What is TurboQuant and does it run 70B models on MacBook?

TurboQuant compresses KV cache during inference, not weights. 70B still needs ~35GB quantized weights. But long contexts (32k+) become feasible without OOM.

How do I install TurboQuant stack on Apple Silicon MacBook?

Clone the repo, run `bash install.sh`. Starts Ollama, MLX sidecar, routing proxy. Point tools to localhost:8000.

Is TurboQuant better than just using Ollama with quantization?

Yes for long prompts—KV savings stack on top. Short chats? Ollama alone wins on simplicity. Word count: ~950. Repo link in comments (shameless plug). Try it. Mock it later.

🤖 AI & Machine Learning

TurboQuant on a MacBook: The KV Cache Killer You've Been Ignoring

KV cache on a 70B model at 32k tokens? That's 40GB+ in FP16, dooming your MacBook. TurboQuant compresses it ruthlessly—without touching model quality.

theAIcatchup Apr 09, 2026 3 min read

Diagram of TurboQuant routing proxy stack connecting client to Ollama and MLX sidecar on MacBook

⚡ Key Takeaways

TurboQuant targets KV cache bloat, enabling long-context work on MacBooks. 𝕏
One-command install builds Ollama + MLX sidecar with smart routing proxy. 𝕏
Skeptical of 'bigger models' hype—pair with weight quant for real gains. 𝕏

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theAIcatchup

Community-driven. Code-first.

#Apple Silicon #KV cache #MLX #MLX Apple Silicon #Ollama #TurboQuant #local LLM stack #local LLMs

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Originally reported by Dev.to

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