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MiaAI-Lab/Qwen3.6-27B-NVFP4-vLLM

Production-ready vLLM deployment wrapper for Qwen3.6-27B (NVFP4) — self-hosted OpenAI-compatible inference

#LLM inference
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Jinja
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7
1 contributors
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Momentum

How fast MiaAI-Lab/Qwen3.6-27B-NVFP4-vLLM is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Aug 30 – Oct 26, 2026

Stars +1 in 58 days

01
1

Forks +0 in 58 days

01

Merged PRs 0 in 58 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Top 25 in Jinja

10
Aug 30Sep 7Sep 15Sep 23Oct 1Oct 9Oct 17Oct 25
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRsJinja
Sun, Aug 30, 2026+1+00#10
Mon, Aug 31, 2026+0+00Not trending
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Total+1+00
  • 1#1 on the list
  • 7Rank that day
  • Not trending
  • Mentions and achievements

    Where people are talking about MiaAI-Lab/Qwen3.6-27B-NVFP4-vLLM, and every badge it has earned
    1. Aug 151 post
      • 🧵Mega thread of all the @Alibaba_Qwen 27B recipes for a single DGX Spark, consumer GPUs & machines, 24hrs post-launch! 🚨Pro tip: Feed your agent/harness this to analyse & A/B test for the best…

        Read full on X, posted by @0xWhiteMage

        @0xWhiteMage · x.com

    2. 1 month earlier
    3. Jul 21 post
      • Important enough to be crystal clear. If your hardware can run @NVIDIAAI's NVFP4 version of Qwen 3.6 27B, this is the ONLY quantization of that model you should consider running. PERIOD.

        Read full on X, posted by @MiaAI_lab

        @MiaAI_lab · x.com

    4. Jul 11 post
      • Nvidia's Qwen 3.6 27b NVFP4 even beats Unsloth's Qwen3.6-27B-UD-Q8_K_XL in tool-eval-bench. That's 4-bit vs 8-bit 🤯 @NVIDIAAI is on 🔥 Full report here:

        Read full on X, posted by @MiaAI_lab

        @MiaAI_lab · x.com

    5. 11 days earlier
    6. Jun 201 post
      • I've tested how well Qwen3.6-27B NVFP4 runs on @NVIDIAAI DGX Spark 256k context, kv cache fp16, MTP --> 88gb VRAM Results: 1 session: 18.62 tok/s 2 concurrent sessions: 38.43 tok/s cumulative 4…

        Read full on X, posted by @MiaAI_lab

        @MiaAI_lab · x.com

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