Observed Signal · Apr 19, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Run Gemma 4 on Raspberry Pi with TurboQuant
A developer guide demonstrates how to run an autonomous OpenClaw agent on a Raspberry Pi 4B (8GB) by compiling a TurboQuant-enabled fork of llama.cpp to host Gemma 4 (Q4_K_M quantization) locally. The post documents hardware and OS recommendations (SSD boot, swap increase, cooling), build steps (NEON acceleration, build flags, TurboQuant KV cache branch), model acquisition (Hugging Face gemma-4-E2B-it Q4_K_M), and runtime flags (--cache-type-k turbo4 / --cache-type-v turbo4, Flash Attention). It shows exposing the local model via llama-server as an OpenAI-compatible API for OpenClaw, optional remote access with Tailscale, and a hybrid fallback to Google’s Gemini API for heavier tasks. The author also describes governing the agent with a KheAi Protocol system prompt for constrained, goal-oriented behavior.
Provides a practical, reproducible method to run a quantized LLM and agent framework at the edge, lowering hardware barriers for private/local agent deployment and demonstrating hybrid edge→cloud architectures relevant to AI operations.
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Key Takeaways & Evidence Grounding
- Author ran an autonomous OpenClaw agent on a Raspberry Pi 4B (8GB RAM) booting from a 120GB SSD.
- Built a community fork of llama.cpp (llama-cpp-turboquant) with TurboQuant KV cache compression and NEON acceleration to run Gemma 4 on ARMv8.
- Used Gemma 4 E2B model in Q4_K_M quantized GGUF format (gemma-4-E2B-it-Q4_K_M.gguf) as the local LLM weights.
- Runtime flags --cache-type-k turbo4 and --cache-type-v turbo4 plus Flash Attention ( -fa ) compress KV cache and reduce RAM usage during long contexts.
- Exposed the model via llama-server on port 8080 as an OpenAI-compatible backend for OpenClaw; optional remote access via Tailscale and hybrid fallback to Google’s Gemini API are described.
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Edge Autonomy Agent: Gemma 4 on Raspberry Pi 4B
A step-by-step technical guide (published 2026-05-09) showing how to run a local autonomous AI agent on a Raspberry Pi 4B (8GB RAM, SSD boot) by combining OpenClaw with Gemma 4 E2B (Q4_K_M) and a community fork of llama.cpp that implements TurboQuant KV-cache compression. The author details hardware and OS tuning (SSD boot, swap increase, thermal management), building the turboquant-enabled llama.cpp on ARMv8 (NEON), downloading GGUF-quantized Gemma 4 weights, running llama-server as an OpenAI-compatible backend, onboarding OpenClaw to the local model, applying the KheAi Protocol OODA persona, using Tailscale for secure remote access, and optionally routing heavy tasks to Google’s Gemini API for hybrid cloud-edge reasoning.
Run Gemma 4 26B on GTX 1080 with llama.cpp
A developer how-to demonstrating how to run Google’s Gemma 4 26B‑A4B Mixture‑of‑Experts model locally on an 8 GiB NVIDIA GeForce GTX 1080 using an enhanced llama.cpp fork (AtomicBot-ai/atomic-llama-cpp-turboquant). The guide details system setup (driver pinning, CUDA nvcc, gcc-14 workaround, glibc patch), building the fork with CUDA, downloading the main GGUF and MTP assistant head, and tuning offload parameters. Key optimisations include keeping most MoE expert weights in host RAM (streamed over PCIe), using RotorQuant/TurboQuant KV cache to enable 128k context, and forcing the assistant embedding table onto the GPU with --override-tensor-draft to enable effective MTP speculative decoding. The author reports ~24.5 tokens/sec at 128k context and describes the memory/PCIe tradeoffs and the final recommended command-line configuration.
Quantizing Gemma 4 on Mac with llama.cpp
A technical how-to showing how to run and quantize Google's Gemma 4 LLM on macOS using the community llama.cpp project. The guide covers building llama.cpp with Metal (GGML_METAL), creating a Python environment with required packages (torch, transformers, gguf, huggingface_hub, sentencepiece, protobuf), downloading the Hugging Face model google/gemma-4-E4B-it, converting safetensors to the GGUF format (BF16), quantizing to Q4_K_M with llama-quantize, and launching the model via llama-cli. The post includes example commands, a brief interactive session demonstrating responses and throughput metrics, and notes the model identifies as Gemma 4 developed by Google DeepMind.
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