litmus-lab 0.1.3

A CLI benchmarking framework for LLM inference across FP16/INT8/INT4 quantization

litmus-lab

A blazing-fast, zero-dependency local CLI profiler built for benchmarking Large Language Models across different precision formats (Native FP16 vs INT8 vs INT4) directly on your GPU.

litmus-lab measures:

• GPU VRAM consumption
• Tokens per second (TPS)
• Time to first token (TTFT)
• Linguistic degradation (Perplexity)

After profiling, an offline mathematical heuristic engine automatically recommends the best deployment precision for your hardware.

No cloud APIs. No subscriptions. No hallucinated advice.

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Features

Multi-Precision Benchmarking

Profile and compare:

• Native FP16
• INT8 Quantization
• INT4 Quantization (NF4/GPTQ-style)

on the exact same prompt and architecture.

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Offline Recommendation Engine

litmus-lab contains a local rule-based mathematical evaluation engine.

It automatically determines:

• whether quantization is worth it
• whether the VRAM savings justify the quality loss
• if lower precision actually hurts latency or throughput
• if perplexity degradation becomes unsafe

Example:

• Small models may not benefit from quantization
• Some architectures become unstable in 4-bit
• Certain GPUs gain VRAM savings but lose TPS

The engine evaluates all of this locally and outputs a deployment verdict.

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VRAM Isolation & Cleanup

Each profiling worker is completely isolated.

Between benchmark passes, litmus-lab aggressively performs:

• CUDA cache cleanup
• Python garbage collection
• IPC memory clearing
• model unloads
• allocator flushes

This prevents hidden memory leaks and false VRAM readings during sequential quantization tests.

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🛡 Context-Length Protection

Some older transformer architectures crash if generation exceeds positional embedding limits.

litmus-lab automatically:

• reads max_position_embeddings
• scales test sequence lengths safely
• avoids index out-of-bound runtime failures

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Beautiful Terminal Dashboard

All benchmark data is rendered using rich terminal tables for clean visualization directly inside the CLI.

Example:

┏━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Quantization ┃ VRAM (MB) ┃ Tokens/sec(TPS) ┃ Time to first token(TTFT) ┃ Perplexity ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ Native       │ 7540.12   │ 54.2180         │ 0.0120 sec                │ 12.42      │
│ INT8         │ 4210.45   │ 18.9412         │ 0.0540 sec                │ 12.45      │
│ INT4         │ 2840.88   │ 22.1054         │ 0.0610 sec                │ 12.68      │
└──────────────┴───────────┴─────────────────┴───────────────────────────┴━━━━━━━━━━━━┛

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Installation

Install globally from PyPI:

pip install litmus-lab

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Quick Start

Run a full benchmark pass:

litmus-lab --model microsoft/Phi-3-mini-4k-instruct --prompt "Write a short poem on a wall"

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CLI Options

Flag	Description
--model	Hugging Face model repository path
--prompt	Prompt text sent to the inference runner
--token	Optional Hugging Face token for gated models

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Supported Models

Most Hugging Face causal language models are supported, including:

• Phi
• Qwen
• Gemma
• Mistral
• Llama
• OPT
• Falcon
• TinyLlama
• DeepSeek

Examples:

litmus-lab --model Qwen/Qwen2.5-7B-Instruct --prompt "Explain quantum gravity"

litmus-lab --model google/gemma-2-2b-it --prompt "Write a Linux shell script"

litmus-lab --model meta-llama/Llama-3.1-8B-Instruct --token YOUR_TOKEN --prompt "Explain TCP congestion control"

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Example System Evaluation Report

==========================================================================================
SYSTEM EVALUATION REPORT FOR microsoft/Phi-3-mini-4k-instruct:

• Recommendation: Deploy INT4 (NF4 format).

• Reason:
  Reclaims a significant 4699.24 MB of GPU VRAM compared to Native FP16 execution.

  The perplexity delta remains tightly controlled (+0.26 PPL), making INT4 the
  most hardware-efficient deployment format for this architecture size.

==========================================================================================

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How Recommendations Are Calculated

The heuristic engine evaluates:

• VRAM reclaimed
• TPS throughput changes
• TTFT latency penalties
• Perplexity degradation
• architecture stability
• quantization efficiency ratios

The recommendation engine is completely offline and deterministic.

No LLM APIs are used.

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Why This Exists

Most quantization tooling tells you:

"INT4 uses less memory."

But memory reduction alone does not determine deployment quality.

Some quantized models:

• become slower
• lose coherence
• spike TTFT
• destabilize logits
• produce negligible VRAM savings

litmus-lab exists to mathematically determine whether quantization is actually worth deploying on YOUR hardware.

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Example Workflow

# Benchmark a 7B instruct model
litmus-lab \
  --model Qwen/Qwen2.5-7B-Instruct \
  --prompt "Explain transformers in simple terms"

# Benchmark a gated Llama model
litmus-lab \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --token hf_xxxxxxxxx \
  --prompt "Write a memory allocator in C"

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Performance Metrics Explained

VRAM (MB)

Peak GPU memory allocated during inference.

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Tokens/sec (TPS)

Measures generation throughput speed.

Higher is better.

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Time To First Token (TTFT)

Measures inference latency before the first generated token appears.

Lower is better.

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Perplexity (PPL)

Measures language degradation and prediction uncertainty.

Lower is better.

Small increases are acceptable.

Large jumps indicate quantization damage.

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Architecture

litmus-lab internally uses:

• PyTorch
• Transformers
• bitsandbytes
• CUDA memory instrumentation
• isolated worker runners
• rich terminal rendering

while exposing a single lightweight CLI interface.

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Roadmap

Planned future features:

• ONNX Runtime benchmarking
• GGUF profiling
• AWQ/GPTQ support
• AMD ROCm backend
• CPU-only profiling
• tensor parallel profiling
• JSON/CSV export mode
• benchmark history tracking
• automated regression detection

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License

MIT License

Free to use, modify, distribute, and integrate into commercial tooling.

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Contributing

Pull requests, issue reports, architecture improvements, and benchmark contributions are welcome.

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Disclaimer

Benchmark results vary depending on:

• GPU architecture
• CUDA version
• driver versions
• kernel scheduler state
• model architecture
• tokenizer implementation
• quantization backend

Always validate production deployments independently.