  --mem-fraction-static 0.85 \
  --reasoning-parser glm45 \
  --tool-call-parser glm47
```

- Low Concurrency (Latency-Optimized)

```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model nvidia/GLM-4.7-NVFP4 \
  --dataset-name random \
  --random-input-len 4096 \
  --random-output-len 1024 \
  --num-prompts 5 \
  --max-concurrency 1 \
  --request-rate inf
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 1
Successful requests:                     5
Benchmark duration (s):                  25.07
Total input tokens:                      8105
Total generated tokens:                  2674
Request throughput (req/s):              0.20
Input token throughput (tok/s):          323.25
Output token throughput (tok/s):         106.65
Total token throughput (tok/s):          429.90
Concurrency:                             1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   5011.93
Median E2E Latency (ms):                 6441.44
---------------Time to First Token----------------
Mean TTFT (ms):                          179.61
Median TTFT (ms):                        169.05
P99 TTFT (ms):                           238.01
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          9.05
Median TPOT (ms):                        9.03
P99 TPOT (ms):                           9.16
---------------Inter-Token Latency----------------
Mean ITL (ms):                           9.05
Median ITL (ms):                         9.05
==================================================
```

- Medium Concurrency (Balanced)

```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model nvidia/GLM-4.7-NVFP4 \
  --dataset-name random \
  --random-input-len 4096 \
  --random-output-len 1024 \
  --num-prompts 80 \
  --max-concurrency 16 \
  --request-rate inf
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 16
Successful requests:                     80
Benchmark duration (s):                  60.60
Total input tokens:                      179772
Total generated tokens:                  39657
Request throughput (req/s):              1.32
Input token throughput (tok/s):          2966.39
Output token throughput (tok/s):         654.37
Total token throughput (tok/s):          3620.76
Concurrency:                             14.01
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   10615.87
Median E2E Latency (ms):                 9985.45
---------------Time to First Token----------------
Mean TTFT (ms):                          267.39
Median TTFT (ms):                        177.26
P99 TTFT (ms):                           584.29
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          20.98
Median TPOT (ms):                        21.06
P99 TPOT (ms):                           24.88
---------------Inter-Token Latency----------------
Mean ITL (ms):                           20.92
Median ITL (ms):                         17.93
==================================================
```

- High Concurrency (Throughput-Optimized)

```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model nvidia/GLM-4.7-NVFP4 \
  --dataset-name random \
  --random-input-len 4096 \
  --random-output-len 1024 \
  --num-prompts 640 \
  --max-concurrency 128 \
  --request-rate inf
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 128
Successful requests:                     640
Benchmark duration (s):                  172.95
Total input tokens:                      1453591
Total generated tokens:                  308740
Request throughput (req/s):              3.70
Input token throughput (tok/s):          8404.67
Output token throughput (tok/s):         1785.14
Total token throughput (tok/s):          10189.80
Concurrency:                             117.85
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   31848.20
Median E2E Latency (ms):                 28554.42
---------------Time to First Token----------------
Mean TTFT (ms):                          1598.40
Median TTFT (ms):                        298.88
P99 TTFT (ms):                           11015.96
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          65.94
Median TPOT (ms):                        65.81
P99 TPOT (ms):                           137.73
---------------Inter-Token Latency----------------
Mean ITL (ms):                           62.99
Median ITL (ms):                         35.44
==================================================
```

#### 5.1.4.2 NVIDIA GB200

**Model Deployment (NVIDIA GB200, TP=2 NVFP4 — max tok/s/gpu config):**

```bash Command
python -m sglang.launch_server \
  --model nvidia/GLM-4.7-NVFP4 \
  --tp-size 2 \
  --mem-fraction-static 0.85 \
  --reasoning-parser glm45 \
  --tool-call-parser glm47
```

- Low Concurrency (Latency-Optimized)

```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model nvidia/GLM-4.7-NVFP4 \
  --dataset-name random \
  --random-input-len 4096 \
  --random-output-len 1024 \
  --num-prompts 5 \
  --max-concurrency 1 \
  --request-rate inf
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 1
Successful requests:                     5
Benchmark duration (s):                  24.74
Total input tokens:                      8105
Total generated tokens:                  2674
Request throughput (req/s):              0.20
Input token throughput (tok/s):          327.65
Output token throughput (tok/s):         108.10
Total token throughput (tok/s):          435.75
Concurrency:                             1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   4944.47
Median E2E Latency (ms):                 6347.31
---------------Time to First Token----------------
Mean TTFT (ms):                          211.41
Median TTFT (ms):                        207.25
P99 TTFT (ms):                           226.46
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          8.86
Median TPOT (ms):                        8.84
P99 TPOT (ms):                           8.96
---------------Inter-Token Latency----------------
Mean ITL (ms):                           8.87
Median ITL (ms):                         8.85
==================================================
```

- Medium Concurrency (Balanced)

```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model nvidia/GLM-4.7-NVFP4 \
  --dataset-name random \
  --random-input-len 4096 \
  --random-output-len 1024 \
  --num-prompts 80 \
  --max-concurrency 16 \
  --request-rate inf
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 16
Successful requests:                     80
Benchmark duration (s):                  60.40
Total input tokens:                      179772
Total generated tokens:                  39657
Request throughput (req/s):              1.32
Input token throughput (tok/s):          2976.52
Output token throughput (tok/s):         656.61
Total token throughput (tok/s):          3633.13
Concurrency:                             13.97
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   10611.51
Median E2E Latency (ms):                 9956.84
---------------Time to First Token----------------
Mean TTFT (ms):                          338.14
Median TTFT (ms):                        215.25
P99 TTFT (ms):                           915.40
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          20.87
Median TPOT (ms):                        21.36
P99 TPOT (ms):                           27.05
---------------Inter-Token Latency----------------
Mean ITL (ms):                           20.77
Median ITL (ms):                         16.53
==================================================
```

- High Concurrency (Throughput-Optimized)

```bash Command
python -m sglang.bench_serving \
  --backend sglang \
  --model nvidia/GLM-4.7-NVFP4 \
  --dataset-name random \
  --random-input-len 4096 \
  --random-output-len 1024 \
  --num-prompts 640 \
  --max-concurrency 128 \
  --request-rate inf
```

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Max request concurrency:                 128
Successful requests:                     640
Benchmark duration (s):                  181.89
Total input tokens:                      1453591
Total generated tokens:                  309221
Request throughput (req/s):              3.52
Input token throughput (tok/s):          7991.59
Output token throughput (tok/s):         1700.04
Total token throughput (tok/s):          9691.63
Concurrency:                             118.86
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   33690.47
Median E2E Latency (ms):                 30421.55
---------------Time to First Token----------------
Mean TTFT (ms):                          1353.16
Median TTFT (ms):                        383.52
P99 TTFT (ms):                           8940.53
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          69.88
Median TPOT (ms):                        71.77
P99 TPOT (ms):                           131.75
---------------Inter-Token Latency----------------
Mean ITL (ms):                           67.23
Median ITL (ms):                         33.46
==================================================
```

#### 5.1.5 Understanding the Results

**Key Metrics:**

- **Request Throughput (req/s)**: Number of requests processed per second
- **Output Token Throughput (tok/s)**: Total tokens generated per second
- **Mean TTFT (ms)**: Time to First Token - measures responsiveness
- **Mean TPOT (ms)**: Time Per Output Token - measures generation speed
- **Mean ITL (ms)**: Inter-Token Latency - measures streaming consistency

**Why These Configurations Matter:**

- **1K/1K (Chat)**: Represents the most common conversational AI workload. This is the highest priority scenario for most deployments.
- **1K/8K (Reasoning)**: Tests long-form generation capabilities crucial for complex reasoning, code generation, and detailed explanations.
- **8K/1K (Summarization)**: Evaluates performance with large context inputs, essential for RAG systems, document Q&A, and summarization tasks.
- **4K/1K (Throughput)**: Realistic mixed workload typical of production deployments (RAG context + medium response). Long enough input that prefill matters, long enough output that decode steady-state dominates. Used for the inline B200 / GB200 results above.
- **Variable Concurrency**: Captures the Pareto frontier - the optimal tradeoff between throughput and latency at different load levels. Low concurrency shows best-case latency, high concurrency shows maximum throughput.

**Interpreting Results:**

- Compare your results against baseline numbers for your hardware
- Higher throughput at same latency = better performance
- Lower TTFT = more responsive user experience
- Lower TPOT = faster generation speed

### 5.2 Accuracy Benchmark

Document model accuracy on standard benchmarks:

#### 5.2.1 GSM8K Benchmark

- Benchmark Command
```bash Command
python -m sglang.test.few_shot_gsm8k \
  --num-shots 5 \
  --num-questions 1319 \
  --port 30000
```

- Test Result (NVIDIA B200, TP=2 NVFP4)
```text Output
Accuracy: 0.946
Latency: 178.284 s
Output throughput: 769.204 token/s
```

- Test Result (NVIDIA GB200, TP=2 NVFP4)
```text Output
Accuracy: 0.951
Latency: 175.190 s
Invalid: 0.000
```
