David Hendrickson
@TeksEdge • 10,143 subscribers
CEO & Founder | PhD | Startup Advisor | @Columbia | Author Generative Software Engineering https://t.co/9oqvHuTX5f | 🔔 Follow for AI & Vibe Coding Tips 👇
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🤯 A localmaxxer hit ~381 tok/s on a SINGLE RTX 3090 with Qwen3.8-27B. This developer has turned a 24GB RTX 3090 into a monster Qwen inference box - w/some creativity. Four days ago 👉 ⚡ ~82 tok/s single-user Then 👉 ⚡ ~114 tok/s with optimized MTP ⚡ ~138 tok/s with DFlash2 + lookup drafting Now 👉 🔥 ~381 tok/s on ONE request How? The recipe combines ... 🧠 Qwen3.8-27B 🎮 1× RTX 3090 24GB @ 250W ⚙️ heavily optimized vLLM ⚡ DFlash2 speculative decoding 🔎 lookup-augmented drafting 📚 prefix caching 🧮 16-token verification blocks 💾 quantized KV / heads / activations DFlash2 normally proposes 7 tokens. The developer realized the verification block doesn't have to stop there. If Qwen is answering from a document already sitting in the prompt, the system can fill the remaining draft positions using tokens found directly in that context. 🎯 So the target model can verify 16 tokens at once. On a ~25K-token document reproduction task: Previous DFlash2 👉 ~260 tok/s Longer verification + context lookup 👉 🔥 ~382 tok/s Acceptance: 🤯 15 of 16 tokens per verification step That is where the crazy number comes from. ⚠️ On ordinary real-world chat prompts, the same setup is around ~133 tok/s Still extremely fast for a dense 27B model on an RTX 3090. The 381 tok/s mode shines when the answer largely comes from material already in context so these are best use cases 📚 RAG / document Q&A 💻 Coding assistants applying edits 📝 Quoting or rewriting documents 🔎 Extracting information from long prompts And another optimization 👉 With prefix caching, a second question against the same 25K-token document reportedly goes from: 🐌 22.4 sec TTFT → ⚡ 0.56 sec TTFT Because the model doesn't need to process the whole document again. 🎯 It's specifically a mode for RAG front ends and coding agents. Follow iamMess on Reddit or syv-ai on GitHub 🔗 Reddit: r/LocalLLaMA/comments/1vtup5s/ 🔗 GitHub: /syv-ai/qwen38-27b-rtx3090
David Hendrickson105,418 views • 4 days ago
Been tracking how LLMs do on IQ tests, especially on TrackingAI. Regardless of what you think about the test, LLMs have struggled to exceed 130, especially on questions not included in its training. Recently, models have breached this and now test better than 99% of humans
David Hendrickson84,101 views • 15 days ago
DeepSeek V4 Flash 0731 vs Opus 5. Only 7 days separated their releases. While Opus 5 accomplished in 1 shot what took DS 3, DeepSeek V4 Flash did it in ~900 lines vs ~3000 lines for Opus. Cost delta is stark. DeepSeek cost 1 cent. DS V4 Flash 0731 is new baseline for value-perf
David Hendrickson41,980 views • 23 days ago
GLM-5.2 is legit. It produces games easily and as accurately as SOTA closed-source models. My GLM 5.2 versus Fable 5 comparison is close, but I prefer Fable 5 just a little. Both games are equal in nearly all respects, but Fable 5 produced slightly better graphics and more challenging gameplay. Both needed 2 shots to get all the prompt instructions correct.
David Hendrickson18,785 views • 2 months ago
🆕 Fable 5 vs Nex-N2-Pro. Anthropic's new Fable 5 did NOT one-shot my Cosmic Dodge prompt, unlike Nex-N2-Pro. imho Nex-N2's output is more exciting, gameplay more aggressive, and bonus drops faster, but the outputs are still very close. 🏆 I give Nex-N2 the WIN, but very close. ✦ Fable 5 $50/1M > Nex-N2 $0/1M ✦ Nex-N2 1-shot > Fable 5 2-shot
David Hendrickson18,054 views • 2 months ago
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