name string | body string | score int64 | controversiality int64 | created timestamp[us] | author string | collapsed bool | edited timestamp[us] | gilded int64 | id string | locked bool | permalink string | stickied bool | ups int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
t1_o8dn13m | What quants are you using ?
I still don't know if I should use the full 27B (in FP16) with vLLM (tp=4 on 4*RTX 3090, great speed around 70-80 tok/s) or the 122B in GGUF (way slower because of llamacpp).
Sadly toolcall in EXL3 isn't working well. | 1 | 0 | 2026-03-03T08:22:33 | TacGibs | false | null | 0 | o8dn13m | false | /r/LocalLLaMA/comments/1rjg5qm/qwen3535ba3b_vs_qwen3_coder_30b_a3b_instruct_for/o8dn13m/ | false | 1 |
t1_o8dmrss | How long do the abliterated versions usually take to start appearing? | 1 | 0 | 2026-03-03T08:19:56 | chaosboi | false | null | 0 | o8dmrss | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8dmrss/ | false | 1 |
t1_o8dmr5c | wat | 1 | 0 | 2026-03-03T08:19:46 | MelodicRecognition7 | false | null | 0 | o8dmr5c | false | /r/LocalLLaMA/comments/1rjikwz/help_me_create_my_llm_ecosystem/o8dmr5c/ | false | 1 |
t1_o8dmpz0 | Kawaii | 1 | 0 | 2026-03-03T08:19:26 | ksoops | false | null | 0 | o8dmpz0 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o8dmpz0/ | false | 1 |
t1_o8dmmyl | it's just more and more unnecessary yapping | 1 | 0 | 2026-03-03T08:18:37 | KomithErr404 | false | null | 0 | o8dmmyl | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8dmmyl/ | false | 1 |
t1_o8dml7x | Sunshine/Moonlight.
I use them to play games from my desktop anywhere on earth.
10/10 no notes | 1 | 0 | 2026-03-03T08:18:07 | insanemal | false | null | 0 | o8dml7x | false | /r/LocalLLaMA/comments/1rjh5wg/unsloth_fixed_version_of_qwen3535ba3b_is/o8dml7x/ | false | 1 |
t1_o8dml8x | You’re not missing anything obvious — a lot of the excitement around **OpenClaw** is more about *direction* than a single killer feature.
What’s different isn’t so much UX polish (tools like Manus are strong there), but a shift in **where control lives**.
A few things people are reacting to:
* **Architecture over prompts.** OpenClaw pushes people to think in terms of tools, execution loops, permissions, and state, instead of just clever prompting. That’s appealing to folks who want agents to *do things*, not just chat.
* **Explicit control layers.** Instead of abstracting everything away, it makes the decision/action boundary more visible. You’re forced to be explicit about *when* an agent can act, which tools it can use, and under what constraints.
* **Composability.** It’s closer to a framework than a product. That makes it less friendly at first, but more flexible if you want to build something long-lived or production-ish.
* **Alignment with “prod reality.”** The hype is cooling on fully autonomous agents. OpenClaw fits the newer mindset of agents as orchestrated workflows with guardrails, which resonates with builders who’ve been burned by demo-only setups.
The tradeoff is obvious: it’s harder to get started, and without the right execution environment it can feel underwhelming or even “broken.”
*Context:* we started building **ClawDock** largely because we saw people bounce off OpenClaw at this exact point — the ideas make sense, but wiring runtime, permissions, and visibility is where things get confusing.
If you’re comparing it to Manus: Manus optimizes for immediacy and UX; OpenClaw optimizes for control and extensibility. Which one feels “special” really depends on whether you’re exploring or trying to build something that has to survive contact with production. | 1 | 0 | 2026-03-03T08:18:07 | Icy-Resource164 | false | null | 0 | o8dml8x | false | /r/LocalLLaMA/comments/1r9gve8/i_feel_left_behind_what_is_special_about_openclaw/o8dml8x/ | false | 1 |
t1_o8dmhyp | And so far the model output seems to be good even though this is fairly exotic quant. Quality work, thanks! Gonna play with it a bit :D | 1 | 0 | 2026-03-03T08:17:14 | jslominski | false | null | 0 | o8dmhyp | false | /r/LocalLLaMA/comments/1rianwb/running_qwen35_27b_dense_with_170k_context_at/o8dmhyp/ | false | 1 |
t1_o8dmc6n | It understood that pressurized fuel tanks are basically eggs tho | 1 | 0 | 2026-03-03T08:15:41 | huffalump1 | false | null | 0 | o8dmc6n | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8dmc6n/ | false | 1 |
t1_o8dma0m | Shit, have you been bot swizzled? | 1 | 0 | 2026-03-03T08:15:05 | Accomplished_Ad9530 | false | null | 0 | o8dma0m | false | /r/LocalLLaMA/comments/1rjikwz/help_me_create_my_llm_ecosystem/o8dma0m/ | false | 1 |
t1_o8dm8tq | Litellm is one. I just wrote a guide:
[https://www.reddit.com/r/LocalLLaMA/comments/1rj8zuh/manage\_qwen\_35\_model\_settings\_with\_litellm\_proxy/](https://www.reddit.com/r/LocalLLaMA/comments/1rj8zuh/manage_qwen_35_model_settings_with_litellm_proxy/) | 1 | 0 | 2026-03-03T08:14:45 | CATLLM | false | null | 0 | o8dm8tq | false | /r/LocalLLaMA/comments/1rit2fy/reverted_from_qwen35_27b_back_to_qwen3_8b/o8dm8tq/ | false | 1 |
t1_o8dm2tg | isn't llama.cpp WASM dead? | 1 | 0 | 2026-03-03T08:13:07 | holdenk | false | null | 0 | o8dm2tg | false | /r/LocalLLaMA/comments/1rizodv/running_qwen_35_08b_locally_in_the_browser_on/o8dm2tg/ | false | 1 |
t1_o8dm1j5 | Isn’t rdma only going to work with TB5, will rdma work over TB4? | 1 | 0 | 2026-03-03T08:12:46 | JaatGuru | false | null | 0 | o8dm1j5 | false | /r/LocalLLaMA/comments/1rga9x4/qwen35122ba10b_pooled_on_dual_mac_studio_m4_max/o8dm1j5/ | false | 1 |
t1_o8dly4j | Why would you want to do do with 2 Mac studios since it can fit one easily? Preparing for Deepseek 4, perhaps? | 1 | 0 | 2026-03-03T08:11:52 | JaatGuru | false | null | 0 | o8dly4j | false | /r/LocalLLaMA/comments/1rga9x4/qwen35122ba10b_pooled_on_dual_mac_studio_m4_max/o8dly4j/ | false | 1 |
t1_o8dlxbg | I have 4GB of RAM, and I'm not sure if the phone came with a physical problem or a software issue, but the RAM management is so terrible that it feels like I have 2GB or less. | 1 | 0 | 2026-03-03T08:11:39 | Samy_Horny | false | null | 0 | o8dlxbg | false | /r/LocalLLaMA/comments/1rjcqm5/qwen_35_4b_is_scary_smart/o8dlxbg/ | false | 1 |
t1_o8dlvuf | I tried the 4B and 9B models and honestly, they are the weakest models I’ve ever used. Their instruction-following and reasoning abilities are poor. Even when I specifically asked for JSON output, they failed to understand correctly. They struggle with normal logical thinking.
On the other hand, I tested the Qwen 3 4B Instruct model, and it performed much better than the newer Qwen 3.5 4B. This is a serious issue benchmark scores alone don’t reflect real-world usability. Just because a model performs well in benchmarks doesn’t mean it will actually be good in practice.
I’m very disappointed with Qwen because the results don’t match expectations. | 1 | 0 | 2026-03-03T08:11:16 | Turbulent_Pie_8135 | false | null | 0 | o8dlvuf | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o8dlvuf/ | false | 1 |
t1_o8dlthp | As a sidenode to the sidenote, if you actually need to put dynamic data into the system prompt, like a current timestamp - the prompt above has CURRENT_DATETIME}}) - you should put it at the end. This way more of it can be cached. | 1 | 0 | 2026-03-03T08:10:39 | AnotherAvery | false | null | 0 | o8dlthp | false | /r/LocalLLaMA/comments/1rjh5wg/unsloth_fixed_version_of_qwen3535ba3b_is/o8dlthp/ | false | 1 |
t1_o8dlqhl | If your smartphone has 8GB ram then 4b handle easily. | 1 | 0 | 2026-03-03T08:09:50 | Healthy-Nebula-3603 | false | null | 0 | o8dlqhl | false | /r/LocalLLaMA/comments/1rjcqm5/qwen_35_4b_is_scary_smart/o8dlqhl/ | false | 1 |
t1_o8dlpze | I noticed that. How on earth does it work? Is it some sort of context compression? | 1 | 0 | 2026-03-03T08:09:43 | roosterfareye | false | null | 0 | o8dlpze | false | /r/LocalLLaMA/comments/1rjff88/how_do_i_get_the_best_speed_out_of_qwen_35_9b_in/o8dlpze/ | false | 1 |
t1_o8dlolv | [Well no](https://fr.reddit.com/r/LocalLLaMA/comments/1oymku1/heretic_fully_automatic_censorship_removal_for/np7jhxd/) | 1 | 0 | 2026-03-03T08:09:21 | Extraaltodeus | false | null | 0 | o8dlolv | false | /r/LocalLLaMA/comments/1rja0sb/gptoss_had_to_think_for_4_minutes_where_qwen359b/o8dlolv/ | false | 1 |
t1_o8dln3t | Thanks for posting. Have you compared it against Qwen3.5 35B A3B variant? I haven't tried the 27B dense model, but the one I mentioned shows very good results so far, but holy smoking reasoning tokens | 1 | 0 | 2026-03-03T08:08:57 | Lazy-Variation-1452 | false | null | 0 | o8dln3t | false | /r/LocalLLaMA/comments/1rj6m71/qwen_35_27b_a_testament_to_the_transformer/o8dln3t/ | false | 1 |
t1_o8dlkta | I test on different models with the same question. You can try by yourself and see that it does get it every time. | 1 | 0 | 2026-03-03T08:08:20 | Extraaltodeus | false | null | 0 | o8dlkta | false | /r/LocalLLaMA/comments/1rja0sb/gptoss_had_to_think_for_4_minutes_where_qwen359b/o8dlkta/ | false | 1 |
t1_o8dlgit | I tried the 4B and 9B models and honestly, they are the weakest models I’ve ever used. Their instruction-following and reasoning abilities are poor. Even when I specifically asked for JSON output, they failed to understand correctly. They struggle with normal logical thinking.
On the other hand, I tested the Qwen 3 4B Instruct model, and it performed much better than the newer Qwen 3.5 4B. This is a serious issue benchmark scores alone don’t reflect real-world usability. Just because a model performs well in benchmarks doesn’t mean it will actually be good in practice.
I’m very disappointed with Qwen because the results don’t match expectations. | 1 | 0 | 2026-03-03T08:07:13 | Turbulent_Pie_8135 | false | null | 0 | o8dlgit | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o8dlgit/ | false | 1 |
t1_o8dleo6 | The #QuitGPT movement is so justified.
Only problem is that people have spent so much time building context in chatgpt that moving away from it feels like throwing away everything.
I've built an MCP server that acts as a universal memory layer across all AI platforms simultaneously. That has honestly been helping me switch between different AIs without losing context.
Happy to share if someone is interested. | 1 | 0 | 2026-03-03T08:06:44 | Reasonable-Jump-8539 | false | null | 0 | o8dleo6 | false | /r/LocalLLaMA/comments/1rh2lew/openai_pivot_investors_love/o8dleo6/ | false | 1 |
t1_o8dlec8 | granite was not built to compete on raw benchmarks.. its whole value is in the training data transparancy and apache 2.0 licencing which matter way more in enterprise or regulated deployments than context window size ever will.. | 1 | 0 | 2026-03-03T08:06:39 | ashersullivan | false | null | 0 | o8dlec8 | false | /r/LocalLLaMA/comments/1rji5bc/how_do_the_small_qwen35_models_compare_to_the/o8dlec8/ | false | 1 |
t1_o8dldyx | I tried the 4B and 9B models and honestly, they are the weakest models I’ve ever used. Their instruction-following and reasoning abilities are poor. Even when I specifically asked for JSON output, they failed to understand correctly. They struggle with normal logical thinking.
On the other hand, I tested the Qwen 3 4B Instruct model, and it performed much better than the newer Qwen 3.5 4B. This is a serious issue benchmark scores alone don’t reflect real-world usability. Just because a model performs well in benchmarks doesn’t mean it will actually be good in practice.
I’m very disappointed with Qwen because the results don’t match expectations. | 1 | 0 | 2026-03-03T08:06:33 | Turbulent_Pie_8135 | false | null | 0 | o8dldyx | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o8dldyx/ | false | 1 |
t1_o8dlczy | I tried the 4B and 9B models and honestly, they are the weakest models I’ve ever used. Their instruction-following and reasoning abilities are poor. Even when I specifically asked for JSON output, they failed to understand correctly. They struggle with normal logical thinking.
On the other hand, I tested the Qwen 3 4B Instruct model, and it performed much better than the newer Qwen 3.5 4B. This is a serious issue benchmark scores alone don’t reflect real-world usability. Just because a model performs well in benchmarks doesn’t mean it will actually be good in practice.
I’m very disappointed with Qwen because the results don’t match expectations. | 1 | 0 | 2026-03-03T08:06:18 | Turbulent_Pie_8135 | false | null | 0 | o8dlczy | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o8dlczy/ | false | 1 |
t1_o8dlcqx | Yeah this happens with a lot of thinking models. | 1 | 0 | 2026-03-03T08:06:13 | FuzzyLogick | false | null | 0 | o8dlcqx | false | /r/LocalLLaMA/comments/1rj8x1q/qwen35_4b_overthinking_to_say_hello/o8dlcqx/ | false | 1 |
t1_o8dlbsf | Ah interesting, so vision models even without using the vision portion, just text are better than non-vision if you had the same model size for example? | 1 | 0 | 2026-03-03T08:05:58 | rorowhat | false | null | 0 | o8dlbsf | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8dlbsf/ | false | 1 |
t1_o8dlaaw | I have the same, did you resolve it ?
| 1 | 0 | 2026-03-03T08:05:34 | Odd-Ad8796 | false | null | 0 | o8dlaaw | false | /r/LocalLLaMA/comments/1re64fe/qwen35_thinking_blocks_in_output/o8dlaaw/ | false | 1 |
t1_o8dl862 | I tried the 4B and 9B models and honestly, they are the weakest models I’ve ever used. Their instruction-following and reasoning abilities are poor. Even when I specifically asked for JSON output, they failed to understand correctly. They struggle with normal logical thinking.
On the other hand, I tested the Qwen 3 4B Instruct model, and it performed much better than the newer Qwen 3.5 4B. This is a serious issue benchmark scores alone don’t reflect real-world usability. Just because a model performs well in benchmarks doesn’t mean it will actually be good in practice.
I’m very disappointed with Qwen because the results don’t match expectations. | 1 | 0 | 2026-03-03T08:05:00 | Turbulent_Pie_8135 | false | null | 0 | o8dl862 | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o8dl862/ | false | 1 |
t1_o8dl6oi | This was a solid read, appreciate the real-world context. | 1 | 0 | 2026-03-03T08:04:37 | Interesting_Lie_9231 | false | null | 0 | o8dl6oi | false | /r/LocalLLaMA/comments/1riw6kd/mcp_colocation_stdio_49ms_single_client_vs_http/o8dl6oi/ | false | 1 |
t1_o8dl59m | Thank you for understanding my point of view. From the responses to my post, I’ve realized that this is not the right community to discuss these topics. It seems that many members lack the necessary background knowledge and are more casual AI enthusiasts than informed participants. | 1 | 0 | 2026-03-03T08:04:15 | CapitalShake3085 | false | null | 0 | o8dl59m | false | /r/LocalLLaMA/comments/1rj8x1q/qwen35_4b_overthinking_to_say_hello/o8dl59m/ | false | 1 |
t1_o8dl4ki | There are so many OCR / document understanding models out there, here is my personal OCR list I try to keep up to date:
GOT-OCR:
https://huggingface.co/stepfun-ai/GOT-OCR2_0
granite-docling-258m:
https://huggingface.co/ibm-granite/granite-docling-258M
MinerU 2.5:
https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B
OCRFlux:
https://huggingface.co/ChatDOC/OCRFlux-3B
MonkeyOCR-pro:
1.2B:
https://huggingface.co/echo840/MonkeyOCR-pro-1.2B
3B:
https://huggingface.co/echo840/MonkeyOCR-pro-3B
FastVLM:
0.5B:
https://huggingface.co/apple/FastVLM-0.5B
1.5B:
https://huggingface.co/apple/FastVLM-1.5B
7B:
https://huggingface.co/apple/FastVLM-7B
MiniCPM-V-4_5:
https://huggingface.co/openbmb/MiniCPM-V-4_5
GLM-4.1V-9B:
https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking
InternVL3_5:
4B:
https://huggingface.co/OpenGVLab/InternVL3_5-4B
8B:
https://huggingface.co/OpenGVLab/InternVL3_5-8B
AIDC-AI/Ovis2.5
2B:
https://huggingface.co/AIDC-AI/Ovis2.5-2B
9B:
https://huggingface.co/AIDC-AI/Ovis2.5-9B
RolmOCR:
https://huggingface.co/reducto/RolmOCR
Nanonets OCR:
https://huggingface.co/nanonets/Nanonets-OCR2-3B
dots OCR:
https://huggingface.co/rednote-hilab/dots.ocr
https://modelscope.cn/models/rednote-hilab/dots.ocr-1.5
olmocr 2:
https://huggingface.co/allenai/olmOCR-2-7B-1025
Light-On-OCR:
https://huggingface.co/lightonai/LightOnOCR-2-1B
Chandra:
https://huggingface.co/datalab-to/chandra
GLM 4.6V Flash:
https://huggingface.co/zai-org/GLM-4.6V-Flash
Jina vlm:
https://huggingface.co/jinaai/jina-vlm
HunyuanOCR:
https://huggingface.co/tencent/HunyuanOCR
bytedance Dolphin 2:
https://huggingface.co/ByteDance/Dolphin-v2
PaddleOCR-VL:
https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5
Deepseek OCR 2:
https://huggingface.co/deepseek-ai/DeepSeek-OCR-2
GLM OCR:
https://huggingface.co/zai-org/GLM-OCR
Nemotron OCR:
https://huggingface.co/nvidia/nemotron-ocr-v1 | 1 | 0 | 2026-03-03T08:04:04 | Mkengine | false | null | 0 | o8dl4ki | false | /r/LocalLLaMA/comments/1rivzcl/qwen_35_2b_is_an_ocr_beast/o8dl4ki/ | false | 1 |
t1_o8dl40m | How the hell a 27b dense model is better than the 122b a10b???? | 1 | 0 | 2026-03-03T08:03:55 | Significant_Fig_7581 | false | null | 0 | o8dl40m | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o8dl40m/ | false | 1 |
t1_o8dkwrw | That is good to know! TBH, I didn't know what Open Weights meant. And thank you for that breakdown! | 1 | 0 | 2026-03-03T08:02:00 | Odd-Aside456 | false | null | 0 | o8dkwrw | false | /r/LocalLLaMA/comments/1rjjcyk/still_a_noob_is_anyone_actually_running_the/o8dkwrw/ | false | 1 |
t1_o8dkw73 | Most frontends discard the CoT from context | 1 | 0 | 2026-03-03T08:01:52 | MerePotato | false | null | 0 | o8dkw73 | false | /r/LocalLLaMA/comments/1rj8x1q/qwen35_4b_overthinking_to_say_hello/o8dkw73/ | false | 1 |
t1_o8dkw83 | It likely can. You probably should try. | 1 | 0 | 2026-03-03T08:01:52 | kaisurniwurer | false | null | 0 | o8dkw83 | false | /r/LocalLLaMA/comments/1rj6m71/qwen_35_27b_a_testament_to_the_transformer/o8dkw83/ | false | 1 |
t1_o8dkt2t | Also 27b is slow! | 1 | 0 | 2026-03-03T08:01:04 | johakine | false | null | 0 | o8dkt2t | false | /r/LocalLLaMA/comments/1rj9bl7/api_price_for_the_27b_qwen_35_is_just_outrageous/o8dkt2t/ | false | 1 |
t1_o8dksho | Your app is dope! 😎 One question: how can I use it as an OpenAI-compatible API provider like llama.cpp or Ollama? | 1 | 0 | 2026-03-03T08:00:55 | RIP26770 | false | null | 0 | o8dksho | false | /r/LocalLLaMA/comments/1rjec8a/qwen35_on_a_mid_tier_300_android_phone/o8dksho/ | false | 1 |
t1_o8dks2h | Use Qwen3-Coder-Next and wait for Qwen3.5 coder | 1 | 0 | 2026-03-03T08:00:49 | ExistingAd2066 | false | null | 0 | o8dks2h | false | /r/LocalLLaMA/comments/1rjg5qm/qwen3535ba3b_vs_qwen3_coder_30b_a3b_instruct_for/o8dks2h/ | false | 1 |
t1_o8dkq42 | PocketPal works well and download models from HuggingFace. I have no relationship with the app aside from using it occasionally. | 1 | 0 | 2026-03-03T08:00:18 | nonother | false | null | 0 | o8dkq42 | false | /r/LocalLLaMA/comments/1rjec8a/qwen35_on_a_mid_tier_300_android_phone/o8dkq42/ | false | 1 |
t1_o8dknzr | Thanks for sharing.
Great finding, and would be fantastic if this holds true with larger/deeper model. I'm digging into this. | 1 | 0 | 2026-03-03T07:59:44 | NandaVegg | false | null | 0 | o8dknzr | false | /r/LocalLLaMA/comments/1rj2y4n/you_can_monitor_lora_training_quality_without/o8dknzr/ | false | 1 |
t1_o8dklks | For Qwen3.5 35B we won't be updating the exisiting ones anymore. We will only be uploading new ones we didn't before e.g. Q8\_0.
For the rest we will be however, especially for the tool-calling fixes | 1 | 0 | 2026-03-03T07:59:07 | yoracale | false | null | 0 | o8dklks | false | /r/LocalLLaMA/comments/1rjh5wg/unsloth_fixed_version_of_qwen3535ba3b_is/o8dklks/ | false | 1 |
t1_o8dkh7t | How straightforward is llamacpp to setup and run? Genuine question before I dive in. One of the biggest selling points of Ollama is the convenience of it. Oneliner to setup, openai server completion format OOTB, great integration with many AI apps etc | 1 | 0 | 2026-03-03T07:57:59 | Bac-Te | false | null | 0 | o8dkh7t | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8dkh7t/ | false | 1 |
t1_o8dkh5j | It's not the top rec due to the login requirements, I'm in the same boat, it only starts the service after login | 1 | 0 | 2026-03-03T07:57:58 | Everlier | false | null | 0 | o8dkh5j | false | /r/LocalLLaMA/comments/1rjh5wg/unsloth_fixed_version_of_qwen3535ba3b_is/o8dkh5j/ | false | 1 |
t1_o8dkfid | Added this post to my f ollama copypasta (saved as a snippet in raycast for convenience, requesting everyone to save and share this everywhere you see ollama. Case in point - if you Ask reddit (the feature in the search) whats the recommended way to run local AI, it still has Ollama at the top, despite the fact that we've been shitting on it in this sub non-stop for the better part of the past year)
The snippet
Use llama.cpp - the library they ripped off.
https://old.reddit.com/r/LocalLLaMA/comments/1pvjpmb/why_i_quit_using_ollama/
https://old.reddit.com/r/LocalLLaMA/comments/1mncrqp/ollama/
https://old.reddit.com/r/LocalLLaMA/comments/1ko1iob/ollama_violating_llamacpp_license_for_over_a_year/ | 1 | 0 | 2026-03-03T07:57:32 | rm-rf-rm | false | null | 0 | o8dkfid | false | /r/LocalLLaMA/comments/1rjb7yk/psa_if_you_want_to_test_new_models_use/o8dkfid/ | false | 1 |
t1_o8dke0i | >It's passing my reasoning and knowledge tests roughly at the level of R1 0528.
Mine are pretty heavily weighted to the humanities and soft sciences so if that's a weak point it's easy to drag a model down. But it did pretty poorly for me. Gemma 3 27b beat it in every category by a pretty good margin. That said, I am VERY grateful to have another dense model at that size with distinct output from the rest. | 1 | 0 | 2026-03-03T07:57:09 | toothpastespiders | false | null | 0 | o8dke0i | false | /r/LocalLLaMA/comments/1rj6m71/qwen_35_27b_a_testament_to_the_transformer/o8dke0i/ | false | 1 |
t1_o8dkcdm | pls do not confuse "open source" and "open weights" models, Kimi is open weights not open source, because you have access to its weights only and do not have access to its training materials so you could not build the same LLM "from source".
to run it at home at about 10 tokens per second, which is usable for single requests only, you'll need about $30k hardware, to run it for long complex tasks like agentic coding you'll need about $200k hardware which is not "local" as in "at home" but still "local" as in "on premises for a company". | 1 | 0 | 2026-03-03T07:56:42 | MelodicRecognition7 | false | null | 0 | o8dkcdm | false | /r/LocalLLaMA/comments/1rjjcyk/still_a_noob_is_anyone_actually_running_the/o8dkcdm/ | false | 1 |
t1_o8dkate | That’s some real useful performance you have there. Thanks for sharing the details of your setup. | 1 | 0 | 2026-03-03T07:56:17 | Beautiful-Honeydew10 | false | null | 0 | o8dkate | false | /r/LocalLLaMA/comments/1rianwb/running_qwen35_27b_dense_with_170k_context_at/o8dkate/ | false | 1 |
t1_o8dk645 | I'm mostly looking for ones that are 600 - 700 rn, but I can't buy for a couple weeks | 1 | 0 | 2026-03-03T07:55:05 | Lord_Curtis | false | null | 0 | o8dk645 | false | /r/LocalLLaMA/comments/1rj8zhq/where_can_i_get_good_priced_3090s/o8dk645/ | false | 1 |
t1_o8dk2qm | Hm.. interesting. I couldn't compile it due to a missing file. | 1 | 0 | 2026-03-03T07:54:15 | HighFlyingB1rd | false | null | 0 | o8dk2qm | false | /r/LocalLLaMA/comments/1rj5ngc/running_qwen3508b_on_my_7yearold_samsung_s10e/o8dk2qm/ | false | 1 |
t1_o8dk0k7 | doctor will also say "hi" without thinking, actually saying hi before starting new dialogue is quite common and polite.
So now we are pretending that bug is a feature?! Imagine all of these models like GTP, or GEMINI would behave the same way as qwen3.5, what would be the cost of response? Take older model like open source gtp-oss:20B response for such questions is almost instant, even qwen3 is a speed of light compared to this one however deep reasoning maybe little bit weaker then qwen 3.5. That's so funny when you try to push this behavior as a feature. You are gaining few percentage of advantage compared to older models with with 50% slower response and 50% more energy used, very questionable tradeoff. | 1 | 0 | 2026-03-03T07:53:40 | Specialist-Chain-369 | false | null | 0 | o8dk0k7 | false | /r/LocalLLaMA/comments/1rj8x1q/qwen35_4b_overthinking_to_say_hello/o8dk0k7/ | false | 1 |
t1_o8djyyr | Reading docs say yes | 1 | 0 | 2026-03-03T07:53:17 | Holiday_Purpose_3166 | false | null | 0 | o8djyyr | false | /r/LocalLLaMA/comments/1rit2fy/reverted_from_qwen35_27b_back_to_qwen3_8b/o8djyyr/ | false | 1 |
t1_o8djwjb | Haha yeah I stopped going to parties :/ | 1 | 0 | 2026-03-03T07:52:39 | Icy-Degree6161 | false | null | 0 | o8djwjb | false | /r/LocalLLaMA/comments/1rj8x1q/qwen35_4b_overthinking_to_say_hello/o8djwjb/ | false | 1 |
t1_o8djror | I mean, the whole goal of multi-agent system is that eventually, the whole group of agents would converges toward the completion of the task you requested.What I usually see is that the orchestrator loses control of the agents (the AutoGen example), or the agents just loop without finishing. But the worst case is that everyone works fine within their own context, but together as a system, they loop around and override each other, rather than converging toward task completion.
A few weeks ago I saw an empirical study on LLM multi-agent in software engineering. The study found that functional accuracy (test pass rate, essentially) actually drops in multi-agent design. However, there was small improvement in non-quality attributes like code quality. I think the day when the big labs manage to solve this multi-agent coordination problem (like really solving it, not barely working), it's going to be a major change. Until then, multi-agent is fun to look at, but you need to keep an eye on them.
Good point about using different models for different agents, btw. | 1 | 0 | 2026-03-03T07:51:23 | o0genesis0o | false | null | 0 | o8djror | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8djror/ | false | 1 |
t1_o8djo6y | In which way are you running inference and on which OS? Are you using docker or the strix halo toolboxes or self compiled binaries?
I am also playing around with different quants of the 35b-a3b, but found radv to be the fastest (and rocm 7.2 tanking completely).
Would love to get more insight into how others run inference on the strix halo platform. | 1 | 0 | 2026-03-03T07:50:30 | Frequent-Noise-8129 | false | null | 0 | o8djo6y | false | /r/LocalLLaMA/comments/1rjh5wg/unsloth_fixed_version_of_qwen3535ba3b_is/o8djo6y/ | false | 1 |
t1_o8djnmy | Probably using Q2 with Ollama 🤡 | 1 | 0 | 2026-03-03T07:50:21 | TacGibs | false | null | 0 | o8djnmy | false | /r/LocalLLaMA/comments/1rji5bc/how_do_the_small_qwen35_models_compare_to_the/o8djnmy/ | false | 1 |
t1_o8djhn8 | very interesting take 🤔 | 1 | 0 | 2026-03-03T07:48:48 | Old-Sherbert-4495 | false | null | 0 | o8djhn8 | false | /r/LocalLLaMA/comments/1rjff88/how_do_i_get_the_best_speed_out_of_qwen_35_9b_in/o8djhn8/ | false | 1 |
t1_o8dje2u | So what’s the verdict folks, which quant/repo is the best for coding? Thanks | 1 | 0 | 2026-03-03T07:47:54 | JaatGuru | false | null | 0 | o8dje2u | false | /r/LocalLLaMA/comments/1rf9dey/running_qwen_35_122b_with_72gb_of_vram_setup_and/o8dje2u/ | false | 1 |
t1_o8djdmh | that's above my pay grade tbh. i just downloaded from the releases. but from what I've read, i guess it only could get it a little optimized, I'd love to know more if thats not the case. also the cache improves the prompt processing speed at a marginal cost, so i could keep it anyways. | 1 | 0 | 2026-03-03T07:47:47 | Old-Sherbert-4495 | false | null | 0 | o8djdmh | false | /r/LocalLLaMA/comments/1rjff88/how_do_i_get_the_best_speed_out_of_qwen_35_9b_in/o8djdmh/ | false | 1 |
t1_o8djcr4 | Do you have any guides or resources on setting up qwen locally or are you just following the GitHub? I also have 6gb vram, 1660ti I think, and I was wondering if that slows down other processes on ur PC and what kind of latency you are getting | 1 | 0 | 2026-03-03T07:47:33 | lasagna_lee | false | null | 0 | o8djcr4 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o8djcr4/ | false | 1 |
t1_o8djava | May I ask I which cases is 9B better than 35B A3B model? | 1 | 0 | 2026-03-03T07:47:04 | soyalemujica | false | null | 0 | o8djava | false | /r/LocalLLaMA/comments/1rjff88/how_do_i_get_the_best_speed_out_of_qwen_35_9b_in/o8djava/ | false | 1 |
t1_o8dja70 | in open webui, you can create presets as a single drop down option. it just appears as 2 llm choices for the user. | 1 | 0 | 2026-03-03T07:46:53 | DeltaSqueezer | false | null | 0 | o8dja70 | false | /r/LocalLLaMA/comments/1rj8sow/llamacpp_models_preset_with_multiple_presets_for/o8dja70/ | false | 1 |
t1_o8dj9qf | Can I have my ozone and environments back? | 1 | 0 | 2026-03-03T07:46:46 | klipseracer | false | null | 0 | o8dj9qf | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8dj9qf/ | false | 1 |
t1_o8dj6iq | ctx-size doesn't change | 1 | 0 | 2026-03-03T07:45:56 | DeltaSqueezer | false | null | 0 | o8dj6iq | false | /r/LocalLLaMA/comments/1rj8sow/llamacpp_models_preset_with_multiple_presets_for/o8dj6iq/ | false | 1 |
t1_o8diydl | you using the wrong model... gotta pay attention in class kiddo. there is 2 versions | 1 | 0 | 2026-03-03T07:43:51 | CrypticZombies | false | null | 0 | o8diydl | false | /r/LocalLLaMA/comments/1rjcqm5/qwen_35_4b_is_scary_smart/o8diydl/ | false | 1 |
t1_o8dixn6 | what tools are you using？ I am interested | 1 | 0 | 2026-03-03T07:43:39 | shing3232 | false | null | 0 | o8dixn6 | false | /r/LocalLLaMA/comments/1rjfvfx/qwen3535b_is_very_resourceful_web_search_wasnt/o8dixn6/ | false | 1 |
t1_o8dixng | List so far:
- cognee
- memori
- memU
- mubit
- memgpt/letta
- memobase
Can definitely see a pattern here with the names lol | 1 | 0 | 2026-03-03T07:43:39 | selund1 | false | null | 0 | o8dixng | false | /r/LocalLLaMA/comments/1rin5r2/what_memory_systems_should_i_benchmark/o8dixng/ | false | 1 |
t1_o8diw0n | It's actually another ape typing in the zoo | 1 | 0 | 2026-03-03T07:43:14 | TheLexoPlexx | false | null | 0 | o8diw0n | false | /r/LocalLLaMA/comments/1rizodv/running_qwen_35_08b_locally_in_the_browser_on/o8diw0n/ | false | 1 |
t1_o8diupr | The speed is about what I expect of them, but qwen 3.5 is much smarter.
Granite 4,0 H isn't obsolete as it can run nicer on edge hardware and their LLMs are ISO certified which is important for safety compliance for some fields.
Also, why fix what isn't broken? If you use something which works well enough, replacing it can be more of a hassle. | 1 | 0 | 2026-03-03T07:42:54 | Kahvana | false | null | 0 | o8diupr | false | /r/LocalLLaMA/comments/1rji5bc/how_do_the_small_qwen35_models_compare_to_the/o8diupr/ | false | 1 |
t1_o8diu1s | sorry can't answer your exact question but could give some good advice for better results:
- do not use Ollama
- do not quantize KV cache
- do not use quantized multimedia projector file | 1 | 0 | 2026-03-03T07:42:44 | MelodicRecognition7 | false | null | 0 | o8diu1s | false | /r/LocalLLaMA/comments/1rjikwz/help_me_create_my_llm_ecosystem/o8diu1s/ | false | 1 |
t1_o8dis8q | +1 for RustDesk. Only used it for a few months on 10 year old Win10 system but it worked quite well for my needs | 1 | 0 | 2026-03-03T07:42:16 | layer4down | false | null | 0 | o8dis8q | false | /r/LocalLLaMA/comments/1rjh5wg/unsloth_fixed_version_of_qwen3535ba3b_is/o8dis8q/ | false | 1 |
t1_o8dirxc | Will they release base model? I only see Base for the smaller ones :( | 1 | 0 | 2026-03-03T07:42:11 | nnxnnx | false | null | 0 | o8dirxc | false | /r/LocalLLaMA/comments/1rj6m71/qwen_35_27b_a_testament_to_the_transformer/o8dirxc/ | false | 1 |
t1_o8dirul | I've seen you twice now slandering on 3.5 you really set on the agenda huh | 1 | 0 | 2026-03-03T07:42:09 | FUS3N | false | null | 0 | o8dirul | false | /r/LocalLLaMA/comments/1rjhuvq/visual_narrator_with_qwen3508b_on_webgpu/o8dirul/ | false | 1 |
t1_o8dinky | I personally use the open-source TTS model FishAudio‑S1‑mini. The audio it produces can be controlled for emotion through specific input tags or markers. | 1 | 0 | 2026-03-03T07:41:03 | Rosa-Starks | false | null | 0 | o8dinky | false | /r/LocalLLaMA/comments/1nouu70/best_open_source_tts_model_with_emotion_control/o8dinky/ | false | 1 |
t1_o8dilxh | Me? I use Kobo. | 1 | 0 | 2026-03-03T07:40:37 | TheLocalDrummer | false | null | 0 | o8dilxh | false | /r/LocalLLaMA/comments/1rjb7yk/psa_if_you_want_to_test_new_models_use/o8dilxh/ | false | 1 |
t1_o8digam | Having vision means the model was trained on images, so has more knowledge of how things look. | 1 | 0 | 2026-03-03T07:39:07 | Thomas-Lore | false | null | 0 | o8digam | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8digam/ | false | 1 |
t1_o8diesu | 3.5 27B seems to be good for text tasks, on par with Gemma 3 27B/Llama Ultra 70B, maybe even better.
All today 35B MoE variants immediately fall into looping, sadly, either on thinking or after 2-3 text blocks. | 1 | 0 | 2026-03-03T07:38:44 | Solembumm2 | false | null | 0 | o8diesu | false | /r/LocalLLaMA/comments/1rj6m71/qwen_35_27b_a_testament_to_the_transformer/o8diesu/ | false | 1 |
t1_o8di5za | thanks! you actually can use offline models out of the box for both stt and llm as they are already compatible with pipecat | 1 | 0 | 2026-03-03T07:36:25 | kuaythrone | false | null | 0 | o8di5za | false | /r/LocalLLaMA/comments/1pmhqyf/open_source_ai_voice_dictation_app_with_a_fully/o8di5za/ | false | 1 |
t1_o8di5l9 | Do you people not use llms? If you want brevity, just ask for it. | 1 | 0 | 2026-03-03T07:36:19 | Thomas-Lore | false | null | 0 | o8di5l9 | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8di5l9/ | false | 1 |
t1_o8di41w | What are you running it on, and what params? | 1 | 0 | 2026-03-03T07:35:54 | TheTerrasque | false | null | 0 | o8di41w | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8di41w/ | false | 1 |
t1_o8di1d6 | This is the first model that truly works well with opencode locally on my machine that doesn't have a GPU (but has 128GB RAM 😅) | 1 | 0 | 2026-03-03T07:35:12 | octopus_limbs | false | null | 0 | o8di1d6 | false | /r/LocalLLaMA/comments/1rj6m71/qwen_35_27b_a_testament_to_the_transformer/o8di1d6/ | false | 1 |
t1_o8dhwec | please keep the slop to yourself sir | 1 | 0 | 2026-03-03T07:33:53 | Rekkukk | false | null | 0 | o8dhwec | false | /r/LocalLLaMA/comments/1rjh5wg/unsloth_fixed_version_of_qwen3535ba3b_is/o8dhwec/ | false | 1 |
t1_o8dhvbu | Appreciate the detailed response, your wisdom and the humor of selling solutions looking for problems.
I don't understand what you mean about converging towards a goal. In my understanding, you'd want agents that are tasked with the same problem to be as cognitively diverse as possible, and adversarial.
In my own work with MoE and multiple users of the same LLM server, agent threads don't interact so MoE lead to greater throughput of a processing pipeline. | 1 | 0 | 2026-03-03T07:33:36 | PentagonUnpadded | false | null | 0 | o8dhvbu | false | /r/LocalLLaMA/comments/1rjd4pv/qwen_25_3_35_smallest_models_incredible/o8dhvbu/ | false | 1 |
t1_o8dhuqp | There's another requant coming for all unsloth Qwen3.5 models, very soon: [https://www.reddit.com/r/unsloth/comments/1riy7et/comment/o89ca46/](https://www.reddit.com/r/unsloth/comments/1riy7et/comment/o89ca46/)
So if you are on a slow or limited network connection, you might want to hold off until those land. | 1 | 0 | 2026-03-03T07:33:26 | spaceman_ | false | null | 0 | o8dhuqp | false | /r/LocalLLaMA/comments/1rjh5wg/unsloth_fixed_version_of_qwen3535ba3b_is/o8dhuqp/ | false | 1 |
t1_o8dhof0 | Its merged now, thank you for this info, tool calls now working wonderfully 🙏
On bf16 27b model and 4x3090 + driver patch i'm getting ~101t/s.
ps vllm also needs patch to use pcie bus for gpu interconnect after driver patch | 1 | 0 | 2026-03-03T07:31:46 | RS_n | false | null | 0 | o8dhof0 | false | /r/LocalLLaMA/comments/1rianwb/running_qwen35_27b_dense_with_170k_context_at/o8dhof0/ | false | 1 |
t1_o8dhmoo | I feel like it might be worth to use smaller models, like qwen3-vl 4b via vllm. You can process multiple documents at once, instead using the larger models with llama.cpp.
This allows you to illiterate through multiple documents faster during setting up your environment. You literally need to check hundreds of extractions before you can even say the thing works reliably. Hence, it's much better to have 100 extractions done in a minute in parallel instead of having a 100 sequential extractions running for 100 minutes just to end up deciding that you need to adjust the prompt.
Qwen3 4b can be quite capable. For the extraction part. I can strongly recommend it. Running it on a 3060 with 12gb right now with plenty of parallel requests and pretty decent speed. | 1 | 0 | 2026-03-03T07:31:18 | Njee_ | false | null | 0 | o8dhmoo | false | /r/LocalLLaMA/comments/1rjikwz/help_me_create_my_llm_ecosystem/o8dhmoo/ | false | 1 |
t1_o8dhl5e | 22 tps on a 4060 Ti 16GB with Q8_K_XL is actually really solid. The 4060 Ti has ~288 GB/s memory bandwidth, and that is your real ceiling for token generation. Every GB you shave off the model weight directly translates to more tokens per second. The biggest untapped win for you: Q8_K_XL is 12GB but Q5_K_M lands around 6.6GB. That frees up ~5.5GB of VRAM, which you can use to remove KV cache quantization entirely (drop --cache-type-k and --cache-type-v flags and let it use f16). Quality difference between Q8 and Q5_K_M on Qwen 3.5 is basically imperceptible for most tasks. You could realistically hit 40+ tps just by dropping the model quant. The context size not affecting speed much is expected behavior with Qwen 3.5 architecture, that part is legit. You are basically already near the ceiling for Q8 on that card. | 1 | 0 | 2026-03-03T07:30:53 | ElectricalOpinion639 | false | null | 0 | o8dhl5e | false | /r/LocalLLaMA/comments/1rjff88/how_do_i_get_the_best_speed_out_of_qwen_35_9b_in/o8dhl5e/ | false | 1 |
t1_o8dhdxd | Its hard to keep it under 3TB of models these days. | 1 | 0 | 2026-03-03T07:28:58 | lemondrops9 | false | null | 0 | o8dhdxd | false | /r/LocalLLaMA/comments/1rjg5qm/qwen3535ba3b_vs_qwen3_coder_30b_a3b_instruct_for/o8dhdxd/ | false | 1 |
t1_o8dhb3v | I speak for my VRAM-self. Only **8**GB VRAM. Granite4's Small 32B model is not fast on my VRAM as that model's Active parameters is **9**B which is bigger than my VRAM.
For example, Qwen3-30B MOE(**A3B**) giving 35-40 t/s, while Granite4-Small(**A9B**) giving me only 10-15 t/s.
If you have bigger VRAM(at least 12-16GB), that model would run faster. Anyway I still use Granite3's 8B model which's good one. | 1 | 0 | 2026-03-03T07:28:15 | pmttyji | false | null | 0 | o8dhb3v | false | /r/LocalLLaMA/comments/1rji5bc/how_do_the_small_qwen35_models_compare_to_the/o8dhb3v/ | false | 1 |
t1_o8dha62 | I was under the impression that more parameters implied less hallucination, for the models are more "grounded", and that the "ADHD" is in fact a limitation of context size, and inevitably, KV cache issues as context size is reached, and discarded, unless some kind of memory snapshotting is done to pin the answer(s). This would affect frontier models as well though. | 1 | 0 | 2026-03-03T07:28:01 | InsensitiveClown | false | null | 0 | o8dha62 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o8dha62/ | false | 1 |
t1_o8dh9vz | [removed] | 1 | 0 | 2026-03-03T07:27:56 | [deleted] | true | null | 0 | o8dh9vz | false | /r/LocalLLaMA/comments/1r7bsfd/best_audio_models_feb_2026/o8dh9vz/ | false | 1 |
t1_o8dh483 | Guess it’ll just be to fuck around with in that case | 1 | 0 | 2026-03-03T07:26:28 | tableball35 | false | null | 0 | o8dh483 | false | /r/LocalLLaMA/comments/1rjgwhm/so_with_the_new_qwen35_release_what_should_i_use/o8dh483/ | false | 1 |
t1_o8dgz14 | Unsloth released a new gguf to fix this issue earlier today. Re download. | 1 | 0 | 2026-03-03T07:25:06 | 3spky5u-oss | false | null | 0 | o8dgz14 | false | /r/LocalLLaMA/comments/1rjhy83/tool_calling_issues_with_qwen3535b_with_16gb_vram/o8dgz14/ | false | 1 |
t1_o8dgw18 | What problems do you face? Just tested it briefly, seemed to work just fine.
https://github.com/Danmoreng/local-qwen3-coder-env | 1 | 0 | 2026-03-03T07:24:19 | Danmoreng | false | null | 0 | o8dgw18 | false | /r/LocalLLaMA/comments/1rjb7yk/psa_if_you_want_to_test_new_models_use/o8dgw18/ | false | 1 |
t1_o8dgrst | Why people persist to use ollama where u can get better results and support in llama.cpp? Blows my mind | 1 | 0 | 2026-03-03T07:23:11 | sagiroth | false | null | 0 | o8dgrst | false | /r/LocalLLaMA/comments/1rjdo1i/ollama_keeps_loading_with_openclaw/o8dgrst/ | false | 1 |
t1_o8dgnqa | it depends on your exact tasts but in general you need around 500B model to replace the cloud ones, and to run 500B locally you'll need at least quad or better octo GPU setup not just dual. But for the simpler tasks even <50B model could "replace" the cloud and will work on a single or dual GPU. | 1 | 0 | 2026-03-03T07:22:09 | MelodicRecognition7 | false | null | 0 | o8dgnqa | false | /r/LocalLLaMA/comments/1rjaliw/what_llm_to_replace_claude_35_sonnet_for_server/o8dgnqa/ | false | 1 |
t1_o8dgnhy | [removed] | 1 | 0 | 2026-03-03T07:22:05 | [deleted] | true | null | 0 | o8dgnhy | false | /r/LocalLLaMA/comments/1jhiail/uncensored_image_generator/o8dgnhy/ | false | 1 |
t1_o8dgn69 | > Would Ollama + anythingLLM + qwen 3.5 (27b?) a good combo for my needs?
You could manage only with IQ4\_XS of 27B at decent t/s as it's dense model. Agree with other comment, go with 35B MOE model which is faster one. | 1 | 0 | 2026-03-03T07:22:00 | pmttyji | false | null | 0 | o8dgn69 | false | /r/LocalLLaMA/comments/1rjikwz/help_me_create_my_llm_ecosystem/o8dgn69/ | false | 1 |
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