UC Berkeley just open-sourced FreeToken.
(2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit
Top AI LLM Models for Every Task
Ask Gemini 3.1 Pro FREE now 👉 → Writing & Research: GPT-5.4, Claude 4.6, Gemini 3.1 Pro, Perplexity → Social Content: Grok 4, GPT o3, DeepSeek → Academic / STEM: Claude Opus 4.6, MiniMax M2.7
Top AI LLMs for Every Task
Try Gemini 3.1 Pro FREE on GlobalGPT 👉 → Writing & Research: GPT-5.4, Claude 4.6, Gemini 3.1 Pro, Perplexity → Social Content: Grok 4, GPT o3, DeepSeek → Academic / STEM: Claude Opus 4.6, MiniMax M2.7
DeepSeek V4 Pro is already huge
1.6T total parameters. 49B active per token. 1M-token context. And it uses token-wise compression + sparse attention so it doesn’t need to reread everything constantly. But long agent tasks can still drift.
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