litmus-lab 0.3.0

A CLI benchmarking framework for LLM inference across FP16/INT8/INT4/HQQ/Quanto/AWQ/GPTQ quantization

⚗️ litmus-lab

Benchmark your LLM across quantization formats and backends on your own GPU.
Get one verdict — which precision, which backend, what it costs.

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What it does

litmus-lab runs your model through HuggingFace (FP16, INT8, INT4) and vLLM in isolated passes — measuring VRAM, throughput, latency and perplexity on your actual hardware — then outputs a single deployment recommendation.

┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Mode            ┃ VRAM (MB) ┃ TPS      ┃ TTFT     ┃ Perplexity ┃
┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━┩
│ HF · FP16       │ 7297.83   │ 32.69    │ 0.030s   │ 5.64       │
│ HF · INT8       │ 3846.47   │ 14.26    │ 0.083s   │ 5.81       │
│ HF · INT4 (NF4) │ 2334.96   │ 25.98    │ 0.069s   │ 7.34       │
│ vLLM · FP16     │ 12687.31  │ 111.68   │ 0.448s   │ 5.65       │
└─────────────────┴───────────┴──────────┴──────────┴────────────┘

  Recommendation  Deploy vLLM · FP16
                  3.4× faster than HF · PPL delta 0.01
                  Memory-constrained? HF · INT4 saves 4963 MB (PPL +1.70)

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Installation

# HF Transformers only (FP16, INT8, INT4)
pip install litmus-lab

# HF + vLLM backend
pip install "litmus-lab[vllm]"

# HF + AI recommendations via Groq
pip install "litmus-lab[ai]"

# Everything
pip install "litmus-lab[all]"

Note: vLLM requires Linux or WSL2. It does not run on Windows natively.

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Quick start

litmus-lab \
  --model microsoft/Phi-3-mini-4k-instruct \
  --prompt "Explain the transformer architecture" \
  --backend all

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Usage

Flags

Flag	Description	Default
--model	HuggingFace model repo ID	required
--prompt	Input prompt	required
--backend	hf · vllm · all	hf
--token	HuggingFace token for gated models	None
--version	Print version and exit	—

Backend modes

Mode	What runs
hf	HF Transformers · FP16, INT8, INT4 (NF4)
vllm	vLLM · FP16 with PagedAttention
all	All four passes in sequence

Examples

# Compare quantization formats
litmus-lab \
  --model Qwen/Qwen2.5-7B-Instruct \
  --prompt "Explain quantum gravity" \
  --backend hf

# vLLM only
litmus-lab \
  --model mistralai/Mistral-7B-Instruct-v0.3 \
  --prompt "Write a Linux shell script" \
  --backend vllm

# Full benchmark — HF + vLLM side by side
litmus-lab \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --token hf_xxxxxxx \
  --prompt "Explain TCP congestion control" \
  --backend all

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AI Recommendations

Set a Groq API key (free tier) to get an AI-powered verdict from llama-3.3-70b-versatile instead of the built-in heuristic:

export GROQ_API_KEY=gsk_...
litmus-lab --model Qwen/Qwen2.5-7B --prompt "Hello" --backend all

No extra flags needed. Falls back to the offline heuristic automatically if the key is missing, rate limit is hit, or there is no internet.

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Metrics explained

Metric	What it measures
VRAM (MB)	Peak GPU memory — model weights + KV cache, measured as a before/after delta
TPS	Tokens generated per second — generation throughput
TTFT	Time to first token — how quickly the model starts responding
Perplexity	Language quality on WikiText-2. Lower = better. Delta >2.0 vs FP16 signals degradation

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Supported models

✅ HF backend — FP16 · INT8 · INT4

Any HuggingFace causal language model loadable via AutoModelForCausalLM:

Family	Models
Meta	Llama 3, 3.1, 3.2, 3.3
Microsoft	Phi-3, Phi-3.5, Phi-4
Alibaba	Qwen2, Qwen2.5
Mistral AI	Mistral 7B, Mixtral 8x7B
Google	Gemma, Gemma 2
DeepSeek	DeepSeek-V2, DeepSeek-V3, DeepSeek-R1
Community	Falcon, Yi, TinyLlama, Vicuna, Zephyr, WizardLM, and most fine-tunes

Gated models (Llama, Gemma) require --token.

✅ vLLM backend — FP16

Same model coverage as HF. Runs FP16 only. VRAM is higher than HF FP16 due to the KV cache pool but throughput is significantly better under concurrent load.

❌ Not supported

	Reason
BERT, RoBERTa, encoder-only models	No autoregressive generation
T5, BART, mT5, seq2seq models	Different generation API
Models that exceed GPU VRAM at FP16	No CPU offloading yet
vLLM on Windows	Use Linux or WSL2
Multi-GPU / tensor parallel	Not yet implemented

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Requirements

• Python 3.10+
• CUDA-capable NVIDIA GPU (CPU works but is very slow)
• CUDA 11.8+ or 12.x

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What's being built 🚧

Feature	Status
GGUF / llama.cpp backend (CPU + Mac M-series)	🔨 In progress
Cost prediction (--target-users, --gpu-cost)	🔨 In progress
Concurrency benchmarking (--concurrency 1,4,8,16,32)	📋 Planned
Multi-model comparison in one run	📋 Planned
JSON / HTML report export	📋 Planned
Multi-GPU / tensor parallel benchmarking	📋 Planned
AWQ / GPTQ quantization on vLLM backend	📋 Planned

Want to shape the roadmap? Open an issue or join the waitlist for the web platform.

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License

MIT