The anti-Ollama essay is five months old — and r/LocalLLaMA just made it a hit again

Late Sunday, a link post titled "Friends Don't Let Friends Use Ollama" went up on r/LocalLLaMA. The essay underneath is not new — it first circulated in mid-April, when it drew roughly 650 points on Hacker News — but the repost by u/rm-rf-rm has pulled in roughly 1,000 points and more than 300 comments in two days. The argument, about how much the most popular local model runner owes the engine it wraps, is older than both. Enough has changed since April, though, that the fight is partly about an Ollama that no longer exists.

An April polemic with a March slogan

The essay, by the blogger Zetaphor on Sleeping Robots, opens by conceding its bias: "This isn't a 'both sides' piece. I've used Ollama. I've moved on." Its thesis is that Ollama rose by making llama.cpp accessible, then spent years obscuring that dependence — skipping license notices, forking the engine into a worse version, locking models into its own registry — before pivoting to cloud under venture-capital pressure.

Even the title is borrowed. The phrase comes from a March 3 r/LocalLLaMA PSA urging users to test new models on llama.cpp or vLLM instead of "frameworks that are basically llama.cpp but worse." The essay turned that slogan into a movement; the September repost made it the subreddit's fight of the week. Replies split into converts, defenders who call it tribalism, and a factual middle the thread keeps rediscovering.

What the record actually shows

The licensing complaint is checkable, and it mostly checks out. GitHub issue #3185, opened March 16, 2024 by jart, the llamafile developer, documented that Ollama's release artifacts contained no MIT copyright notices for llama.cpp. It remains open in September 2026, some 275 reactions and an assignee later. A May 2025 comment counted "411 days have passed since this issue was opened, with no response from the ollama maintainers." The essay's companion citation, issue #3697 requesting README acknowledgment, was opened April 17, 2024 and closed the same day by co-founder Michael Chiang, who added the line "llama.cpp project founded by Georgi Gerganov" with the note: "Thank you! We definitely should do a better job."

That contrast — attribution fixed within hours when asked politely, license compliance left open for years — is the essay's strongest material, and no Ollama representative disputed it in the thread. The rest of the case — DeepSeek-R1 distillates sold as the full model, a closed-source desktop app in July 2025, a Modelfile duplicating GGUF metadata — rests on issues and write-ups cited in the essay: documented, but advocacy with links rather than audit.

The engine story has moved on

The essay's middle act describes Ollama's mid-2025 replacement of llama.cpp with a custom ggml-based backend that, it says, reintroduced bugs llama.cpp had solved years earlier — broken structured output, vision failures, models its fork could not load. Community reports from that era exist, and Georgi Gerganov himself publicly identified bad GGML changes in the fork. The essay then misses the sequel. In late May 2026 Ollama merged PR #16031, whose description states the new architecture plainly: "Remove the vendored GGML and llama.cpp backend, CGO runner, GGML-based Go model implementations... llama-server (built from upstream llama.cpp via FetchContent) is now the sole inference engine for GGUF-based models." Ollama announced the result on June 5, 2026 as version 0.30, crediting llama.cpp for "improved performance and GGUF model compatibility." Commenters noticed: u/Dantzig walked through the merge dates and concluded the essay's engine complaint describes a backend Ollama itself retired.

Measured on GitHub on September 8, 2026:

• ollama/ollama: ~180,500 stars, ~17,800 forks, MIT license
• ggml-org/llama.cpp: ~127,500 stars, ~22,900 forks, ~1,970 contributors, latest release v0.4.0 on September 4, 2026

The wrapper still out-stars the engine that powers it.

Speed claims run ahead of measurements

The performance case is where the debate is loudest and thinnest. The essay cites community tests claiming llama.cpp runs 1.8x faster than Ollama (161 versus 89 tokens per second on one blog's hardware), a 30-50 percent CPU gap, and roughly 70 percent higher throughput on Qwen-3 Coder 32B in a Reddit comparison. None of these are controlled, version-pinned benchmarks, and all predate the May engine re-merge. No published head-to-head compares current Ollama against llama.cpp on identical GGUFs and hardware — the only fair test now that both run the same engine. u/unchikuso pushed back on the slow narrative: "Have you even tried Ollama in the last few months? I consistently get 150+ tok/s running Qwen3.8-27b on my 5090." Others describe the friction that pushed them out: u/Sweet-Transition-787 hit hash-mismatch errors, could not load GGUFs already on disk, and "moved to llama.cpp after that and most of the random issues just disappeared." Both can be true; quantifying the gap fairly requires the controlled test nobody has published. The speed debate is running ahead of the measurements.

The real dispute is governance, not tokens

Strip the benchmarks and what remains is a fight about stewardship. The sharpest comment in the thread, from u/Fratil, makes the values case explicitly:

"Free software is free because open source comes with rules, ollama is explicitly breaking those rules... ollama abstracts too much information and uses proprietary formats in a way that prevents noobs from a solid path to actually understand the hobby." — u/Fratil, r/LocalLLaMA

The governance grievances are verifiable from Ollama's own homepage: the "local-first" pitch has become "use open models with your coding agents," with hosted cloud tiers (Pro at $20 a month), data-region promises, a speed chart against unnamed "Provider A/B/C," and launcher integrations for Claude Code, Codex, OpenCode and others. "Trusted by more than 9M developers," the site claims — a marketing figure with an unambiguous direction. Whether that trajectory is betrayal, natural commercialization, or what happens when the easiest on-ramp to local AI becomes a business is a question of values, not measurements. The thread produced no data on what Ollama's third-party cloud providers do with prompts, and none on how much of the historical performance gap survives the new engine — the gaps a real audit would close. Until then, the slogan is doing the work the benchmarks have not finished — and warnings about incentives can be right even when the numbers cited to support them are stale.