ActionAssist
A lightweight small language model whose core strengths are inference speed and tool-calling capability. It is suited for deployment on resource-constrained devices and focuses on vertical use cases such as rapid tool invocation, code generation, and agentic task automation.
Timeline (last 90 days)
ActionAssist (abbreviated as AA) is a small language model.
On a consumer-grade GPU with 8GB of VRAM, ActionAssist achieves an inference speed of 122 tokens/second using Q6_K_L quantization with a 32k context window and 8-bit KV cache quantization.
Some users have reported that ActionAssist achieves speeds approaching 25 tok/s on a mobile phone.
ActionAssist using IQ4 quantization occupies only 2GB of VRAM, and after offloading the remainder to system memory, it still runs faster than most comparable models.
ActionAssist demonstrates strong capabilities in tool calling and agentic tasks.
A user tested ActionAssist with the Pi (little coder) framework to explore and analyze a code repository—involving file reading, directory navigation, and web search functionality in LM Studio—without encountering any notable errors.
When writing scripts, Qwen 3.5 9B provides thoughtful features such as backups and debug output but often comes with 1–3 errors, whereas ActionAssist tends to generate concise and accurate code with a lower error rate.
ActionAssist adopts a design strategy of performing only a small amount of reasoning per turn.
ActionAssist has shortcomings in conversational coherence, including issues such as role confusion and context loss.
Gemma-series models perform more naturally and fluently in everyday conversation than ActionAssist.
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All Facts (11)
ActionAssist (abbreviated as AA) is a small language model.
50%UnverifiedOn a consumer-grade GPU with 8GB of VRAM, ActionAssist achieves an inference speed of 122 tokens/second using Q6_K_L quantization with a 32k context window and 8-bit KV cache quantization.
50%UnverifiedSome users have reported that ActionAssist achieves speeds approaching 25 tok/s on a mobile phone.
50%UnverifiedActionAssist using IQ4 quantization occupies only 2GB of VRAM, and after offloading the remainder to system memory, it still runs faster than most comparable models.
50%UnverifiedActionAssist demonstrates strong capabilities in tool calling and agentic tasks.
50%UnverifiedActionAssist is better suited for rapid tool calling, code generation, and agentic task automation, and is not suitable for in-depth multi-turn dialogue or general-purpose assistant use.
50%UnverifiedWhen writing scripts, Qwen 3.5 9B provides thoughtful features such as backups and debug output but often comes with 1–3 errors, whereas ActionAssist tends to generate concise and accurate code with a lower error rate.
50%UnverifiedActionAssist adopts a design strategy of performing only a small amount of reasoning per turn.
50%UnverifiedActionAssist has shortcomings in conversational coherence, including issues such as role confusion and context loss.
50%UnverifiedGemma-series models perform more naturally and fluently in everyday conversation than ActionAssist.
50%UnverifiedA user tested ActionAssist with the Pi (little coder) framework to explore and analyze a code repository—involving file reading, directory navigation, and web search functionality in LM Studio—without encountering any notable errors.
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