deepseekplugins

DeepSeek Harness plugin

dsh-model-deploy

DeepSeek Harness plugin: LLM model selection & deployment analysis (deployability, VRAM, TTFT, latency, throughput, power for 38 models × 20 GPUs/NPUs)

Tools & Capabilities
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Source facts

Repository
lhwwxy/dsh-model-deploy
Latest update
Aug 18, 2026
Category
Tools & Capabilities
GitHub stars
0
Format
plugin
Catalog evidence
Upstream dsh.bundle evidence
Evidence path
package.json#dsh.bundle
Checked against
0.1.0-rc.8
Upstream check date
2026-08-20

This evidence comes from the upstream catalog. This site has not installed, run, or security-reviewed the plugin.

Install

Start with a prompt that asks an agent to review the GitHub repository and source. Switch to the command if you want to install it yourself.

Copy this prompt into DSH, Codex, or another agent and ask it to review the GitHub repository and source first.

Do not install or run any commands yet. Read this plugin's GitHub repository, README, and relevant source code. Then answer the questions below clearly and directly so I can decide whether it fits my needs:

1. What is this plugin, and what problem does it solve?
2. Who is it for, and what are its typical use cases?
3. How is it used after installation? Include one minimal example.
4. What known limitations or privacy, security, compatibility, or maintenance risks does it have?
5. Give a clear recommendation: recommend, conditionally recommend, or do not recommend, with reasons.

Distinguish statements documented by the repository, inferences from source code, and unknowns. If evidence is insufficient, say so explicitly. Do not guess or simply repeat the README.

GitHub: https://github.com/lhwwxy/dsh-model-deploy
Plugin: dsh-model-deploy
Author: lhwwxy

Check the source files

Read the README and other files from this plugin directory before installing.

File explorer4 files
README.en.mdSource · read only
README language

dsh-model-deploy

> 🌐 Language / 语言切换: English · 简体中文

DeepSeek Harness (DSH) plugin for LLM model selection & deployment analysis.

Give it a model, a GPU/NPU, node count, precision, context length, concurrency and interconnect bandwidth — it estimates, with a transparent first-order analytical model:

  • ✅/⚠️/❌ deployability (VRAM fit, precision support, interconnect checks) with fixes
  • memory: per-GPU and cluster breakdown (weights / KV cache / activations / runtime)
  • latency: time-to-first-token and end-to-end latency (idle & loaded)
  • throughput: per-request and aggregate tokens/s, req/s, prefill tokens/s
  • power: idle / typical / peak (PUE-adjusted), energy per token
  • automatic TP × PP × DP strategy selection with reasoning

Install

dsh plugin --profile web add dsh-model-deploy

Or from a GitHub checkout:

dsh plugin --profile web add github:lhwwxy/dsh-model-deploy

> --profile is mandatory. The plugin registers two tools; restart the session > (or the profile) after installing so the tool schemas join prompt assembly.

Tools

model_deploy_analyze

Main tool. Example call:

{
  "model": "deepseek-v3",
  "gpu": "ascend910b",
  "gpusPerNode": 8,
  "nodes": 1,
  "precision": "int4",
  "ctx": 131072,
  "batch": 4
}

Fields: model (catalog id, or a custom-model JSON string), gpu (catalog id),

gpusPerNode (1–8), nodes (1–64), precision (fp32|bf16|fp16|fp8|int8|int4|fp4), ctx / batch / prompt / output (tokens), intraMode (auto|custom) + intraGBs, interMode (none|ib400|ib800|roce100|roce200|custom) + interGBs.

Returns a structured report (status, issues, strategy, memory, performance, power, assumptions); the rendered output is a compact Chinese text report.

model_deploy_catalog

Lists supported models (id, params, layers, context limit) and GPUs/NPUs (id, memory, bandwidth, FP16/FP8/INT8/FP4, interconnect, TDP). Optional query / vendor filters.

Coverage

  • 38 models — Qwen3 family (incl. Qwen3-Next / Coder / VL), Llama 3.1/3.3/4,

DeepSeek-V3/V3.1/V3.2/V4-Flash/V4-Pro/R1, Kimi-K2/-K2-Thinking/-K3, GLM-4.5-Air/4.6/5, Hunyuan-A13B, Baichuan-M2, Seed-OSS, GPT-OSS, Mixtral, MiniCPM4, QwQ, Qwen2.5 …

  • 20 GPUs/NPUs — NVIDIA (H100/H200/H20/A100/B200/L40S/RTX 3090/4090/5090),

AMD MI300X, 华为昇腾 (910B/910C/950PR), 平头哥含光800, 海光 Z100, 昆仑芯 P800, 寒武纪 MLU590, 天数天垓150, 沐曦 C500, 摩尔线程 S5000

Architecture data comes from each model's published config.json (ModelScope/HuggingFace); hardware data from official datasheets and public spec tables. Sources are linked per entry in dsh/data.js. Fields that vendors never published (e.g. Hanguang 800 bandwidth) are marked ~estimated and flagged in reports.

Estimation model (the honest part)

All formulas and coefficients are public — that is what makes the numbers auditable, not magic:

  • weights = params × precision bytes; KV = 2 × layers × KV-heads × head_dim × bytes

(GQA), or (kv_lora_rank + rope) × layers × bytes (MLA), with sliding-window layers prorated (Qwen3-Next)

  • decode step ≈ max((weights + KV) / bandwidth, compute) + TP allreduce

(2·(TP−1)/TP × hidden × batch × 2B over NVLink/HCCS/PCIe/node uplink)

  • TTFT ≈ 2 × active params × prompt tokens / (FLOPs × TP × PP × MFU 0.5) + comm
  • power = TDP × duty ratios + 22% platform overhead, PUE 1.25
  • MoE expert all-to-all ×1.35 correction; PP bubble ×10%/stage

Estimates are validated against real-world anchors (70B BF16 on 8×H100 ≈ 150 tok/s; DeepSeek-V3 FP8 does not fit 8×80GB, INT4 does; Qwen3-8B INT4 on one RTX 4090 ≈ 170 tok/s), plus a NaN-free sweep across all 760 model×GPU combinations.

Non-CUDA NPUs are estimated at datasheet-ideal values; the tool flags that CANN/XPU/MUSA software-stack maturity varies — benchmark before purchase.

Development, testing and release flow (maintainers): see [docs/MAINTAINING.md](./docs/MAINTAINING.md).

License

MIT

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