---
title: GLM-5
metatags:
description: "Deploy GLM-5 with SGLang on NVIDIA H100/H200/B200 and AMD MI300X/MI325X/MI355X — state-of-the-art reasoning, enhanced coding, and robust tool calling capabilities."
---
## 1. Model Introduction
[GLM-5](https://huggingface.co/zai-org/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](https://github.com/THUDM/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](https://huggingface.co/zai-org/GLM-5)
- **FP8 (8-bit quantized)**: [zai-org/GLM-5-FP8](https://huggingface.co/zai-org/GLM-5-FP8)
**License:** MIT
## 2. SGLang Installation
Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
## 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. SGLang supports serving GLM-5 on NVIDIA H100, H200, B200, and AMD MI300X/MI325X/MI355X GPUs.
import { GLM5Deployment } from '/src/snippets/autoregressive/glm-5-deployment.jsx'
All recipes here run the DSA indexer top-k on the default `--dsa-topk-backend sgl-kernel`. Other top-k backend choices have not been fully validated on GLM-5.
### 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 on NVIDIA hardware.
| Hardware |
FP8 |
BF16 |
| H100 |
tp=16 |
tp=32 |
| H200 |
tp=8 |
tp=16 |
| B200 |
tp=8 |
tp=16 |
| MI300X/MI325X |
— |
tp=8 |
| MI355X |
— |
tp=8 |
- **B200 (FP8)**: Use `--ep 1 --attention-backend dsa --dsa-decode-backend trtllm --dsa-prefill-backend trtllm --moe-runner-backend flashinfer_trtllm --enable-flashinfer-allreduce-fusion` for optimized DSA and MoE backends on Blackwell. Also add `--quantization fp8` for FP8 weight quantization.
- **AMD GPUs**: Use `--dsa-prefill-backend tilelang --dsa-decode-backend tilelang` for the DSA attention backend. Add `--chunked-prefill-size 131072` and `--watchdog-timeout 1200` (20 minutes for weight loading). EAGLE speculative decoding is not currently supported on AMD for GLM-5.
- For other configuration tips (MTP, DSA kernel, Context Parallel, HiSparse, NVFP4, Index Cache), see the [DeepSeek-V3.2 cookbook page](../DeepSeek/DeepSeek-V3_2). GLM-5 and DeepSeek-V3.2 share the same model structure, so the optimization techniques are common.
- **Prefill CP on CUDA**: Zigzag (`--cp-strategy zigzag`) is temporarily unavailable for GLM-5. Use `--enable-prefill-cp --cp-strategy interleave` with `--dp 1`.
- Use `--json-model-override-args '{"index_topk_pattern": "FFSFSSSFSSFFFSSSFFFSFSSSSSSFFSFFSFFSSFFFFFFSFFFFFSFFSSSSSSFSFFFSFSSSFSFFSFFSSS"}'` for GLM-5-FP8 if you want to enable the [IndexCache](https://github.com/THUDM/IndexCache) method. This feature is supported through [this PR](https://github.com/sgl-project/sglang/pull/21405) and introduces only a small accuracy loss. However, if you are running rigorous accuracy evaluations, it is not recommended to enable this feature.
## 4. Model Invocation
Deploy GLM-5 with the following command (FP8 on H200, all features enabled):
```shell Command
sglang serve \
--model-path 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 \
--enable-flashinfer-allreduce-fusion \
--mem-fraction-static 0.85 \
--host 0.0.0.0 \
--port 30000
```
### 4.1 MI300X/MI325X/MI355X (ROCm) Server Command
The following ROCm command is an additional option for AMD GPUs and does not replace the NVIDIA instructions above.
```shell Command
sglang serve \
--model-path zai-org/GLM-5 \
--tp 8 \
--trust-remote-code \
--dsa-prefill-backend tilelang \
--dsa-decode-backend tilelang \
--chunked-prefill-size 131072 \
--mem-fraction-static 0.80 \
--watchdog-timeout 1200 \
--host 0.0.0.0 \
--port 30000
```
### 4.2 Basic Usage
For basic API usage and request examples, please refer to:
- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
### 4.3 Advanced Usage
#### 4.3.1 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`:
```python Example
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:**
```text Output
=============== 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`:
```python Example
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:**
```text Output
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
\[
\boxed{36}
\]
```
#### 4.3.2 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):**
```python Example
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:**
```text Output
=============== 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
```bash Command
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
```
```text Output
============ 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
```bash Command
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
```
```text Output
============ 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
The accuracy benchmark results below are shared with GLM-5.1, as GLM-5.1 was not independently benchmarked at the time of this writing. A separate GLM-5.1 benchmark run is planned.
#### 5.2.1 GSM8K Benchmark
- Benchmark Command
```bash Command
python3 benchmark/gsm8k/bench_sglang.py --port 30000
```
- Test Result
```text Output
Accuracy: 0.955
Invalid: 0.000
Latency: 32.470 s
Output throughput: 642.044 token/s
```
#### 5.2.2 MMLU Benchmark
- Benchmark Command
```bash Command
python3 benchmark/mmlu/bench_sglang.py --port 30000
```
- Test Result
```text Output
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
```
### 5.3 AMD GPU Benchmarks
#### 5.3.1 GSM8K Benchmark (MI325/MI35x)
- MI325/MI35x Test (GLM-5 BF16, `tp=8`, TileLang DSA backends)
```bash Command
python3 benchmark/gsm8k/bench_sglang.py --num-questions 200
```
```text Output
Accuracy: 0.970
Invalid: 0.000
```
Results from [AMD nightly CI](https://github.com/sgl-project/sglang/actions/runs/22556197510/attempts/2#summary-65346783629). See also [sglang#18911](https://github.com/sgl-project/sglang/pull/18911).