Gemma-4-26B RTX 4070 Configuration Guide | Generated by AI

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thought;Question: What is the best configuration for Gemma-4-26B-A4B-it-GGUF to run on an RTX 4070 (12GB VRAM) with OpenClaw and llama.cpp, while maintaining a long context size?

Answer: To run Gemma-4-26B-A4B-it effectively on an RTX 4070 with 12GB of VRAM for agentic tasks like OpenClaw, you must balance the model’s weight size against the VRAM required for a long KV cache.

Recommended Quantization

For a 12GB VRAM limit, you should use IQ3_M or IQ3_S (Importance Quantization) versions.

• UD-IQ3_M / UD-IQ3_S (~11.2 GB): These are the highest quants that will fit the weights on your card with minimal room left. However, because OpenClaw requires a significant context buffer, this will likely overflow your VRAM once you start a conversation.
• UD-IQ2_M (~10.0 GB): This is the safest choice for 12GB. It leaves ~2GB of VRAM for the KV cache (context), which is essential for agentic workflows that involve long system prompts and history.

Llama.cpp Configuration for Long Context

To maximize your 12GB VRAM, use the following llama.cpp flags:

1. Flash Attention: Use --flash-attn to significantly reduce VRAM usage during long context processing.
2. KV Cache Quantization: Use --cache-type-k q8_0 --cache-type-v q8_0 (or even q4_0) to compress the context memory, allowing for much longer context (e.g., 32k or 64k) within the remaining 2GB of VRAM.
3. Partial Offloading: If the model weights slightly exceed 12GB, set -ngl (number of GPU layers) to offload most, but not all, layers to the GPU. For this 26B MoE model, offloading about 40-45 layers usually fits 12GB while keeping the “active” experts mostly on-GPU for speed.

OpenClaw Integration

Since OpenClaw is an agent that often sends large amounts of context (tools, past thoughts, and environment state), ensure your server is started with a high context limit:

./llama-server -m gemma-4-26B-A4B-it-UD-IQ2_M.gguf -c 32768 --flash-attn --cache-type-k q4_0 -ngl 99

Note: If you experience “Out of Memory” (OOM) errors, reduce -ngl until it stabilizes.

References:

• Unsloth Gemma-4-26B-A4B-it-GGUF Hub
• Gemma 4 Fine-tuning and Inference Guide
• LocalLLaMA: VRAM Requirements for Gemma 4

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