Observed Signal · May 24, 2026 · Technical Guide · Source: DEV Community · Impact: 3/5 · Sentiment: Positive
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.
Provides practical, reproducible techniques to run a large MoE LLM (Gemma 4) on widely-available low-VRAM hardware; lowers barriers to local/fall-back inference and informs infrastructure trade-offs (PCIe vs VRAM, quantised KV caches, speculative decoding) relevant to teams deploying on-prem or edge LLMs.
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Key Takeaways & Evidence Grounding
- Gemma 4 26B-A4B is a Mixture‑of‑Experts model with 25.2B total parameters and ~3.8B active parameters per token (30 layers, trained context 256K).
- Using AtomicBot-ai/atomic-llama-cpp-turboquant and an NVIDIA GeForce GTX 1080 (8 GiB VRAM) the author achieved ~24.5 tokens/second at 128k context.
- RotorQuant / TurboQuant KV cache plus offloading most MoE experts to host RAM enables 128k context on 8 GiB VRAM.
- The assistant MTP head required forcing its tied embedding tensor onto the GPU via --override-tensor-draft "token_embd\.weight=CUDA0" to avoid large per-draft PCIe transfers and to make speculative decoding effective.
- Optimal configuration on this hardware used --n-cpu-moe 21 (with MTP) — --n-cpu-moe 20 OOMs at 128k context; 20 was the floor without MTP.
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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.
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.
Run Gemma 4 26B on a 13‑Year‑Old Xeon CPU
A technical how‑to showing how to run Google's Gemma 4 26B LLM on an older Xeon CPU using CPU-only optimizations. The tutorial lists prerequisites (Xeon server with ≥64GB RAM, Python 3.10+, ~200GB disk), shows using Hugging Face transformers and PyTorch with 4-bit quantization (load_in_4bit) to reduce memory from ~120GB to ~40GB, and applies CPU execution optimizations such as torch._dynamo.optimize_for_cpu and Intel MKL tuning. Reported performance on an Intel Xeon E5 v2: ~45GB RAM usage, ~12 tokens/sec, 3–5 minute cold start, ~150W power. The author contrasts CPU throughput with GPUs (100–300 tokens/sec) and recommends this approach for edge or proof‑of‑concept deployments.
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