GLM-5.3 Flash

Reasoning Tool calling Multimodal (image)Long context

Z.ai GLM-5.3 Flash is a multimodal mixture-of-experts model with 321B total and 18B active parameters. This preset serves the native FP8 checkpoint on eight H100 GPUs through vLLM with a 1M-token context window, image and video inputs, always-on reasoning, automatic tool calling, and multi-token prediction speculative decoding.

Setup

Sign in to Baseten

uvx truss login --browser

Install the OpenAI SDK

uv pip install openai

This preset serves GLM-5.3 Flash on H100:8 from its native FP8 checkpoint, with a multi-token prediction head proposing five speculative tokens per step to speed up decoding.

Hardware

H100 × 8

Engine

vLLM (glm53-flash build)

Context

Concurrency

Write the config

Create and move into the project directory:

mkdir glm-5.3-flash-latency && cd glm-5.3-flash-latency

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

config.yaml

model_name: "model:glm-5.3-flash preset:latency"

model_metadata:
  description: >-
    Z.ai GLM-5.3-Flash is a 321B-parameter, 18B-active multimodal MoE served from
    its native FP8 checkpoint with vLLM on eight H100 GPUs. It provides
    OpenAI-compatible chat completions, reasoning, tool calling, image and video
    inputs, MTP speculative decoding, and a 1M-token context window.
  repo_id: zai-org/GLM-5.3-Flash
  tags:
    - openai-compatible
    - multimodal
    - moe
    - reasoning
    - tool-use
    - fp8
  example_model_input:
    model: zai-org/GLM-5.3-Flash
    messages:
      - role: user
        content: "Explain sparse attention in three concise sentences."
    stream: true
    max_tokens: 512
    temperature: 1.0
    top_p: 0.95

base_image:
  # Dedicated image required until GLM-5.3-Flash support reaches a stable vLLM release.
  image: "vllm/vllm-openai:glm53-flash@sha256:2c6da6c6f16ed15c91e412d896dba13701f25fe1861eaec9ddaa4db34d1d21c4"

weights:
  - source: "hf://zai-org/GLM-5.3-Flash@3f1971b7b5f7a528c9c4ef6212c8785298a8c24a"
    mount_location: "/app/checkpoint/model"
    auth_secret_name: "hf_access_token"

secrets:
  hf_access_token: null

environment_variables:
  VLLM_LOGGING_LEVEL: WARNING
  VLLM_ENGINE_READY_TIMEOUT_S: "3600"

docker_server:
  start_command: >-
    vllm serve /app/checkpoint/model
    --host 0.0.0.0
    --port 8000
    --served-model-name zai-org/GLM-5.3-Flash
    --tensor-parallel-size 8
    --max-model-len 1048576
    --gpu-memory-utilization 0.90
    --max-num-seqs 16
    --enable-prefix-caching
    --limit-mm-per-prompt.image 1
    --limit-mm-per-prompt.video 1
    --tool-call-parser glm47
    --reasoning-parser glm45
    --default-chat-template-kwargs.reasoning_effort high
    --enable-auto-tool-choice
    --speculative-config.method mtp
    --speculative-config.num_speculative_tokens 5
    --no-enable-flashinfer-autotune
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000

resources:
  instance_type: H100:8

runtime:
  predict_concurrency: 16
  health_checks:
    restart_check_delay_seconds: 1800
    restart_threshold_seconds: 1200
    stop_traffic_threshold_seconds: 120

The container loads the FP8 checkpoint to /app/checkpoint/model and serves the OpenAI-compatible API on port 8000 with tensor parallel size 8 across the eight H100 GPUs. The engine caps each request at 1,048,576 tokens and runs 16 concurrent sequences, keeping weights in FP8 while the KV cache stays in BF16, because the current implementation does not support an FP8 KV cache on Hopper. Requests that omit reasoning_effort inherit the server default of high, and each prompt accepts one image and one video alongside its text.

Flags

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

Flag	Value	What it does
`--tensor-parallel-size`	`8`	Number of GPUs to shard the model across.
`--max-model-len`	`1048576`	Maximum context length (tokens) the server accepts per request.
`--gpu-memory-utilization`	`0.90`	Fraction of GPU memory vLLM may use for weights and KV cache.
`--max-num-seqs`	`16`	Maximum number of concurrent sequences in the batch.
`--enable-prefix-caching`	(no value)	Reuse KV cache across requests that share a prefix.
`--limit-mm-per-prompt.image`	`1`	Maximum number of image inputs per prompt.
`--limit-mm-per-prompt.video`	`1`	Maximum number of video inputs per prompt.
`--tool-call-parser`	`glm47`	Server-side parser that emits structured `tool_calls` on the response. glm47: GLM-4.7-style tool format, also used by GLM-5 and GLM-5.3.
`--reasoning-parser`	`glm45`	Server-side parser that separates reasoning output into `reasoning_content`. glm45: GLM-4.5-style thinking format, also used by GLM-5 and GLM-5.3.
`--default-chat-template-kwargs.reasoning_effort`	`high`	Reasoning effort the chat template applies when a request omits `reasoning_effort`. Clients override it per request.
`--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`	`5`	Number of tokens the draft speculator proposes per step.
`--no-enable-flashinfer-autotune`	(no value)	Skip the FlashInfer kernel autotuning pass that normally runs at server startup.

Deploy

Push the config to Baseten:

uvx truss push

You should see output similar to:

✨ Model glm-5.3-flash-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-5.3-Flash",
    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-5.3-Flash",
    "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-5.3-Flash",
    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-5.3-Flash",
    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