Ollama Turbo

Original Article ↗ Hacker News Discussion ↗

Partnership, branding, and “local” identity

  • Launch is seen as coordinated with OpenAI via the gpt-oss models; some view this as OpenAI “oss-washing” using Ollama’s reputation.
  • Several commenters are surprised Ollama is an independent, VC-backed company and not part of Meta; some say learning this improves their opinion.
  • Concern that “Ollama” had become synonymous with local, offline use, and this move shifts focus toward being a conventional cloud provider.

Open source, governance, and investor influence

  • Multiple comments argue the real issue is governance, not “open source” per se: without independent foundations, companies can later relicense or restrict (Redis, Elastic, Mongo cited).
  • Ollama is praised for MIT-licensed server code but criticized for being controlled by a single VC-backed company, making long‑term direction and licensing uncertain.
  • Some say investor funding made this kind of monetization inevitable and that people should have expected it.

llama.cpp / ggml attribution and engine debate

  • Strong sentiment that the llama.cpp/ggml author “brought LLMs to the masses” and deserves far more credit and money.
  • Dispute over how much Ollama is “just a wrapper”:
    • Ollama team says they now have their own engine, using ggml for tensors and llama.cpp only for legacy models.
    • Critics reply that ggml is effectively the core of llama.cpp, that differences are small, and accuse Ollama of minimizing this dependence and “gaslighting.”
  • Some users are leaving Ollama for llama.cpp + llama-server, saying it now matches or exceeds Ollama’s usability.

Value proposition and pricing of Turbo

  • $20/month flat fee is compared to ChatGPT/Claude; many want cheaper or purely usage-based options and dislike unspecified “hourly and daily limits.”
  • Supporters see value in:
    • Easy way to test big open models without buying GPUs.
    • A simple, unified local/cloud dev story.
  • Skeptics question why pay $20 for quantized open models when state-of-the-art proprietary models cost the same or less via usage-based APIs.

Privacy, jurisdiction, and data handling

  • “Privacy first” marketing is viewed as under-specified; lack of detailed policies and closed-source desktop app reduce trust.
  • Some see no privacy advantage over other US-based providers; others would pay more for EU/Swiss hosting.
  • Debate over whether US jurisdiction is safer or more dangerous than EU/China; consensus only that local remains best for sensitive data.

Local vs cloud; production vs hobby use

  • Many still see Ollama as an excellent on-ramp: install, download models, and go—especially for less technical users.
  • Some argue it’s mainly a “toy” for individuals, with vLLM/SGLang/Bedrock/Vertex preferred for serious deployments; others say Ollama has benchmarked competitively and can be used in production in constrained environments.
  • Frustration that features like sharded GGUF and Vulkan support lag, with an old Vulkan PR cited as evidence of neglected community contributions.

Community reaction and “enshittification” fears

  • A noticeable split:
    • One camp is angry or wary, seeing a familiar pattern of VC-backed OSS turning into a locked-in, monetized platform (Docker Desktop cited).
    • Another camp defends Ollama: Turbo is optional, core remains open, and projects need revenue to survive; paying for GPUs is framed as fair.
  • Several expect more open, purely local alternatives (llama.cpp, sglang, ramalama, etc.) to benefit if Ollama drifts toward a conventional SaaS model.