Agent-Skills.md

Agent Skills: Serve Config Guide

Generate a source-backed starting `trtllm-serve --config` YAML for basic

UncategorizedID: autohandai/community-skills/nvidia-tensorrt-llm-trtllm-serve-config-guide

Repository

autohandai/community-skills
autohandaiLicense: Apache-2.0
103

Install this agent skill to your local

pnpm dlx add-skill https://github.com/autohandai/community-skills/tree/HEAD/nvidia-tensorrt-llm-trtllm-serve-config-guide

Skill Files

Browse the full folder contents for nvidia-tensorrt-llm-trtllm-serve-config-guide.

Download Skill

Loading file tree…

nvidia-tensorrt-llm-trtllm-serve-config-guide/SKILL.md

Skill Metadata

Name
nvidia-tensorrt-llm-trtllm-serve-config-guide
Description
Generate a source-backed starting `trtllm-serve --config` YAML for basic

Serve Config Guide

Scope: aggregate/IFB (in-flight batching) colocated prefill+decode, single node, PyTorch backend, non-speculative by default; DeepSeek-R1 MTP is the standard mode (all checked-in configs include it).

Input: model, GPU, ISL (input sequence length), OSL (output sequence length), concurrency, TP, performance objective (Min Latency | Balanced | Max Throughput | unspecified). Output: repo-grounded starting YAML for trtllm-serve --config.

If the request is adjacent but out of scope, provide a best-effort answer using the nearest in-scope config as a starting point, clearly label inferred vs. verified fields, and point to the relevant feature doc in docs/source/features/ (e.g., speculative-decoding, disagg-serving, parallel-strategy) or examples/llm-api/.

Constraints

  1. Speculative exclusion: Exclude configs containing speculative_config by default. Exception: exact checked-in DeepSeek-R1 MTP configs (models with decoding_type: MTP in examples/configs/). When including MTP, copy the full speculative_config block verbatim — never interpolate speculative fields.

  2. Objective preservation: Preserve the user's stated objective through config selection. Use database.py profile labels (Min Latency, Balanced, Max Throughput; plus Low Latency/High Throughput in smaller sets) as selection aids. If a config is unlabeled, treat it as a default starting point — do not claim it matches a specific objective. If the only match conflicts with the stated objective, call out the mismatch.

  3. Source preference: Prefer checked-in configs over interpolation. When docs and configs disagree, prefer the config for the exact scenario and note the mismatch. Mark any interpolation as unverified.

Response Format

For exact matches: Config → Source → Launch command

For interpolated configs: Config → Source used as starting point → What to benchmark (single list of knobs worth sweeping, not per-field unverified tags)

Step 0: Lock Objective and Decode Mode

Identify the user's objective (Min Latency | Balanced | Max Throughput | unspecified) and decode mode (non-speculative or DeepSeek-R1 MTP per Constraint 1). Preserve both through the remaining steps.

Step 1: Exact Database Match

Search examples/configs/database/lookup.yaml for an exact (model, gpu, isl, osl, concurrency, num_gpus) match. Use database.py as a loader/helper.

  • Apply speculative exclusion.
  • When multiple recipes exist at different concurrency points, use profile labels to match the user's objective per objective preservation.
  • Prefer an exact match that also matches the stated objective over manual tuning.

Step 2: Nearest Checked-In Config

If no exact match, widen the search to also include examples/configs/curated/lookup.yaml.

Apply the same constraints as Step 1. Additionally:

  • A partial match from database/ is preferred over a partial match from curated/ for the same model (database configs are benchmark-tuned).
  • Exclude disaggregated-only or prefill-only entries (e.g., qwen3-disagg-prefill.yaml).
  • For curated configs, only treat intent as explicit when the repo labels it (e.g., *-latency.yaml, *-throughput.yaml, or guide text).
  • If no in-scope config matches the stated objective, pick the nearest same-model starting point and call out the mismatch.

Step 3: Read Model Docs

Search docs/source/deployment-guide/ and examples/models/core/ for the model's deployment guide and README. Read both before adjusting knobs.

Excluded sources: Do NOT use docs/source/legacy/ tuning values or benchmark numbers — those were measured on the TensorRT engine-building backend and do not transfer to PyTorch backend serving.

DeepSeek-V3 caveat: For DeepSeek-V3/V3.2-Exp, use examples/models/core/deepseek_v3/README.md, not the R1 deployment guide.

Step 4: Adjust Source-Backed Fields

Commonly scenario-dependent fields (adjust only these, guided by the checked-in source):

max_batch_size, max_num_tokens, max_seq_len, enable_attention_dp, attention_dp_config.*, kv_cache_config.free_gpu_memory_fraction, moe_expert_parallel_size (MoE), moe_config.backend (when guide specifies), stream_interval, num_postprocess_workers, cuda_graph_config.max_batch_size/batch_sizes, and MTP-specific fields when using DeepSeek-R1 MTP configs.

Do not assume other fields are constant across models/GPUs. For tuning notes, read references/knob-heuristics.md.

Validation Checklist

  • [ ] trust_remote_code: true called out as trust boundary when present
  • [ ] max_num_tokens >= ISL + chat template overhead (requests rejected if violated)
  • [ ] If interpolated: single "What to benchmark" section listing knobs to sweep, not per-field unverified tags