GLM-5

Z.ai’s GLM-5 reasoning model, served from an FP8 checkpoint on B200:8.

Setup

Sign in to Baseten

uvx truss login --browser

Install the OpenAI SDK

uv pip install openai

This preset serves GLM-5 FP8 on B200:8, Z.ai’s frontier model tuned for the lowest time-to-first-token available.

Hardware

B200 × 8

Engine

vLLM 0.22.0

Context

128K

Concurrency

Write the config

Create and move into the project directory:

mkdir glm-5-latency && cd glm-5-latency

Then create a file named config.yaml and paste the following:

config.yaml

model_metadata:
  example_model_input:
    messages:
      - role: system
        content: "You are a helpful assistant."
      - role: user
        content: "What is the meaning of life?"
    stream: true
    model: zai-org/GLM-5
    max_tokens: 32768
    temperature: 0.7
  tags:
    - openai-compatible

model_name: "model:glm-5 preset:latency"

base_image:
  image: vllm/vllm-openai:v0.22.0

docker_server:
  start_command: >
    sh -c "VLLM_DEEP_GEMM_WARMUP=relax python3 -m vllm.entrypoints.openai.api_server
    --model /models/GLM-5-FP8
    --chat-template /models/GLM-5-FP8/chat_template.jinja
    --host 0.0.0.0 --port 8000
    --served-model-name zai-org/GLM-5
    --tensor-parallel-size 8
    --trust-remote-code
    --load-format runai_streamer
    --disable-log-stats
    --max-num-seqs 64
    --max-num-batched-tokens 8192
    --tool-call-parser glm47
    --reasoning-parser glm45
    --enable-auto-tool-choice
    --speculative-config.method mtp
    --speculative-config.num_speculative_tokens 1"
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000

weights:
  - source: "hf://zai-org/GLM-5-FP8@main"
    mount_location: "/models/GLM-5-FP8"
    ignore_patterns:
      - "*.md"
      - "*.txt"

resources:
  accelerator: B200:8
  use_gpu: true

runtime:
  predict_concurrency: 64
  health_checks:
    restart_check_delay_seconds: 1800
    restart_threshold_seconds: 1200
    stop_traffic_threshold_seconds: 120

Flags

The start_command passes these flags to the engine. Each one controls a runtime or serving behavior:

Flag	Value	What it does
`--model`	`/models/GLM-5-FP8`	Path (or HF repo) the engine loads the model from.
`--chat-template`	`/models/GLM-5-FP8/chat_template.jinja`	Path to a Jinja chat template file that overrides the checkpoint’s default.
`--tensor-parallel-size`	`8`	Number of GPUs to shard the model across.
`--trust-remote-code`	(no value)	Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures).
`--load-format`	`runai_streamer`	Weight loading backend. runai_streamer: Stream weights from object storage without materializing to disk.
`--disable-log-stats`	(no value)	Suppress periodic engine stats logging.
`--max-num-seqs`	`64`	Maximum number of concurrent sequences in the batch.
`--max-num-batched-tokens`	`8192`	Maximum total tokens processed per scheduler step.
`--tool-call-parser`	`glm47`	Server-side parser that emits structured `tool_calls` on the response.
`--reasoning-parser`	`glm45`	Server-side parser that separates reasoning output into `reasoning_content`.
`--enable-auto-tool-choice`	(no value)	Let the model choose when to call tools without requiring `tool_choice: "required"`.
`--speculative-config.method`	`mtp`	Speculative decoding method. mtp: Multi-token prediction head speculation.
`--speculative-config.num_speculative_tokens`	`1`	Number of tokens the draft speculator proposes per step.

Deploy

Push the config to Baseten:

uvx truss push

You should see output similar to:

✨ Model glm-5-latency was successfully pushed ✨

   Model ID:      abc1d2ef
   Deployment ID: xyz123
   Endpoint:      model-abc1d2ef.api.baseten.co
   Logs:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

truss push prints your model ID (abc1d2ef in the example). The examples below use it wherever you see {model_id}, and read your API key from the BASETEN_API_KEY environment variable.

Call the model

Your deployment serves an OpenAI-compatible API. Now call your deployment to run inference:

Python
cURL

main.py

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["BASETEN_API_KEY"],
    base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1",
)

response = client.chat.completions.create(
    model="zai-org/GLM-5",
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ],
)

print(response.choices[0].message.content)

curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "zai-org/GLM-5",
    "messages": [
      {"role": "user", "content": "What is machine learning?"}
    ]
  }'

The server parses the model’s chain of thought into a separate reasoning_content field on the response. Read it alongside the final answer:

response = client.chat.completions.create(
    model="zai-org/GLM-5",
    messages=[
        {"role": "user", "content": "How many r's in strawberry?"}
    ],
)
print(response.choices[0].message.reasoning_content)  # chain of thought
print(response.choices[0].message.content)            # final answer

To let the model call tools, pass a tools array. The server returns structured tool_calls on the response:

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"],
        },
    },
}]

response = client.chat.completions.create(
    model="zai-org/GLM-5",
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=tools,
)
print(response.choices[0].message.tool_calls)

Examples

Models

Engines

Custom Docker servers

Custom Python models

Chains

Setup

Hardware

Engine

Context

Concurrency

Write the config

Flags

Deploy

Call the model

Next steps

Call your model

Autoscaling

​Setup

Hardware

Engine

Context

Concurrency

​Write the config

​Flags

​Deploy

​Call the model

​Next steps

Call your model

Autoscaling

Setup

Write the config

Flags

Deploy

Call the model

Next steps