Configuration reference

This reference covers the full Truss config.yaml schema for BIS-LLM (Baseten Inference Stack v2). The v2 stack simplifies the build: section and moves runtime fields out of build. For translating an Engine-Builder-LLM (v1) configuration to BIS-LLM, see Migrate from Engine-Builder-LLM.

Configuration structure

trt_llm:
  inference_stack: v2  # Always v2 for BIS-LLM
  build:
    checkpoint_repository: {...}
    quantization_type: no_quant | fp8 | fp8_kv | fp4 | fp4_kv | fp4_mlp_only
    quantization_config: {...}
    num_builder_gpus: 1
    skip_build_result: false
  runtime:
    max_seq_len: 32768
    max_batch_size: 256
    max_num_tokens: 8192
    tensor_parallel_size: 1
    enable_chunked_prefill: true
    served_model_name: "model-name"
    patch_kwargs: {...}

Build configuration

The build section configures model compilation and optimization settings.

checkpoint_repository

object

required

Specifies where to find the model checkpoint. Same structure as v1 with v2-specific optimizations.For training checkpoint deployment, see Deploy with optimized inference engines. For cloud storage sources (GCS, S3, Azure), see Deploy from cloud storage.

checkpoint_repository:
  source: HF | GCS | S3 | AZURE | REMOTE_URL | BASETEN_TRAINING
  repo: "model-repository-name"
  revision: main  # Optional, only for HF
  runtime_secret_name: hf_access_token  # Optional, for private repos

quantization_type

string

Quantization format for model weights (simplified from v1).Options:

no_quant: precision of the repo (fp16 or bf16). BIS-LLM also supports quantized checkpoints from nvidia-modelopt libraries.
fp8: FP8 weights + 16-bit KV cache
fp8_kv: FP8 weights + FP8 KV cache
fp4: FP4 weights + 16-bit KV cache (B200 only)
fp4_kv: FP4 weights + FP8 KV cache (B200 only)
fp4_mlp_only: FP4 MLP layers only + 16-bit KV cache (B200 only)

For detailed quantization guidance including hardware requirements, calibration strategies, and model-specific recommendations, see Quantization guide.

build:
  quantization_type: fp8

quantization_config

object

Configuration for post-training quantization calibration.

quantization_config:
  calib_size: 1024
  calib_dataset: "abisee/cnn_dailymail"
  calib_max_seq_length: 2048

num_builder_gpus

number

default:"1"

Number of GPUs to use during the build process. Auto-detected from resources. Minimum: 1, with no fixed maximum.

build:
  num_builder_gpus: 4  # For large models or complex quantization

skip_build_result

boolean

default:"false"

Skip the engine build step and use a pre-built model that does not require quantization. Use when you have a pre-built engine from model cache.

build:
  skip_build_result: true

Runtime configuration

The runtime section configures inference engine behavior.

max_seq_len

number

default:"auto-detected from model config"

Maximum sequence length (context) for single requests. Range: 1 to 1048576.

runtime:
  max_seq_len: 131072  # 128K context

max_batch_size

number

default:"256"

Maximum number of input sequences processed concurrently. Range: 1 to 2048.

runtime:
  max_batch_size: 128  # Lower for better latency

max_num_tokens

number

default:"8192"

Maximum number of batched input tokens after padding removal. Range: 65 to 131072.

runtime:
  max_num_tokens: 16384  # Higher for better throughput

tensor_parallel_size

number

default:"1"

Number of GPUs to use for tensor parallelism. Auto-detected from resources. Minimum: 1, with no fixed maximum (set it to the number of GPUs in your accelerator setting).

runtime:
  tensor_parallel_size: 4  # For large models

enable_chunked_prefill

boolean

default:"true"

Enable chunked prefilling for long sequences.

runtime:
  enable_chunked_prefill: true

served_model_name

string

default:"model name from config"

Model name returned in API responses.

runtime:
  served_model_name: "gpt-oss-120b"

patch_kwargs

object

Preview. Pass-through configuration patches for the v2 inference stack. Fields under patch_kwargs may change without notice; keys that overlap standard runtime fields are rejected at build time.

runtime:
  patch_kwargs:
    custom_setting: "value"
    advanced_config:
      nested_setting: true

Complete configuration examples

Qwen3-30B-A3B-Instruct-2507 MoE with FP4 on B200

model_name: Qwen3-30B-A3B-Instruct-2507-FP4
resources:
  accelerator: B200:1
  cpu: '4'
  memory: 40Gi
  use_gpu: true
trt_llm:
  inference_stack: v2
  build:
    checkpoint_repository:
      source: HF
      repo: "Qwen/Qwen3-Coder-30B-A3B-Instruct"
      revision: main
    quantization_type: fp4
    quantization_config:
      calib_size: 2048
      calib_dataset: "abisee/cnn_dailymail"
      calib_max_seq_length: 4096
    num_builder_gpus: 1
  runtime:
    max_seq_len: 65536
    max_batch_size: 256
    max_num_tokens: 8192
    tensor_parallel_size: 1
    enable_chunked_prefill: true
    served_model_name: "Qwen3-30B-A3B-Instruct-2507"

GPT-OSS 120B on B200:1 with no_quant

This example deploys GPT-OSS with default settings. For production throughput with Eagle speculative decoding on B200, see Speculative decoding for BIS-LLM and Advanced features for BIS-LLM.

model_name: gpt-oss-120b-b200
resources:
  accelerator: B200:1
  cpu: '4'
  memory: 40Gi
  use_gpu: true
trt_llm:
  inference_stack: v2
  build:
    checkpoint_repository:
      source: HF
      repo: "openai/gpt-oss-120b"
      revision: main
      runtime_secret_name: hf_access_token
    quantization_type: no_quant
    quantization_config:
      calib_size: 1024
      calib_dataset: "abisee/cnn_dailymail"
      calib_max_seq_length: 2048
  runtime:
    max_seq_len: 131072
    max_batch_size: 256
    max_num_tokens: 16384
    tensor_parallel_size: 1
    enable_chunked_prefill: true
    served_model_name: "gpt-oss-120b"

DeepSeek V3

This example deploys a pre-quantized ModelOpt checkpoint with no_quant. For higher throughput on DeepSeek V3 family models, use multi-GPU B200 layouts with MTP speculative decoding or disaggregated serving. See Speculative decoding for BIS-LLM and Disaggregated serving.

model_name: nvidia/DeepSeek-V3.1-NVFP4
resources:
  accelerator: B200:4
  cpu: '8'
  memory: 80Gi
  use_gpu: true
trt_llm:
  inference_stack: v2
  build:
    checkpoint_repository:
      source: HF
      repo: "nvidia/DeepSeek-V3.1-NVFP4"
      revision: main
      runtime_secret_name: hf_access_token
    quantization_type: no_quant # nvidia/DeepSeek-V3.1-NVFP4 is already modelopt compatible
    quantization_config:
      calib_size: 1024
      calib_dataset: "abisee/cnn_dailymail"
      calib_max_seq_length: 2048
  runtime:
    max_seq_len: 131072
    max_batch_size: 256
    max_num_tokens: 16384
    tensor_parallel_size: 8
    enable_chunked_prefill: true
    served_model_name: "nvidia/DeepSeek-V3.1-NVFP4"

Hardware selection

GPU recommendations for v2:

B200: Best for FP4 quantization and next-gen performance
H100: Best for FP8 quantization and production workloads
Multi-GPU: Required for large MoE models (>30B parameters)

Configuration guidelines:

Model Size	Recommended GPU	Quantization	Tensor Parallel
`<30B` MoE	H100:2-4	FP8	2-4
30-100B MoE	H100:4-8	FP8	4-8
100B+ MoE	B200:4-8	FP4	4-8
Dense >30B	H100:2-4	FP8	2-4

BIS-LLM overview: Main engine documentation.
Migrate from Engine-Builder-LLM: Translate a v1 configuration to BIS-LLM (v2).
Advanced features for BIS-LLM: KV-aware routing, disaggregated serving, and speculative decoding.
Structured outputs for BIS-LLM: JSON schema validation.
Model deployment examples: Concrete deployment examples.

​Configuration structure

​Build configuration

​Runtime configuration

​Complete configuration examples

​Qwen3-30B-A3B-Instruct-2507 MoE with FP4 on B200

​GPT-OSS 120B on B200:1 with no_quant

​DeepSeek V3

​Hardware selection

​Related

Configuration structure

Build configuration

Runtime configuration

Complete configuration examples

Qwen3-30B-A3B-Instruct-2507 MoE with FP4 on B200

GPT-OSS 120B on B200:1 with no_quant

DeepSeek V3

Hardware selection

Related