GLM-5

1. Model Introduction

GLM-5 is the most powerful language model in the GLM series developed by Zhipu AI, targeting complex systems engineering and long-horizon agentic tasks. Scaling from GLM-4.5’s 355B parameters (32B active) to 744B parameters (40B active), GLM-5 integrates DeepSeek Sparse Attention (DSA) to largely reduce deployment cost while preserving long-context capacity.

With advances in both pre-training (28.5T tokens) and post-training via slime (a novel asynchronous RL infrastructure), GLM-5 delivers significant improvements over GLM-4.7 and achieves best-in-class performance among open-source models on reasoning, coding, and agentic tasks.

Key Features:

• Systems Engineering & Agentic Tasks: Purpose-built for complex systems engineering and long-horizon agentic tasks
• State-of-the-Art Performance: Best-in-class among open-source models on reasoning (HLE, AIME, GPQA), coding (SWE-bench, Terminal-Bench), and agentic tasks (BrowseComp, Vending Bench 2)
• DeepSeek Sparse Attention (DSA): Reduces deployment cost while preserving long-context capacity
• Multiple Quantizations: BF16 and FP8 variants for different performance/memory trade-offs
• Speculative Decoding: EAGLE-based speculative decoding support for lower latency

Available Models:

• BF16 (Full precision): zai-org/GLM-5
• FP8 (8-bit quantized): zai-org/GLM-5-FP8

License: MIT

2. SGLang Installation

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

GLM-5 requires a specific SGLang Docker image or install from source:

# For Hopper GPUs (H100/H200)
docker pull lmsysorg/sglang:glm5-hopper

# For Blackwell GPUs (B200)
docker pull lmsysorg/sglang:glm5-blackwell

For other installation methods, please refer to the official SGLang installation guide.

:::note Blackwell (B200) Source Build If you build SGLang from source on Blackwell GPUs, you need to manually compile sgl-kernel due to existing kernel issues (Hopper GPUs are unaffected). See sglang#18595 for details. :::

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, and capabilities.

3.2 Configuration Tips

• Speculative decoding (MTP) can significantly reduce latency for interactive use cases.
• DP Attention: Enables data parallel attention for higher throughput under high concurrency. Note that DP attention trades off low-concurrency latency for high-concurrency throughput — disable it if your workload is latency-sensitive with few concurrent requests.
• The --mem-fraction-static flag is recommended for optimal memory utilization, adjust it based on your hardware and workload.
• BF16 model always requires 2x GPUs compared to FP8:

Hardware	FP8	BF16
H100	tp=16	tp=32
H200	tp=8	tp=16
B200	tp=8	tp=16

4. Model Invocation

Deploy GLM-5 with the following command (FP8 on H200, all features enabled):

python -m sglang.launch_server \
  --model zai-org/GLM-5-FP8 \
  --tp 8 \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.85 \
  --host 0.0.0.0 \
  --port 30000

4.1 Basic Usage

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

• SGLang Basic Usage Guide

4.2 Advanced Usage

4.2.1 Reasoning Parser

GLM-5 supports Thinking mode by default. Enable the reasoning parser during deployment to separate the thinking and content sections. The thinking process is returned via reasoning_content in the streaming response.

To disable thinking and use Instruct mode, pass chat_template_kwargs at request time:

• Thinking mode (default): The model performs step-by-step reasoning before answering. No extra parameters needed.
• Instruct mode ({"enable_thinking": false}): The model responds directly without a thinking process.

Example 1: Thinking Mode (Default)

Thinking mode is enabled by default. The model will reason step-by-step before answering, and the thinking process is returned via reasoning_content:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

# Thinking mode is enabled by default, no extra parameters needed
response = client.chat.completions.create(
    model="zai-org/GLM-5-FP8",
    messages=[
        {"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
    ],
    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:

=============== Thinking =================
The user wants me to solve a math problem: "What is 15% of 240?".

Step 1: Understand the problem. I need to calculate a percentage of a number.
Formula: Percentage × Number = Result.

Step 2: Convert the percentage to a decimal or fraction.
15% = 15/100 or 0.15.

Step 3: Perform the multiplication.
Method A: Decimal multiplication.
0.15 × 240.
Break it down:
10% of 240 = 24.
5% is half of 10%, so 12.
15% = 10% + 5% = 24 + 12 = 36.

Method B: Fraction multiplication.
15/100 × 240.
Simplify 240/100 = 2.4.
15 × 2.4.
10 × 2.4 = 24.
5 × 2.4 = 12.
24 + 12 = 36.

Method C: Direct multiplication.
240 × 0.15.
240 × 0.10 = 24.
240 × 0.05 = 12.
24 + 12 = 36.

Step 4: Final Verification.
Is 36 reasonable?
10% is 24. 20% is 48.
15% is halfway between 10% and 20%.
Halfway between 24 and 48 is 36.
The result is correct.

Step 5: Structure the final response. I will present the calculation clearly, perhaps showing the fractional or decimal method, or the mental math shortcut (10% + 5%).
=============== Content =================
Here is the step-by-step solution:

**Step 1: Convert the percentage to a decimal.**
To convert 15% to a decimal, divide by 100.
$$15\% = \frac{15}{100} = 0.15$$

**Step 2: Multiply the decimal by the number.**
Now, multiply 0.15 by 240.
$$0.15 \times 240$$

**Step 3: Perform the calculation.**
You can break this down to make it easier:
$$0.15 = 0.10 + 0.05$$

*   First, find 10% of 240:
    $$0.10 \times 240 = 24$$
*   Next, find 5% (which is half of 10%):
    $$\frac{24}{2} = 12$$
*   Add the two results together:
    $$24 + 12 = 36$$

**Answer:**
15% of 240 is **36**.

Example 2: Instruct Mode (Thinking Off)

To disable thinking and get a direct response, pass {"enable_thinking": false} via chat_template_kwargs:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY"
)

# Disable thinking mode via chat_template_kwargs
response = client.chat.completions.create(
    model="zai-org/GLM-5-FP8",
    messages=[
        {"role": "user", "content": "What is 15% of 240?"}
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
    max_tokens=2048,
    stream=True
)

# In Instruct mode, the model responds directly without reasoning_content
for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta
        if delta.content:
            print(delta.content, end="", flush=True)

print()

Output Example:

To find **15% of 240**, follow these steps:

### Step 1: Convert the Percentage to a Decimal
First, convert the percentage to a decimal by dividing by 100.

\[
15\% = \frac{15}{100} = 0.15
\]

### Step 2: Multiply by the Number
Next, multiply the decimal by the number you want to find the percentage of.

\[
0.15 \times 240
\]

### Step 3: Perform the Multiplication
Calculate the multiplication:

\[
0.15 \times 240 = 36
\]

### Final Answer
\[
\boxed{36}
\]

4.2.2 Tool Calling

GLM-5 supports tool calling capabilities. Enable the tool call parser during deployment. Thinking mode is on by default; to disable it for tool calling requests, pass extra_body={"chat_template_kwargs": {"enable_thinking": False}}.

Python Example (with Thinking Process):

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/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-5-FP8",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing?"}
    ],
    tools=tools,
    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:

=============== Thinking =================
The user is asking for the weather in Beijing. I have access to a get_weather function that can provide current weather information. Let me check what parameters are required:

- location: required, should be "Beijing"
- unit: optional (not in required array), can be "celsius" or "fahrenheit"

Since the user didn't specify a unit preference and it's optional, I should not ask about it or make up a value. I'll just call the function with the required location parameter.I'll get the current weather in Beijing for you.
=============== Content =================
Tool Call: get_weather
   Arguments:
Tool Call: None
   Arguments: {
Tool Call: None
   Arguments: "location": "Be
Tool Call: None
   Arguments: ijing"
Tool Call: None
   Arguments: }

5. Benchmark

5.1 Speed Benchmark

Test Environment:

• Hardware: H200 (8x)
• Model: GLM-5-FP8
• Tensor Parallelism: 8
• SGLang Version: commit 947927bdb

5.1.1 Latency Benchmark

python3 -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-5-FP8 \
  --dataset-name random \
  --random-input-len 1000 \
  --random-output-len 1000 \
  --num-prompts 10 \
  --max-concurrency 1 \
  --request-rate inf

============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  35.78
Total input tokens:                      6101
Total input text tokens:                 6101
Total generated tokens:                  4220
Total generated tokens (retokenized):    4213
Request throughput (req/s):              0.28
Input token throughput (tok/s):          170.54
Output token throughput (tok/s):         117.96
Peak output token throughput (tok/s):    148.00
Peak concurrent requests:                2
Total token throughput (tok/s):          288.50
Concurrency:                             1.00
Accept length:                           3.48
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   3576.31
Median E2E Latency (ms):                 2935.97
P90 E2E Latency (ms):                    5908.97
P99 E2E Latency (ms):                    8588.08
---------------Time to First Token----------------
Mean TTFT (ms):                          290.88
Median TTFT (ms):                        282.34
P99 TTFT (ms):                           332.27
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          7.54
Median TPOT (ms):                        6.97
P99 TPOT (ms):                           9.04
---------------Inter-Token Latency----------------
Mean ITL (ms):                           7.80
Median ITL (ms):                         6.81
P95 ITL (ms):                            13.51
P99 ITL (ms):                            26.99
Max ITL (ms):                            29.50
==================================================

5.1.2 Throughput Benchmark

python3 -m sglang.bench_serving \
  --backend sglang \
  --model zai-org/GLM-5-FP8 \
  --dataset-name random \
  --random-input-len 1000 \
  --random-output-len 1000 \
  --num-prompts 1000 \
  --max-concurrency 100 \
  --request-rate inf

============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     1000
Benchmark duration (s):                  411.74
Total input tokens:                      502493
Total input text tokens:                 502493
Total generated tokens:                  500251
Total generated tokens (retokenized):    499614
Request throughput (req/s):              2.43
Input token throughput (tok/s):          1220.41
Output token throughput (tok/s):         1214.97
Peak output token throughput (tok/s):    2648.00
Peak concurrent requests:                105
Total token throughput (tok/s):          2435.38
Concurrency:                             96.30
Accept length:                           3.50
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   39648.76
Median E2E Latency (ms):                 39058.12
P90 E2E Latency (ms):                    57009.82
P99 E2E Latency (ms):                    68880.33
---------------Time to First Token----------------
Mean TTFT (ms):                          20613.80
Median TTFT (ms):                        21429.21
P99 TTFT (ms):                           29543.17
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          38.73
Median TPOT (ms):                        36.52
P99 TPOT (ms):                           67.09
---------------Inter-Token Latency----------------
Mean ITL (ms):                           38.13
Median ITL (ms):                         16.57
P95 ITL (ms):                            86.01
P99 ITL (ms):                            164.88
Max ITL (ms):                            1307.02
==================================================

5.2 Accuracy Benchmark

5.2.1 GSM8K Benchmark

• Benchmark Command

python3 benchmark/gsm8k/bench_sglang.py --port 30000

• Test Result

Accuracy: 0.955
Invalid: 0.000
Latency: 32.470 s
Output throughput: 642.044 token/s

5.2.2 MMLU Benchmark

• Benchmark Command

python3 benchmark/mmlu/bench_sglang.py --port 30000

• Test Result

subject: abstract_algebra, #q:100, acc: 0.860
subject: anatomy, #q:135, acc: 0.874
subject: astronomy, #q:152, acc: 0.941
subject: business_ethics, #q:100, acc: 0.880
subject: clinical_knowledge, #q:265, acc: 0.932
subject: college_biology, #q:144, acc: 0.972
subject: college_chemistry, #q:100, acc: 0.640
subject: college_computer_science, #q:100, acc: 0.900
subject: college_mathematics, #q:100, acc: 0.810
subject: college_medicine, #q:173, acc: 0.873
subject: college_physics, #q:102, acc: 0.912
subject: computer_security, #q:100, acc: 0.880
subject: conceptual_physics, #q:235, acc: 0.928
subject: econometrics, #q:114, acc: 0.807
subject: electrical_engineering, #q:145, acc: 0.897
subject: elementary_mathematics, #q:378, acc: 0.937
subject: formal_logic, #q:126, acc: 0.778
subject: global_facts, #q:100, acc: 0.710
subject: high_school_biology, #q:310, acc: 0.961
subject: high_school_chemistry, #q:203, acc: 0.847
subject: high_school_computer_science, #q:100, acc: 0.960
subject: high_school_european_history, #q:165, acc: 0.891
subject: high_school_geography, #q:198, acc: 0.960
subject: high_school_government_and_politics, #q:193, acc: 0.984
subject: high_school_macroeconomics, #q:390, acc: 0.923
subject: high_school_mathematics, #q:270, acc: 0.696
subject: high_school_microeconomics, #q:238, acc: 0.962
subject: high_school_physics, #q:151, acc: 0.821
subject: high_school_psychology, #q:545, acc: 0.956
subject: high_school_statistics, #q:216, acc: 0.889
subject: high_school_us_history, #q:204, acc: 0.941
subject: high_school_world_history, #q:237, acc: 0.945
subject: human_aging, #q:223, acc: 0.857
subject: human_sexuality, #q:131, acc: 0.908
subject: international_law, #q:121, acc: 0.934
subject: jurisprudence, #q:108, acc: 0.907
subject: logical_fallacies, #q:163, acc: 0.933
subject: machine_learning, #q:112, acc: 0.830
subject: management, #q:103, acc: 0.942
subject: marketing, #q:234, acc: 0.940
subject: medical_genetics, #q:100, acc: 0.990
subject: miscellaneous, #q:783, acc: 0.959
subject: moral_disputes, #q:346, acc: 0.873
subject: moral_scenarios, #q:895, acc: 0.837
subject: nutrition, #q:306, acc: 0.922
subject: philosophy, #q:311, acc: 0.897
subject: prehistory, #q:324, acc: 0.929
subject: professional_accounting, #q:282, acc: 0.844
subject: professional_law, #q:1534, acc: 0.714
subject: professional_medicine, #q:272, acc: 0.941
subject: professional_psychology, #q:612, acc: 0.913
subject: public_relations, #q:110, acc: 0.791
subject: security_studies, #q:245, acc: 0.878
subject: sociology, #q:201, acc: 0.940
subject: us_foreign_policy, #q:100, acc: 0.920
subject: virology, #q:166, acc: 0.596
subject: world_religions, #q:171, acc: 0.936
Total latency: 165.275
Average accuracy: 0.877