AI tool comparison
Baton 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
Baton
Run multiple AI coding agents in parallel, each in isolated git worktrees
75%
Panel ship
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Community
Free
Entry
Baton is a native desktop orchestration tool for running multiple AI coding agents in parallel — each in its own isolated git worktree. Built for developers who want to run Claude Code, Gemini CLI, or OpenAI Codex CLI simultaneously without agents overwriting each other's work. The key insight is elegant: git worktrees let you check out the same repo to multiple directories, each on its own branch. Baton makes this trivial — auto-generating branch names and workspace titles with AI, surfacing notification badges when agents finish or hit errors, and letting you toggle "Accept Edits" mode per workspace independently. At $49 one-time with no subscription, Baton is aimed squarely at developers who find single-agent coding frustrating and want to run multiple tasks concurrently. The free tier caps at 4 concurrent workspaces. It's available for Mac, Windows, and Linux.
Developer Tools
DeepGEMM
DeepSeek's FP8 GEMM kernels hit 1,550 TFLOPS on H100 — no CUDA install needed
50%
Panel ship
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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 the workflow tool I didn't know I needed. Running three Claude Code instances on different features simultaneously, each in isolation, feels like having a real team. The worktree isolation means no constant merge conflicts — and getting notified when agents finish is genuinely delightful.”
“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.”
“It's a GUI wrapper around git worktrees and process management — most of what Baton does can be scripted in bash in an afternoon. The $49 price is reasonable but the moat is thin. Expect this to become a built-in feature of Cursor or Windsurf within a release cycle.”
“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.”
“Parallel agent orchestration at the desktop level is the first step toward autonomous software teams. Baton is primitive, but the pattern it establishes — isolated worktrees, parallel execution, async notification — is exactly how future dev environments will work. Get comfortable with the paradigm now.”
“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.”
“For non-developers using AI coding tools, Baton removes a lot of the confusion about why agents interfere with each other. The UX is clean enough that even designers who occasionally vibe-code can manage multiple tasks at once without losing their minds.”
“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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