Anyone successfully using Gemma4 31B with OpenClaw?
On April 10th, someone in the LocalLLaMA community posted a straightforward question: is anyone successfully running Gemma4 31B with OpenClaw? The post scored a modest 4.3, suggesting a handful of people engaged but no flood of answers came rushing in. That silence is itself informative. Gemma4, Google's latest open-weight model family, arrived with considerable interest from the local inference crowd, and 31B sits in a sweet spot — large enough to be genuinely capable, small enough that enthusiasts with high-end consumer hardware can theoretically run it. OpenClaw, the inference framework in question, has been gaining quiet traction among the self-hosting community as an alternative to more established tooling. The question being asked at all tells you something: someone has tried, hit friction, and gone looking for others who navigated the same terrain. That is usually how compatibility issues surface in this ecosystem — not through official announcements, but through one person's frustration becoming a public thread.
If confirmed as a genuine compatibility gap, here is what this means. The local LLM community functions as an early stress-test for model deployments, and friction here tends to propagate — if OpenClaw struggles with Gemma4 31B's architecture, quantisation, or attention implementation, other frameworks are likely encountering similar edges. Users who have built workflows around OpenClaw face a real decision: wait for a patch, switch tooling, or drop down to a smaller Gemma4 variant. For Google, rough edges in the open-source inference ecosystem matter more than they once did — the credibility of an open-weight release depends on whether the community can actually run it cleanly. A model that works in benchmarks but fights with popular inference tools is a model that loses mindshare fast.
Watch for follow-up replies in the original thread confirming specific error conditions or workarounds, and keep an eye on the OpenClaw GitHub issues tracker for any Gemma4-specific bug reports filed around the same date.
<!-- SC_OFF --><div class="md"><p>In case you haven't heard, Google just released Multi Token Prediction drafters for Gemma 4, a speculative decoding approach that pairs the main
<!-- SC_OFF --><div class="md"><p>Beginner here, now I have a good GPU and ollama using docker. Pulled the Gemma4 weights and was able to add it to cursor using ngrok. </p>
<!-- SC_OFF --><div class="md"><p>I’ve been testing LM Studio on my RTX 5070 Ti (16GB) and Ryzen 9800X3D, running Unsloth Qwen3.5 35B (UD Q4_K_XL).</p> <p>Initially, I thought LM
<!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I taught Computer Science for 30 years in a French School of Electrical Engineering, Computer Science Department.</p> <p>I recently
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<!-- SC_OFF --><div class="md"><blockquote> <p><strong>WARNING: wait before download from HF: I just realised my upload of the new versions with the additional fix in the chat
<!-- SC_OFF --><div class="md"><p>I need a 4bit quant to run 27B on my 3090 but I swear I can't find anything that works. TevunahAi/Qwen3.5-27B-TevunahAi-GPTQ doesn't seem to work
<!-- SC_OFF --><div class="md"><p>Hi all I had a quick question while we wait for llama.cpp MTP implementation, have any of y'all tried Gemma4 MTP models on ollama and or
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