Need English-only (or minimal multilingual) 2B-4B LLM for Agentic AI on GTX 1660 Super (6GB VRAM) – quantization friendly

Hey azhak1, I feel your VRAM pain.

We’ve been running agentic workflows on 6GB laptop GPUs (RTX 3050/4050), and you are 100% right about the multilingual overhead. When you only have 3B parameters, wasting them on 50 languages is a crime against reasoning.

For your specific hardware (GTX 1660 Super) and 6GB limit, forget about Llama 3.2-3B for complex agents—it’s too ‘diluted’. Here are 3 specific recommendations that punch way above their weight in English reasoning:

Mistral-7B-v0.3 (Quantized to IQ3_M or Q4_K_S): > Wait, I know you asked for 2B-4B, but hear me out. Mistral 7B is primarily English-focused. Using GGUF (llama.cpp) with a high-compression bit (like IQ3_XS), you can fit the weights into ~3.5GB VRAM. This leaves 2.5GB for a decent 8k-16k context. It remains the gold standard for English instruction-following compared to any 3B model.

Phi-3.5-mini-instruct (3.8B):

While technically multilingual, Microsoft heavily optimized its reasoning for English. It is arguably the most ‘intelligent’ model under 4B parameters. In 4-bit (GGUF or EXL2), it fits comfortably into 6GB VRAM and handles tool-use much better than SmolLM or Llama 3.2.

StableLM-Zephyr-3B:

This is an older but extremely focused English-only model. It’s very ‘punchy’ for short agentic tasks. It follows instructions with less ‘chatty’ fluff, which saves tokens and compute time on your Turing card.

Pro-Tip for 6GB VRAM:

Use KVP-Cache Quantization (Q4_0 or Q8_0 cache). On a GTX 1660, your bottleneck isn’t just the model size, but the context growing and eating the last megabytes of VRAM. Reducing the cache precision will give you that extra 500MB of breathing room you need for agent logic.

Stay localized, stay fast.