{
  "video_id": "reddit_1tkpz2y",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "BeeLlama v0.2.0 – major DFlash update. Single RTX 3090: Qwen 3.6 27B up to 164 tps (4.40x), Gemma 4 31B up to 177.8 tps (4.93x). Prompt processing speed near baseline.",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1tkpz2y/beellama_v020_major_dflash_update_single_rtx_3090/",
  "external_url": null,
  "upload_date": "20260522",
  "published_at": "2026-05-22T17:34:59+00:00",
  "transcript": "**BeeLlama v0.2.0 is here!**\n\n>Not quite a pegasus, but close enough.\n\n[**GitHub**](https://github.com/Anbeeld/beellama.cpp) **|** [**Qwen 3.6 27B Quick Start**](https://github.com/Anbeeld/beellama.cpp/blob/main/docs/quickstart-qwen36-dflash.md) **|** [**Gemma 4 31B Quick Start**](https://github.com/Anbeeld/beellama.cpp/blob/main/docs/quickstart-gemma-4-31b-dflash.md)\n\n* Full Gemma 4 31B support with efficient DFlash implementation and vision.\n* Major Qwen 3.6 27B performance update from lower DFlash overhead, cleaner prefill handling, drafter K/V projection caching, and safer CUDA execution.\n* DFlash GGUFs with upstream architecture are now supported.\n* Fixes to adaptive profit behavior around baseline probing.\n* Reduced verifier path is stricter now, with safer fallback to full logits when grammar, sampler state, or reasoning requires it.\n* Reasoning and tool-call boundaries were tightened.\n* Stricter draft/target validation and better draft-model discovery.\n* ...and many more improvements!\n\n**Benchmarks**\n\n* Setup: Windows 11, AMD Ryzen 7 5700X3D, 32 GB DDR4 RAM, RTX 3090 24 GB\n* Config: same as in quick start docs, but with reasoning off for non-chat prompts\n* Baseline and MTP server in comparison: llama.cpp [b9275](https://github.com/ggml-org/llama.cpp/releases/tag/b9275) CUDA 13.1 Windows prebuilt\n* The full text of the benchmark prompts is in [README.md on GitHub](https://github.com/Anbeeld/beellama.cpp/blob/main/README.md#dflash-speedup)\n\n**Qwen 3.6 27B**\n\nTarget model: [Qwen 3.6 27B Q5\\_K\\_S](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF) or [Qwen 3.6 27B MTP Q5\\_K\\_S](https://huggingface.co/unsloth/Qwen3.6-27B-MTP-GGUF). DFlash model: [Q4\\_K\\_M](https://huggingface.co/Anbeeld/Qwen3.6-27B-DFlash-GGUF).\n\n|Prompt|Server|Output|Median|Best|Speedup|Acceptance|\n|:-|:-|:-|:-|:-|:-|:-|\n|Task store module|Baseline|\\~1K tok|37.2 tok/s|37.2 tok/s|1.00x|N/A|\n|Task store module|DFlash|\\~1K tok|**163.9 tok/s**|181.9 tok/s|**4.40x**|67.7% / 89.2%|\n|Task store module|MTP|\\~1K tok|69.3 tok/s|69.6 tok/s|1.86x|92.0% / 73.3%|\n|KV report module|Baseline|\\~1K tok|34.6 tok/s|36.5 tok/s|1.00x|N/A|\n|KV report module|DFlash|\\~1K tok|**157.7 tok/s**|162.5 tok/s|**4.56x**|58.8% / 88.9%|\n|KV report module|MTP|\\~1K tok|67.3 tok/s|68.1 tok/s|1.94x|89.3% / 73.0%|\n|Doubly-linked list|Baseline|\\~4K tok|36.8 tok/s|36.9 tok/s|1.00x|N/A|\n|Doubly-linked list|DFlash|\\~4K tok|**130.8 tok/s**|154.1 tok/s|**3.56x**|50.4% / 86.8%|\n|Doubly-linked list|MTP|\\~4K tok|66.3 tok/s|68.0 tok/s|1.80x|87.8% / 72.5%|\n|Prompt processing|Baseline|\\~20K tok|1229.5 tok/s|1229.5 tok/s|1.00x|N/A|\n|Prompt processing|DFlash|\\~20K tok|**1214.4 tok/s**|1221.7 tok/s|**0.99x**|N/A|\n|Prompt processing|MTP|\\~20K tok|1162.6 tok/s|1164.7 tok/s|0.95x|N/A|\n|Multi-turn coding|Baseline|\\~28K tok|33.3 tok/s|33.3 tok/s|1.00x|N/A|\n|Multi-turn coding|DFlash|\\~30K tok|**64.6 tok/s**|65.4 tok/s|**1.94x**|24.9% / 72.9%|\n|Multi-turn coding|MTP|\\~34K tok|56.5 tok/s|56.5 tok/s|1.70x|71.9% / 68.3%|\n\n*Acceptance: accepted to proposed draft tokens / accepted draft tokens to final generated tokens*\n\n**Gemma 4 31B**\n\nTarget model: [Gemma 4 31B Q4\\_K\\_S](https://huggingface.co/unsloth/gemma-4-31b-it-GGUF). DFlash model: [Q5\\_K\\_M](https://huggingface.co/Anbeeld/gemma-4-31B-it-DFlash-GGUF).\n\n|Prompt|Server|Output|Median|Best|Speedup|Acceptance|\n|:-|:-|:-|:-|:-|:-|:-|\n|Task store module|Baseline|\\~1K tok|36.1 tok/s|36.1 tok/s|1.00x|N/A|\n|Task store module|DFlash|\\~1K tok|**177.8 tok/s**|182.0 tok/s|**4.93x**|65.7% / 90.0%|\n|KV report module|Baseline|\\~1K tok|35.9 tok/s|36.0 tok/s|1.00x|N/A|\n|KV report module|DFlash|\\~1K tok|**154.3 tok/s**|162.8 tok/s|**4.29x**|55.7% / 88.6%|\n|Doubly-linked list|Baseline|\\~1.9K tok|36.0 tok/s|36.0 tok/s|1.00x|N/A|\n|Doubly-linked list|DFlash|\\~1.9K tok|**116.6 tok/s**|127.3 tok/s|**3.24x**|44.5% / 84.9%|\n|Prompt processing|Baseline|\\~24K tok|1021.3 tok/s|1021.3 tok/s|1.00x|N/A|\n|Prompt processing|DFlash|\\~24K tok|**954.5 tok/s**|954.9 tok/s|**0.93x**|N/A|\n|Multi-turn coding|Baseline|\\~12K tok|34.8 tok/s|34.8 tok/s|1.00x|N/A|\n|Multi-turn coding|DFlash|\\~12K tok|**60.6 tok/s**|64.1 tok/s|**1.74x**|24.4% / 72.3%|\n\n*Acceptance: accepted to proposed draft tokens / accepted draft tokens to final generated tokens*\n\n\n\n--- Top Comments ---\n\n\n[43 upvotes] and here goes my evening...\n\n[16 upvotes] For agentic coding. So like 200k context large chats on opencode. Is MTP from the latest llama.cpp or DFlash faster? \n\n[8 upvotes] This is incredible. Squeezing that 3090 like a lemon. Keep up the good work man",
  "transcript_chars": 4530,
  "ingested_at": "2026-05-23T01:30:28.910984+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 139,
    "upvote_ratio": 0.97,
    "num_comments": 95,
    "author": "Anbeeld",
    "is_self": true
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}