hammerio 1.0.5

GPU-accelerated compression for edge AI. GPU where it matters. CPU where it doesn't.

HammerIO

GPU-accelerated compression for edge AI. GPU where it matters. CPU where it doesn't.
Created by ResilientMind AI | ResilientMindai.com | Joseph C McGinty Jr

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HammerIO is a GPU-accelerated compression toolkit that automatically routes files to the fastest available hardware. Large files go to nvCOMP GPU LZ4 (6+ GB/s decompression). On macOS, it uses Apple LZFSE via the Accelerate framework. Everything else goes to CPU zstd with parallel threading. No flags, no configuration.

Installation

pip install hammerio

GPU acceleration (NVIDIA Jetson or desktop GPU):

pip install hammerio[gpu]

Jetson with jtop monitoring:

pip install hammerio[jetson]

From source:

git clone https://github.com/Subzero121800/HammerIO.git
cd HammerIO && pip install -e .

Requires Python 3.10+. GPU features require CUDA 12.x and NVIDIA nvCOMP.

Post-Install Setup

After installing with pip, run these optional setup commands:

# Accept the license agreement (shown automatically on first run)
hammer --accept-license

# Install right-click compress/decompress in your file manager
hammer install-desktop

# Launch the web dashboard (GPU monitoring, file browser, compression UI)
hammer webui

Quick Start

# Compress anything -- routing is automatic
hammer compress data.csv
hammer compress ./dataset/ --quality fast
hammer compress archive.tar --algo lz4

# Decompress
hammer decompress data.csv.zst
hammer decompress archive.tar.lz4

# Batch compress a directory
hammer batch ./logs/ --workers 8

# See what HammerIO would do
hammer info --routes ./my_file.csv

# Hardware profile
hammer info --hardware

# Install right-click context menu (Nautilus, Nemo, Thunar)
hammer install-desktop

Python API:

import hammerio

router = hammerio.JobRouter(quality="fast")
job = router.route("dataset.csv")
result = router.execute(job)
print(f"{result.compression_ratio:.1f}x via {result.processor_used}")

Benchmarks

Measured on Jetson AGX Orin 64GB, JetPack 6.2.2, CUDA 12.6, MAXN mode. Realistic mixed data (25% CSV, 25% patterns, 25% semi-random, 25% true entropy).

Roundtrip Performance (10 GB)

Method	Compress	Decompress	Ratio	Roundtrip
CPU zstd-1	1,094 MB/s	733 MB/s	2.00x	24.1s
CPU zstd-3	1,014 MB/s	741 MB/s	2.00x	24.0s
GPU nvCOMP LZ4	517 MB/s	4,258 MB/s	1.98x	22.2s

GPU wins the roundtrip: 1.1x faster than CPU best. GPU decompression is 5.7x faster than CPU.

In-Memory (Pure Algorithm Speed, No Disk I/O)

Method	Compress	Decompress	Ratio
CPU zstd-1	1,747 MB/s	2,001 MB/s	2.00x
CPU zstd-3	1,615 MB/s	2,088 MB/s	2.00x
GPU nvCOMP LZ4	705 MB/s	8,537 MB/s	1.98x
GPU nvCOMP Snappy	1,615 MB/s	5,756 MB/s	1.68x

Random I/O (64KB Blocks)

Pattern	IOPS	Bandwidth	Latency p99
Random Read	109,299	6,831 MB/s	0.02 ms
Random Write	29,405	1,838 MB/s	0.05 ms
Mixed 70/30 R/W	57,146	3,572 MB/s	0.05 ms

Scalability (GPU vs CPU Crossover)

Size	CPU zstd-3	GPU LZ4	GPU Wins?
1 MB	260 MB/s	29 MB/s	No
10 MB	341 MB/s	378 MB/s	Yes
100 MB	678 MB/s	693 MB/s	Yes
1 GB	380 MB/s	464 MB/s	Yes

GPU crossover at ~10 MB. Below that, kernel launch overhead dominates.

Run benchmarks yourself:

hammer benchmark              # Standard (500MB)
hammer benchmark --quick      # Quick (100MB)
hammer benchmark --10gb       # Stress test (10GB)
hammer benchmark --type memory     # In-memory only
hammer benchmark --type random-io  # Random I/O patterns
hammer benchmark --type scale      # Scalability sweep

Smart Routing

HammerIO profiles every file and routes to the optimal compressor:

$ hammer info --hardware

Routing Profile:
  Large Files   -> nvCOMP LZ4 (GPU)      # Files > 500MB
  Datasets      -> nvCOMP LZ4 (GPU)      # CSV, Parquet, NPY, etc.
  General       -> zstd parallel (CPU)    # Default path
  Archives      -> passthrough            # Already compressed
  Text Logs     -> zstd (CPU, high ratio) # Best ratio

On macOS Apple Silicon: General -> Apple LZFSE (Accelerate) -- hardware-optimized, faster than zstd on M-series.

If the GPU path fails (OOM, driver issue), HammerIO falls back to CPU and logs why.

Watch Daemon

Drop-folder automation for edge pipelines:

hammer watch --watch-root ./pipeline --threshold-mb 500

# ./pipeline/
#   compress/        <- drop files here
#   decompress/      <- drop .zst/.lz4 files here
#   compressed/      <- output appears here
#   decompressed/    <- output appears here
#   processed/       <- originals moved here after success

GPU routing kicks in for files above the threshold. CPU handles the rest.

Web Dashboard

hammer webui             # http://localhost:5000

Real-time monitoring: GPU/CPU utilization, thermal zones, power rails, per-core CPU bars, compression job history, file browser, quick compress/decompress, and a built-in CLI console.

Hardware Compatibility

Device	Compression Engine	Status
Jetson AGX Orin	nvCOMP GPU LZ4	Primary target, fully tested
Jetson Orin NX / Nano	nvCOMP GPU LZ4	Supported
RTX 3000 / 4000 / 5000	nvCOMP GPU LZ4	Supported
Any CUDA 12.x GPU	nvCOMP GPU LZ4	Supported
macOS Apple Silicon	Apple LZFSE (Accelerate)	M1/M2/M3/M4
CPU-only (no GPU)	zstd / gzip / lz4	Universal fallback

CLI Reference

Command	Description
hammer compress	Compress file or directory (auto GPU/CPU routing)
hammer decompress	Decompress .zst, .lz4, .lzfse, .gz, .bz2 files
hammer batch	Batch compress a directory with parallel workers
hammer watch	Watch folders and auto-process dropped files
hammer benchmark	Run GPU vs CPU benchmark suite
hammer install-desktop	Install right-click compress/decompress in file manager
hammer info	Hardware profile, routing decisions, telemetry
hammer config	Show, save, or generate configuration
hammer monitor	Live terminal telemetry (jtop-style)
hammer webui	Launch web dashboard
hammer version	Version and system info

License

Copyright 2026 ResilientMind AI | ResilientMindai.com | Joseph C McGinty Jr
Licensed under the Apache License, Version 2.0

Open source for personal, educational, research, and internal business use.

Commercial licenses available for redistribution, SaaS, and OEM embedding.

License	Price
Individual commercial	$199 one-time
Organization	$999/year
OEM / Embedded	Custom

See COMMERCIAL_LICENSE.md for details.

Contact: [e-mail] | resilientmindai.com

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ResilientMind AI | resilientmindai.com | Joseph C McGinty Jr