> ## Documentation Index
> Fetch the complete documentation index at: https://docs.baseten.co/llms.txt
> Use this file to discover all available pages before exploring further.
# Qwen3.5
> Qwen3.5 recipes: 4 variants (4B, 9B, 35B, 122B), Dense, Hybrid MoE, and MoE architectures.
## Setup
To get started, sign into Baseten with Truss and then install the OpenAI SDK.
**Sign in to Baseten**
```sh theme={"system"}
uvx truss login --browser
```
**Install the OpenAI SDK**
```sh theme={"system"}
uv pip install openai
```
Pick the model you want to deploy. Each tab is a self-contained recipe.
[Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) is a 4B-parameter dense model with up to 256K context.
This preset serves Qwen3.5-4B with BF16 weights on a single H100, optimized for low time-to-first-token.
H100 × 1
vLLM (0.22.0-cu129 build)
32K
128
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir qwen3.5-4b-latency && cd qwen3.5-4b-latency
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
model_name: "model:qwen3.5-4b preset:latency"
model_metadata:
description: >-
Qwen 3.5 4B instruct (dense), OpenAI-compatible chat via vLLM with Qwen tooling parsers.
repo_id: Qwen/Qwen3.5-4B
example_model_input:
model: "Qwen/Qwen3.5-4B"
messages:
- role: user
content: "What is the capital of France?"
stream: true
max_tokens: 100
temperature: 0.7
tags:
- openai-compatible
base_image:
image: vllm/vllm-openai:v0.22.0-cu129
weights:
- source: "hf://Qwen/Qwen3.5-4B@main"
mount_location: "/app/checkpoint/model"
auth_secret_name: "hf_access_token"
secrets:
hf_access_token: null
docker_server:
start_command: >-
sh -c "GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model --tensor-parallel-size $GPU_COUNT
--served-model-name Qwen/Qwen3.5-4B
--host 0.0.0.0
--port 8000
--gpu-memory-utilization 0.95
--max-model-len 32768
--dtype bfloat16
--reasoning-parser qwen3
--enable-auto-tool-choice
--tool-call-parser qwen3_coder
--trust-remote-code
--enable-prefix-caching
--load-format runai_streamer"
readiness_endpoint: /health
liveness_endpoint: /health
predict_endpoint: /v1/chat/completions
server_port: 8000
environment_variables:
VLLM_LOGGING_LEVEL: WARNING
VLLM_ENGINE_READY_TIMEOUT_S: "3600"
runtime:
predict_concurrency: 128
health_checks:
restart_check_delay_seconds: 1800
restart_threshold_seconds: 1200
stop_traffic_threshold_seconds: 120
resources:
accelerator: H100:1
use_gpu: true
```
## Flags
The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior:
| Flag | Value | What it does |
| --------------------------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--tensor-parallel-size` | `$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` | `32768` | Maximum context length (tokens) the server accepts per request. |
| `--dtype` | `bfloat16` | Weight precision loaded at runtime. **bfloat16:** BF16 weights, no quantization. |
| `--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). |
| `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. |
| `--load-format` | `runai_streamer` | Weight loading backend. **runai\_streamer:** Stream weights from object storage without materializing to disk. |
## Deploy
Push the config to Baseten:
```sh theme={"system"}
uvx truss push
```
You should see output similar to:
```output theme={"system"}
✨ Model qwen3.5-4b-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
```
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 main.py theme={"system"}
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="Qwen/Qwen3.5-4B",
messages=[
{"role": "user", "content": "What is machine learning?"}
],
)
print(response.choices[0].message.content)
```
```sh theme={"system"}
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": "Qwen/Qwen3.5-4B",
"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:
```python theme={"system"}
response = client.chat.completions.create(
model="Qwen/Qwen3.5-4B",
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:
```python theme={"system"}
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"],
},
},
}]
response = client.chat.completions.create(
model="Qwen/Qwen3.5-4B",
messages=[
{"role": "user", "content": "What's the weather in Paris?"}
],
tools=tools,
)
print(response.choices[0].message.tool_calls)
```
[Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) is a 9B-parameter dense model with up to 256K context.
This preset serves Qwen3.5-9B with BF16 weights on a single H100. It's the smallest dense Qwen3.5 deployment that keeps reasoning and tool calling enabled.
H100 × 1
vLLM (0.22.0-cu129 build)
32K
128
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir qwen3.5-9b-latency && cd qwen3.5-9b-latency
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
model_name: "model:qwen3.5-9b preset:latency"
model_metadata:
description: >-
Qwen 3.5 9B instruct (dense), OpenAI-compatible chat via vLLM with Qwen tooling parsers.
repo_id: Qwen/Qwen3.5-9B
example_model_input:
model: "Qwen/Qwen3.5-9B"
messages:
- role: user
content: "What is the capital of France?"
stream: true
max_tokens: 100
temperature: 0.7
tags:
- openai-compatible
base_image:
image: vllm/vllm-openai:v0.22.0-cu129
weights:
- source: "hf://Qwen/Qwen3.5-9B@main"
mount_location: "/app/checkpoint/model"
auth_secret_name: "hf_access_token"
secrets:
hf_access_token: null
docker_server:
start_command: >-
sh -c "GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model --tensor-parallel-size $GPU_COUNT
--served-model-name Qwen/Qwen3.5-9B
--host 0.0.0.0
--port 8000
--gpu-memory-utilization 0.95
--max-model-len 32768
--dtype bfloat16
--reasoning-parser qwen3
--enable-auto-tool-choice
--tool-call-parser qwen3_coder
--trust-remote-code
--enable-prefix-caching
--load-format runai_streamer"
readiness_endpoint: /health
liveness_endpoint: /health
predict_endpoint: /v1/chat/completions
server_port: 8000
environment_variables:
VLLM_LOGGING_LEVEL: WARNING
VLLM_ENGINE_READY_TIMEOUT_S: "3600"
runtime:
predict_concurrency: 128
health_checks:
restart_check_delay_seconds: 1800
restart_threshold_seconds: 1200
stop_traffic_threshold_seconds: 120
resources:
accelerator: H100:1
use_gpu: true
```
## Flags
The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior:
| Flag | Value | What it does |
| --------------------------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--tensor-parallel-size` | `$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` | `32768` | Maximum context length (tokens) the server accepts per request. |
| `--dtype` | `bfloat16` | Weight precision loaded at runtime. **bfloat16:** BF16 weights, no quantization. |
| `--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). |
| `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. |
| `--load-format` | `runai_streamer` | Weight loading backend. **runai\_streamer:** Stream weights from object storage without materializing to disk. |
## Deploy
Push the config to Baseten:
```sh theme={"system"}
uvx truss push
```
You should see output similar to:
```output theme={"system"}
✨ Model qwen3.5-9b-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
```
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 main.py theme={"system"}
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="Qwen/Qwen3.5-9B",
messages=[
{"role": "user", "content": "What is machine learning?"}
],
)
print(response.choices[0].message.content)
```
```sh theme={"system"}
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": "Qwen/Qwen3.5-9B",
"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:
```python theme={"system"}
response = client.chat.completions.create(
model="Qwen/Qwen3.5-9B",
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:
```python theme={"system"}
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"],
},
},
}]
response = client.chat.completions.create(
model="Qwen/Qwen3.5-9B",
messages=[
{"role": "user", "content": "What's the weather in Paris?"}
],
tools=tools,
)
print(response.choices[0].message.tool_calls)
```
[Qwen/Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B) is a 35B-parameter hybrid MoE model (3B active per token) with up to 256K context.
This variant ships in 2 presets tuned for different goals: **Latency** for lowest time-to-first-token, and **Throughput** for highest tokens per second. Pick the tab that matches your workload.
This preset serves Qwen3.5-35B with FP8 weights on H100:2, optimized for low time-to-first-token on interactive chat and short-horizon agent workflows.
H100 × 2
vLLM (0.22.0-cu129 build)
32K
128
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir qwen3.5-35b-latency && cd qwen3.5-35b-latency
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
model_name: "model:qwen3.5-35b preset:latency"
model_metadata:
description: >-
Qwen 3.5 35B A3B MoE instruct FP8 weights, TP=2 latency preset with Qwen parsers.
repo_id: Qwen/Qwen3.5-35B-A3B-FP8
example_model_input:
model: "Qwen/Qwen3.5-35B-A3B-FP8"
messages:
- role: user
content: "What is the capital of France?"
stream: true
max_tokens: 100
temperature: 0.7
tags:
- openai-compatible
base_image:
image: vllm/vllm-openai:v0.22.0-cu129
weights:
- source: "hf://Qwen/Qwen3.5-35B-A3B-FP8@main"
mount_location: "/app/checkpoint/model"
auth_secret_name: "hf_access_token"
secrets:
hf_access_token: null
docker_server:
start_command: >-
sh -c "GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model
--served-model-name Qwen/Qwen3.5-35B-A3B-FP8
--host 0.0.0.0
--port 8000
--gpu-memory-utilization 0.95
--max-model-len 32768
--kv-cache-dtype fp8
--tensor-parallel-size $GPU_COUNT
--reasoning-parser qwen3
--enable-auto-tool-choice
--tool-call-parser qwen3_coder
--trust-remote-code
--enable-prefix-caching
--load-format runai_streamer"
readiness_endpoint: /health
liveness_endpoint: /health
predict_endpoint: /v1/chat/completions
server_port: 8000
environment_variables:
VLLM_LOGGING_LEVEL: WARNING
VLLM_ENGINE_READY_TIMEOUT_S: "3600"
runtime:
predict_concurrency: 128
health_checks:
restart_check_delay_seconds: 1800
restart_threshold_seconds: 1200
stop_traffic_threshold_seconds: 120
resources:
accelerator: H100:2
use_gpu: true
```
## Flags
The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior:
| Flag | Value | What it does |
| --------------------------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--gpu-memory-utilization` | `0.95` | Fraction of GPU memory vLLM may use for weights and KV cache. |
| `--max-model-len` | `32768` | Maximum context length (tokens) the server accepts per request. |
| `--kv-cache-dtype` | `fp8` | KV cache numeric precision. **fp8:** \~2× KV cache density with negligible quality impact on most models. |
| `--tensor-parallel-size` | `$GPU_COUNT` | Number of GPUs to shard the model across. |
| `--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). |
| `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. |
| `--load-format` | `runai_streamer` | Weight loading backend. **runai\_streamer:** Stream weights from object storage without materializing to disk. |
## Deploy
Push the config to Baseten:
```sh theme={"system"}
uvx truss push
```
You should see output similar to:
```output theme={"system"}
✨ Model qwen3.5-35b-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
```
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 main.py theme={"system"}
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="Qwen/Qwen3.5-35B-A3B-FP8",
messages=[
{"role": "user", "content": "What is machine learning?"}
],
)
print(response.choices[0].message.content)
```
```sh theme={"system"}
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": "Qwen/Qwen3.5-35B-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:
```python theme={"system"}
response = client.chat.completions.create(
model="Qwen/Qwen3.5-35B-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:
```python theme={"system"}
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"],
},
},
}]
response = client.chat.completions.create(
model="Qwen/Qwen3.5-35B-A3B-FP8",
messages=[
{"role": "user", "content": "What's the weather in Paris?"}
],
tools=tools,
)
print(response.choices[0].message.tool_calls)
```
This preset serves Qwen3.5-35B FP8 on a single B200, with prefix caching and chunked prefill enabled. It maximizes aggregate throughput at high concurrency with minor quality impact from FP8.
B200
vLLM 0.22.0
256K
1000
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir qwen3.5-35b-throughput && cd qwen3.5-35b-throughput
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
########################################################
# Remove ( --language-model-only ) from the start command to turn on multimodal mode
########################################################
model_name: "model:qwen3.5-35b preset:throughput"
model_metadata:
description: >-
Qwen 3.5 35B A3B FP8 MoE throughput on B200, language-only mode (--language-model-only) optional.
repo_id: Qwen/Qwen3.5-35B-A3B-FP8
example_model_input:
model: "Qwen/Qwen3.5-35B-A3B-FP8"
messages:
- role: user
content: "What is the capital of France?"
stream: true
max_tokens: 100
temperature: 0.7
tags:
- openai-compatible
base_image:
image: vllm/vllm-openai:v0.22.0
weights:
- source: "hf://Qwen/Qwen3.5-35B-A3B-FP8@main"
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"
VLLM_USE_FLASHINFER_MOE_FP8: "0"
PYTORCH_ALLOC_CONF: "expandable_segments:True"
docker_server:
start_command: >-
sh -c "GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model
--tensor-parallel-size $GPU_COUNT
--served-model-name Qwen/Qwen3.5-35B-A3B-FP8
--host 0.0.0.0
--port 8000
--language-model-only
--gpu-memory-utilization 0.95
--kv-cache-dtype fp8
--reasoning-parser qwen3
--enable-chunked-prefill
--enable-prefix-caching
--max-num-seqs 512
--trust-remote-code
--load-format runai_streamer"
readiness_endpoint: /health
liveness_endpoint: /health
predict_endpoint: /v1/chat/completions
server_port: 8000
runtime:
predict_concurrency: 1000
health_checks:
restart_check_delay_seconds: 1800
restart_threshold_seconds: 1200
stop_traffic_threshold_seconds: 120
resources:
accelerator: B200
use_gpu: true
```
## Flags
The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior:
| Flag | Value | What it does |
| -------------------------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--tensor-parallel-size` | `$GPU_COUNT` | Number of GPUs to shard the model across. |
| `--language-model-only` | (no value) | Disable the multimodal path; text-only serving. Remove to enable image/video inputs. |
| `--gpu-memory-utilization` | `0.95` | Fraction of GPU memory vLLM may use for weights and KV cache. |
| `--kv-cache-dtype` | `fp8` | KV cache numeric precision. **fp8:** \~2× KV cache density with negligible quality impact on most models. |
| `--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-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. |
| `--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. |
## Deploy
Push the config to Baseten:
```sh theme={"system"}
uvx truss push
```
You should see output similar to:
```output theme={"system"}
✨ Model qwen3.5-35b-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 main.py theme={"system"}
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="Qwen/Qwen3.5-35B-A3B-FP8",
messages=[
{"role": "user", "content": "What is machine learning?"}
],
)
print(response.choices[0].message.content)
```
```sh theme={"system"}
curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $BASETEN_API_KEY" \
-d '{
"model": "Qwen/Qwen3.5-35B-A3B-FP8",
"messages": [
{"role": "user", "content": "What is machine learning?"}
]
}'
```
To access the model's chain of thought, enable thinking mode. The server parses the reasoning output into a separate `reasoning_content` field on the response:
```python theme={"system"}
response = client.chat.completions.create(
model="Qwen/Qwen3.5-35B-A3B-FP8",
messages=[
{"role": "user", "content": "How many r's in strawberry?"}
],
extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
print(response.choices[0].message.reasoning_content) # chain of thought
print(response.choices[0].message.content) # final answer
```
[Qwen/Qwen3.5-122B-A10B](https://huggingface.co/Qwen/Qwen3.5-122B-A10B) is a 122B-parameter MoE model (10B active per token) with up to 256K context.
This preset serves Qwen3.5-122B with FP8 weights on H100:4. It keeps time-to-first-token low while fitting the full model on a single H100 node.
H100 × 4
vLLM (0.22.0-cu129 build)
32K
128
## Write the config
Create and move into the project directory:
```sh theme={"system"}
mkdir qwen3.5-122b-latency && cd qwen3.5-122b-latency
```
Then create a file named `config.yaml` and paste the following:
```yaml config.yaml theme={"system"}
model_name: "model:qwen3.5-122b preset:latency"
model_metadata:
description: >-
Qwen 3.5 122B A10B MoE instruct FP8 weights, TP=4 latency preset via vLLM with Qwen parsers.
repo_id: Qwen/Qwen3.5-122B-A10B-FP8
example_model_input:
model: "Qwen/Qwen3.5-122B-A10B-FP8"
messages:
- role: user
content: "What is the capital of France?"
stream: true
max_tokens: 100
temperature: 0.7
tags:
- openai-compatible
base_image:
image: vllm/vllm-openai:v0.22.0-cu129
weights:
- source: "hf://Qwen/Qwen3.5-122B-A10B-FP8@main"
mount_location: "/app/checkpoint/model"
auth_secret_name: "hf_access_token"
secrets:
hf_access_token: null
docker_server:
start_command: >-
sh -c "GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model
--served-model-name Qwen/Qwen3.5-122B-A10B-FP8
--host 0.0.0.0
--port 8000
--gpu-memory-utilization 0.95
--max-model-len 32768
--kv-cache-dtype fp8
--tensor-parallel-size $GPU_COUNT
--reasoning-parser qwen3
--enable-auto-tool-choice
--tool-call-parser qwen3_coder
--trust-remote-code
--enable-prefix-caching
--load-format runai_streamer"
readiness_endpoint: /health
liveness_endpoint: /health
predict_endpoint: /v1/chat/completions
server_port: 8000
environment_variables:
VLLM_LOGGING_LEVEL: WARNING
VLLM_ENGINE_READY_TIMEOUT_S: "3600"
runtime:
predict_concurrency: 128
health_checks:
restart_check_delay_seconds: 1800
restart_threshold_seconds: 1200
stop_traffic_threshold_seconds: 120
resources:
accelerator: H100:4
use_gpu: true
```
## Flags
The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior:
| Flag | Value | What it does |
| --------------------------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `--gpu-memory-utilization` | `0.95` | Fraction of GPU memory vLLM may use for weights and KV cache. |
| `--max-model-len` | `32768` | Maximum context length (tokens) the server accepts per request. |
| `--kv-cache-dtype` | `fp8` | KV cache numeric precision. **fp8:** \~2× KV cache density with negligible quality impact on most models. |
| `--tensor-parallel-size` | `$GPU_COUNT` | Number of GPUs to shard the model across. |
| `--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). |
| `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. |
| `--load-format` | `runai_streamer` | Weight loading backend. **runai\_streamer:** Stream weights from object storage without materializing to disk. |
## Deploy
Push the config to Baseten:
```sh theme={"system"}
uvx truss push
```
You should see output similar to:
```output theme={"system"}
✨ Model qwen3.5-122b-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
```
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 main.py theme={"system"}
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="Qwen/Qwen3.5-122B-A10B-FP8",
messages=[
{"role": "user", "content": "What is machine learning?"}
],
)
print(response.choices[0].message.content)
```
```sh theme={"system"}
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": "Qwen/Qwen3.5-122B-A10B-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:
```python theme={"system"}
response = client.chat.completions.create(
model="Qwen/Qwen3.5-122B-A10B-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:
```python theme={"system"}
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"],
},
},
}]
response = client.chat.completions.create(
model="Qwen/Qwen3.5-122B-A10B-FP8",
messages=[
{"role": "user", "content": "What's the weather in Paris?"}
],
tools=tools,
)
print(response.choices[0].message.tool_calls)
```