Holo 3.1 - Baseten

Agentic Multimodal (image)Reasoning Tool calling Long context

Setup

To get started, sign into Baseten with Truss and then install the OpenAI SDK.

Sign in to Baseten

uvx truss login --browser

Install the OpenAI SDK

uv pip install openai

Hcompany/Holo-3.1-35B-A3B-FP8 is a 35B-parameter MoE model (3B active per token) with up to 256K context. This preset serves Holo 3.1 35B-A3B on H100 GPUs with FP8 weights, optimized for high-concurrency throughput on agent and vision workloads.

Hardware

H100

Engine

vLLM (0.20.2-cu129 build)

Context

256K

Concurrency

1000

Write the config

Create and move into the project directory:

mkdir holo-3.1-35b-a3b-throughput && cd holo-3.1-35b-a3b-throughput

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

config.yaml

model_name: "model:holo-3.1-35b-a3b preset:throughput"
model_metadata:
  description: >-
    Holo-3.1-35B-A3B (FP8), H Company's computer-use / GUI-agent vision-language
    model built on the Qwen3.6-35B-A3B MoE base. OpenAI-compatible multimodal chat
    with image input and native function calling, served via vLLM.
  repo_id: Hcompany/Holo-3.1-35B-A3B-FP8
  example_model_input:
    model: Hcompany/Holo-3.1-35B-A3B-FP8
    messages:
      - role: user
        content:
          - type: text
            text: "Describe this image in one sentence."
          - type: image_url
            image_url:
              url: "https://picsum.photos/id/237/200/300"
    stream: true
    max_tokens: 512
    temperature: 1.0
  tags:
    - openai-compatible
    - vllm
    - holo3.1
    - fp8
    - h100
    - multimodal
base_image:
  image: vllm/vllm-openai:v0.20.2-cu129
weights:
  - source: "hf://Hcompany/Holo-3.1-35B-A3B-FP8@main"
    mount_location: "/app/checkpoint/model"
    auth_secret_name: "hf_access_token"
build_commands: []
environment_variables:
  PYTORCH_ALLOC_CONF: "expandable_segments:True"
  VLLM_LOGGING_LEVEL: WARNING
  VLLM_ENGINE_READY_TIMEOUT_S: "3600"
docker_server:
  start_command: >-
    sh -c "GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model
    --tensor-parallel-size $GPU_COUNT
    --served-model-name Hcompany/Holo-3.1-35B-A3B-FP8
    --host 0.0.0.0
    --port 8000
    --gpu-memory-utilization 0.95
    --max-model-len 262144
    --max-num-batched-tokens 32768
    --dtype auto
    --enable-chunked-prefill
    --enable-prefix-caching
    --max-num-seqs 512
    --limit-mm-per-prompt.image 2
    --reasoning-parser qwen3
    --enable-auto-tool-choice
    --tool-call-parser qwen3_coder
    --trust-remote-code"
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000
runtime:
  predict_concurrency: 1000
  health_checks:
    restart_check_delay_seconds: 1500
    restart_threshold_seconds: 1500
    stop_traffic_threshold_seconds: 120
resources:
  accelerator: H100
  use_gpu: true
secrets:
  hf_access_token: null

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`	`$GPU_COUNT`	Number of GPUs to shard the model across.
`--gpu-memory-utilization`	`0.95`	Fraction of GPU memory vLLM may use for weights and KV cache.
`--max-model-len`	`262144`	Maximum context length (tokens) the server accepts per request.
`--max-num-batched-tokens`	`32768`	Maximum total tokens processed per scheduler step.
`--dtype`	`auto`	Weight precision loaded at runtime. auto: Match the model’s checkpoint dtype (default).
`--enable-chunked-prefill`	(no value)	Process long prompts in chunks so decode requests keep running.
`--enable-prefix-caching`	(no value)	Reuse KV cache across requests that share a prefix.
`--max-num-seqs`	`512`	Maximum number of concurrent sequences in the batch.
`--limit-mm-per-prompt.image`	`2`	Maximum number of image inputs per prompt.
`--reasoning-parser`	`qwen3`	Server-side parser that separates reasoning output into `reasoning_content`. qwen3: Qwen3-family thinking format (used by Qwen3, Qwen3.5, and Qwen3.6).
`--enable-auto-tool-choice`	(no value)	Let the model choose when to call tools without requiring `tool_choice: "required"`.
`--tool-call-parser`	`qwen3_coder`	Server-side parser that emits structured `tool_calls` on the response. qwen3_coder: Qwen3-Coder tool format.
`--trust-remote-code`	(no value)	Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures).

Deploy

Push the config to Baseten:

uvx truss push

You should see output similar to:

✨ Model holo-3.1-35b-a3b-throughput was successfully pushed ✨

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

Your model ID is printed in the truss push output (abcd1234 in the example). Use it wherever you see {model_id} in the next section.

Call the model

Your deployment serves an OpenAI-compatible API. Replace {model_id} with your model ID and make sure BASETEN_API_KEY is set. 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="Hcompany/Holo-3.1-35B-A3B-FP8",
    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": "Hcompany/Holo-3.1-35B-A3B-FP8",
    "messages": [
      {"role": "user", "content": "What is machine learning?"}
    ]
  }'

To access the model’s chain of thought, enable thinking mode. The server parses the reasoning output into a separate reasoning_content field on the response:

response = client.chat.completions.create(
    model="Hcompany/Holo-3.1-35B-A3B-FP8",
    messages=[
        {"role": "user", "content": "How many r's in strawberry?"}
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
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="Hcompany/Holo-3.1-35B-A3B-FP8",
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=tools,
)
print(response.choices[0].message.tool_calls)

​Setup

Hardware

Engine

Context

Concurrency

​Write the config

​Flags

​Deploy

​Call the model

Setup

Write the config

Flags

Deploy

Call the model