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Minecraft clone made by Qwen3.8-27B. You can now do frontier-level coding on a laptop GPU.

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Shikhar

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49,522 просмотров • 1 месяц назад •via X (Twitter)

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Completed a first hour side-by-side comparison between Qwen3.5 27b and Qwen3.6 27b on the same 4 canvas coding tests. Running the Qwen3.6 27b FP8, vLLM. What do you think?
1:07

Completed a first hour side-by-side comparison between Qwen3.5 27b and Qwen3.6 27b on the same 4 canvas coding tests. Running the Qwen3.6 27b FP8, vLLM. What do you think?

stevibe

114,366 просмотров • 5 месяцев назад

Qwen3.8-27b produces beautiful results very presentable 😍 Way better than 3.6-27b by a long shot. My three.js and glsl tests are going on, but here is few examples, same prompt with same default settings all run on my 3090. qwen3.6-27b-Q5 vs qwen3.8-27b-Q5
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Qwen3.8-27b produces beautiful results very presentable 😍 Way better than 3.6-27b by a long shot. My three.js and glsl tests are going on, but here is few examples, same prompt with same default settings all run on my 3090. qwen3.6-27b-Q5 vs qwen3.8-27b-Q5

AJ

93,007 просмотров • 1 месяц назад

Qwen3.8-27B on Cerebras Goes so woosh your eyes will hurt, imo this is a frontier level model. The main issue is it thinks so much, and it was quite slow. This, though? flies. Decent price too!
0:45

Qwen3.8-27B on Cerebras Goes so woosh your eyes will hurt, imo this is a frontier level model. The main issue is it thinks so much, and it was quite slow. This, though? flies. Decent price too!

0xSero

34,827 просмотров • 20 дней назад

i tested Qwen3.8 27B locally on my M4 Pro to see how it behaves as a coding agent inside my project showing step-by-step the full guide on how you can run it, perfect for your first local model 00:00 Why I’m testing Qwen3.8 00:49 What Qwen3.8 includes 00:59 Qwen3.8 Max vs 27B 01:14 Qwen3.8 Max explained 02:13 Qwen’s benchmark claims 02:31 Its autonomous coding project 03:18 Qwen3.8 27B explained 04:30 Why I’m testing it locally 05:05 Download Qwen 3.8 in LM Studio 06:00 Pick the Q4 model 06:41 Run it as a local server 07:33 How fast is it? 08:36 Set up OpenCode 09:33 Connect LM Studio to OpenCode 10:03 Add Qwen 3.8 to OpenCode 11:34 Match the context settings 15:00 Give Qwen a real coding task 15:49 A mistake I caught live: session compaction 18:00 Review the code 19:30 My verdict
21:02

i tested Qwen3.8 27B locally on my M4 Pro to see how it behaves as a coding agent inside my project showing step-by-step the full guide on how you can run it, perfect for your first local model 00:00 Why I’m testing Qwen3.8 00:49 What Qwen3.8 includes 00:59 Qwen3.8 Max vs 27B 01:14 Qwen3.8 Max explained 02:13 Qwen’s benchmark claims 02:31 Its autonomous coding project 03:18 Qwen3.8 27B explained 04:30 Why I’m testing it locally 05:05 Download Qwen 3.8 in LM Studio 06:00 Pick the Q4 model 06:41 Run it as a local server 07:33 How fast is it? 08:36 Set up OpenCode 09:33 Connect LM Studio to OpenCode 10:03 Add Qwen 3.8 to OpenCode 11:34 Match the context settings 15:00 Give Qwen a real coding task 15:49 A mistake I caught live: session compaction 18:00 Review the code 19:30 My verdict

Nidhi Singh

43,169 просмотров • 1 месяц назад

You can now run Qwen3.8-27B in full BF16 on a free Kaggle TPU. - with about 130 tok/s decode, - 10k tok/s prefill, - Native 262k context. - No quantization. - No paid GPU. - Measured on TPU v5e-8 It also exposes an OpenAI-compatible endpoint, so it plugs into Claude Code, Codex, and OpenCode.
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You can now run Qwen3.8-27B in full BF16 on a free Kaggle TPU. - with about 130 tok/s decode, - 10k tok/s prefill, - Native 262k context. - No quantization. - No paid GPU. - Measured on TPU v5e-8 It also exposes an OpenAI-compatible endpoint, so it plugs into Claude Code, Codex, and OpenCode.

Md Ismail Šojal 🕷️

50,394 просмотров • 28 дней назад

Qwen3.8-27B running at full BF16 on a free Kaggle TPU is kind of ridiculous. No quantization. No tiny context window. No expensive GPU instance. Just Qwen3.8-27B running on a Kaggle TPU v5e-8. The reported numbers: ~130 tok/s decode ~10,000 tok/s prefill 262K context That prefill number is especially wild. You can throw a huge amount of code or context at the model and ingest it extremely quickly, while still getting around 130 tokens per second during generation. And because it’s running in full BF16, you’re not relying on an aggressive quant just to make the model fit. But the really interesting part isn’t even the raw throughput. You can expose it as an OpenAI-compatible endpoint. That means you can plug the model into tools that already understand OpenAI-style APIs. Claude Code. Codex. OpenCode. And other compatible clients. So the workflow becomes pretty simple: Spin up the Qwen3.8-27B endpoint on Kaggle. Point your coding tool at the API. And suddenly you have a 27B coding model sitting behind the same interface you’d normally use for hosted models. The 262K context is also a huge deal for agentic coding. Large repositories can fit into a single context. Long conversations don’t need to be constantly trimmed. And tools can feed much more information back to the model without hitting a tiny context ceiling. The fact that this can be built around a free TPU environment is what makes this especially interesting. We’re getting to a point where experimenting with serious open models doesn’t always require owning a $2,000 GPU or paying for a large cloud instance. Free compute + open weights + an OpenAI-compatible API + existing coding agents. That’s a pretty powerful combination. Qwen3.8-27B is already an interesting model. Running the full BF16 version at ~130 tok/s with 262K context on free Kaggle TPU compute makes it a lot more interesting.
0:48

Qwen3.8-27B running at full BF16 on a free Kaggle TPU is kind of ridiculous. No quantization. No tiny context window. No expensive GPU instance. Just Qwen3.8-27B running on a Kaggle TPU v5e-8. The reported numbers: ~130 tok/s decode ~10,000 tok/s prefill 262K context That prefill number is especially wild. You can throw a huge amount of code or context at the model and ingest it extremely quickly, while still getting around 130 tokens per second during generation. And because it’s running in full BF16, you’re not relying on an aggressive quant just to make the model fit. But the really interesting part isn’t even the raw throughput. You can expose it as an OpenAI-compatible endpoint. That means you can plug the model into tools that already understand OpenAI-style APIs. Claude Code. Codex. OpenCode. And other compatible clients. So the workflow becomes pretty simple: Spin up the Qwen3.8-27B endpoint on Kaggle. Point your coding tool at the API. And suddenly you have a 27B coding model sitting behind the same interface you’d normally use for hosted models. The 262K context is also a huge deal for agentic coding. Large repositories can fit into a single context. Long conversations don’t need to be constantly trimmed. And tools can feed much more information back to the model without hitting a tiny context ceiling. The fact that this can be built around a free TPU environment is what makes this especially interesting. We’re getting to a point where experimenting with serious open models doesn’t always require owning a $2,000 GPU or paying for a large cloud instance. Free compute + open weights + an OpenAI-compatible API + existing coding agents. That’s a pretty powerful combination. Qwen3.8-27B is already an interesting model. Running the full BF16 version at ~130 tok/s with 262K context on free Kaggle TPU compute makes it a lot more interesting.

FHILY👑

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the era of agentic AI is here Google just open sourced Gemma 3, you can run it on a single GPU laptop or even phone - agentic AI workflows - 4 Sizes – 1B, 4B, 12B, 27B - up to 128K tokens - speaks 140+ languages - hit 1338 ELO on LMArena try & download for free, links 👇

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541,235 просмотров • 1 год назад

GLM 5.2 in pi running at almost 100 tokens/sec on 8x H200. I really like this model. It feels a frontier-level model you can actually self-host. Next step: squeeze more out of it by benchmarking different quants, tuning the stack, and figuring out the minimum GPU setup people can realistically run it on. Expect more on this soon.
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GLM 5.2 in pi running at almost 100 tokens/sec on 8x H200. I really like this model. It feels a frontier-level model you can actually self-host. Next step: squeeze more out of it by benchmarking different quants, tuning the stack, and figuring out the minimum GPU setup people can realistically run it on. Expect more on this soon.

Atharva Ingle

26,784 просмотров • 3 месяцев назад

Here's Qwen3.8-27B Runs on any macbook/gpu with 24gb or more I gave it an hour of video, and told it to find the latest session with claude where it helped me make edit yesterdays video and create the thumbnail Running on Openrouter, big models do first Small models follow
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Here's Qwen3.8-27B Runs on any macbook/gpu with 24gb or more I gave it an hour of video, and told it to find the latest session with claude where it helped me make edit yesterdays video and create the thumbnail Running on Openrouter, big models do first Small models follow

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📣 Kimi.ai's Kimi K3, an open-weight model, is now generally available and rolling out in GitHub Copilot. The model shows frontier-level abilities on agentic coding with highly cost-effective pricing. It is hosted by Fireworks. Learn more. 👇

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