Speculative Decoding for Language Models
What started on April 11th was a question, not a headline. Someone on LocalLLaMA asked whether speculative decoding in llama.cpp could be made to work with Gemma 4 31B Instruct and Qwen 3.5 27B — two of the more capable locally-runnable models doing the rounds right now. The post scored modestly, which in forum terms means it landed in the right room but hadn't found its audience yet. By April 14th, that had shifted. A follow-up thread — "Share your speculative settings for llama.cpp and Gemma4" — suggests the conversation moved from possibility to practice. People weren't just asking whether it could work; they were comparing notes on how they were making it work. That's a meaningful transition. The score jumped to 7.1, indicating genuine community engagement rather than idle curiosity.
If confirmed, here is what this means. Speculative decoding — where a small draft model generates token candidates that a larger model then validates in parallel — can dramatically reduce inference latency on consumer hardware without touching model quality. Getting it working reliably with Gemma 4 and Qwen at these parameter counts would make frontier-class local inference meaningfully faster for anyone running llama.cpp. The second-order effect is broader: every efficiency gain in local model execution is a step away from API dependency, and community-optimised settings shared openly accelerate that curve faster than any single lab's release. The people publishing their configs aren't just solving their own problem — they're building a shared playbook.
Watch for whether verified benchmarks emerge from the LocalLLaMA threads showing quantified speed improvements, and whether llama.cpp's maintainers formally document or merge speculative decoding support for these specific model architectures.
<!-- SC_OFF --><div class="md"><p>DeepSeek dropped the full V4 paper this week. preview from april was 58 pages, this version adds a lot of technical depth.</p> <p>What stood out
<!-- 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>Running Llama-3-70B with long contexts and hitting HBM limits constantly. 128K context burns approx 40GB just for KV cache — half the H100 gone
<!-- SC_OFF --><div class="md"><p>Wrote a deep dive on <strong>FlashAttention-4 (03/05/2026)</strong> that's relevant for anyone thinking about inference performance.</p>
<!-- SC_OFF --><div class="md"><p>Every output token the model generates requires reading the entire KV cache from High Bandwidth Memory.</p> <p>Here's what that actually looks
<!-- SC_OFF --><div class="md"><p>Hey everyone, I've been working on getting Multi-Token Prediction (MTP) working with quantized GGUFs for Qwen3-27B and the results are pretty
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s59rct/rys_part_3_llms_think_in_geometry_not_language/"> <img alt="RYS Part 3: LLMs think in geometry, not
<!-- SC_OFF --><div class="md"><p>PR #22673 (commit 4f13cb7) landed MTP speculative decoding in mainline llama.cpp on May 16. I tested it on two separate rigs.</p> <p>Qwen3.6 27B,
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