AI tool comparison
AMUX vs DeepGEMM
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
AMUX
Run dozens of parallel AI coding agents unattended via tmux
75%
Panel ship
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Community
Paid
Entry
AMUX is an open-source agent multiplexer that lets you run dozens of Claude Code (or other terminal AI coding agents) simultaneously, all managed from a single web dashboard — no complicated setup required. Built by the team at Mixpeek, it requires only Python 3 and tmux, with the entire server delivered as a single ~23,000-line Python file with embedded HTML/CSS/JS. The standout features are a self-healing watchdog that auto-compacts context when it drops below 20% and restarts stuck sessions, a SQLite-backed kanban board where agents atomically claim tasks to prevent duplicate work, and a REST API injected at startup that allows agents to coordinate with each other via simple curl calls. There's even a mobile PWA with offline support via Background Sync so you can monitor your agent army from your phone. In the "agentmaxxing" era, AMUX is the most complete open-source solution for running parallel AI coding agents unattended. Rather than babysitting one agent, you dispatch 5–20 agents to isolated worktrees and check back in as a reviewer. The MIT + Commons Clause license means it's free to self-host.
Developer Tools
DeepGEMM
DeepSeek's FP8 GEMM kernels hit 1,550 TFLOPS on H100 — no CUDA install needed
50%
Panel ship
—
Community
Free
Entry
DeepGEMM is DeepSeek's open-source library of highly optimized FP8 General Matrix Multiplication (GEMM) kernels targeting NVIDIA SM90/SM100 GPUs — the H100, H800, and Blackwell class. The headline feature is a lightweight just-in-time (JIT) compiler that eliminates the need for offline CUDA compilation at install time, dramatically lowering the barrier for teams who want raw GPU throughput without complex build pipelines. The library covers FP8 and FP4 dense GEMMs, BF16 accumulation, grouped GEMMs for Mixture-of-Experts architectures with overlapped NVLink communication, and multi-query attention scoring kernels. On H800 hardware DeepGEMM posts up to 1,550 TFLOPS — competitive with hand-tuned vendor libraries — while remaining fully open source under the MIT license. For LLM inference teams running on H100/H800 clusters, DeepGEMM slots directly into inference stacks like vLLM and SGLang. It's especially notable because it came from DeepSeek's internal training infrastructure, meaning it's been battle-tested at the scale that produced some of 2026's most cost-efficient models. This isn't research code — it's production tooling going public.
Reviewer scorecard
“This is exactly what the agentmaxxing workflow needs. Single Python file, no external services, and the kanban board preventing duplicate agent work is genuinely clever engineering. The self-healing watchdog alone saves hours of babysitting stuck sessions.”
“If you're running inference on H100s or H800s, DeepGEMM is an immediate drop-in for the hottest path in your stack. The JIT approach means you're not fighting CUDA version mismatches, and 1,550 TFLOPS is a number that makes you pay attention. Already integrates with vLLM — just use it.”
“MIT + Commons Clause isn't really open source in the traditional sense — you can't build a commercial product on top of it. Also, coordinating 20+ agents that all share Claude Code rate limits means you'll hit API throttling walls faster than you think.”
“This is only useful if you're already running H100/H800 clusters — consumer GPU users get nothing here. Documentation is still thin in places, and support for anything below SM90 is explicitly not a priority. Great for DeepSeek's own infra needs; might be too narrow for most teams.”
“We're moving from one developer + one agent to one developer + agent swarm. AMUX is early infrastructure for that paradigm shift. The agent-to-agent coordination REST API hints at genuine multi-agent systems emerging from terminal tooling.”
“DeepSeek consistently publishes its internal tooling and each release raises the efficiency ceiling for the whole industry. DeepGEMM is another piece of the puzzle that makes frontier inference cheaper — which ultimately benefits everyone downstream from model providers to end users.”
“The web dashboard with live terminal peeking is surprisingly polished for a side project. Being able to monitor your agent army from a mobile PWA while away from the desk is a genuinely practical touch.”
“Far outside the creative tooling space but the downstream effect matters: faster, cheaper inference means the models powering creative AI tools get cheaper to run. Not something a designer touches directly, but the efficiency wins flow through to them eventually.”
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