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Unsloth AI ✓

@UnslothAI · San Francisco, CA · joined 30 Nov 2023

Run and train models locally with the Unsloth Desktop app. 🦥 https://t.co/2kXqhhvdCD

96 610Followers
478Following
771Posts total
2.6MViews on collected posts

Latest posts

Qwen3.8-Flash can now run 1.7× faster locally with MTP!⚡️ GGUFs can reach 170 tokens/s on a RTX PRO 6000. MTP enables Qwen3.8-Flash-Next ~1.3–1.7× faster inference with no accuracy change. GGUFs: https://t.co/vXkjO3W0fj Guide: https://t.co/LLMclyJTeL https://t.co/KuWfuLOJBR 100.3K views · 1.1K likes · 124 reposts · 60 replies Open on X →
GLM-5.3 can now be run locally! The 2-bit model retains ~81% accuracy after we shrunk it from 1.51TB to 239GB (-83% size). Run on a 256GB Mac or RAM/VRAM setups. GLM-5.3 is the strongest open model to date. Guide: https://t.co/NLgb3CMB6A GGUF: https://t.co/Sqwq3xjgo5 https:// 224.1K views · 1.9K likes · 213 reposts · 100 replies Open on X →
GLM-5.3-Flash can now be run locally! ✨ Run 3-bit on 128GB RAM via Unsloth GGUF. GLM-5.3-Flash (ox-alpha) rivals Claude Opus 4.8 on DeepSWE, coding & agentic benchmarks. Guide: https://t.co/oItBKYNrl9 GGUF: https://t.co/E73FKC7IKM https://t.co/V1t8fDIUtp 302.9K views · 1.7K likes · 201 reposts · 106 replies Open on X →
@UnslothAI Running Qwen 3.8 Flash Next on a 12GB Android phone with unsloth gguf. 2 tok/s https://t.co/O3K1x3ksVc 4K views · 17 likes · 3 reposts · 1 replies Open on X →
@UnslothAI i love that “locally” now means 75gb of ram and a small prayer 5.1K views · 37 likes · 0 reposts · 6 replies Open on X →
@UnslothAI You are faster than light! Updating and downloading everything NOW!!!! 5.6K views · 42 likes · 0 reposts · 0 replies Open on X →
Qwen3.8-Flash can now be run locally! 🔥 The 125B MoE model outperforms Claude-Opus-4.6 (Max). Run on 75GB RAM via Unsloth GGUFs. Qwen3.8-Flash-Next enables CPU RAM / unified mem setups to deliver near VRAM speeds. Guide: https://t.co/LLMclyJTeL GGUF: https://t.co/vXkjO3W0fj h 1.1M views · 2.5K likes · 319 reposts · 164 replies Open on X →
Introducing Unsloth Desktop 🦥 The first desktop app to run and train models locally. • Open-source. Runs on Mac, Windows and Linux • Supports MLX, diffusion image/video, audio, GGUF • Connect Claude Code and Codex to local LLMs • 50% more accurate, self-healing tool calls + http 804.2K views · 4.8K likes · 634 reposts · 272 replies Open on X →

Against accounts of the same size

8 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views162 198this account924median for 10K–100K
Reach, %167.89%this account3.62%median for 10K–100K
Engagement, %0.74%this account1.52%median for 10K–100K
MetricThis accountMedian for 10K–100KRatio
Median views per post162 198924176×
Reach (views ÷ followers)167.89%3.62%46.4×
Engagement rate0.74%1.52%0.48×

Others in this range →   Compare with another account →   How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

804.2K11 Aug
1.1M26 Aug
5.6K
5.1K
4K
302.9K27 Aug
224.1K28 Aug
100.3K2 Sep

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.73%11 Aug
0.28%26 Aug
0.75%
0.85%
0.53%
0.67%27 Aug
1.01%28 Aug
1.31%2 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes56.6%12 104 in total
Reposts7.0%1 494 in total
Replies3.3%709 in total
Quotes1.8%392 in total
Bookmarks31.3%6 694 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

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