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_o89lv5u | to launch multiple web apps? wdym? | 1 | 0 | 2026-03-02T17:56:55 | EmbarrassedAsk2887 | false | null | 0 | o89lv5u | false | /r/LocalLLaMA/comments/1riyi54/i_am_using_qwen_ai_model_for_openclaw_and_i/o89lv5u/ | false | 1 |
t1_o89lu67 | Oh wow 0.8B version .
Good for edge devices. | 1 | 0 | 2026-03-02T17:56:47 | M-notgivingup | false | null | 0 | o89lu67 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89lu67/ | false | 1 |
t1_o89ltyk | I've been constantly bashing my head against this with the qwen3.5 models you mentioned; thank you for the exhaustive writeup and summary. I'm going to give llama.cpp a try locally and see if that fixes it. True, I'm on apple silicon so I'll sacrifice some speed but with how good these newer models are, it's not worth ... | 1 | 0 | 2026-03-02T17:56:46 | chodemunch6969 | false | null | 0 | o89ltyk | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o89ltyk/ | false | 1 |
t1_o89ltmw | How recent. I updated Llama.cpp yesterday, and it definitely solved the prompt reprocessing issue and is running perfectly. I'm just not sure about its overall agentic quality. It is great in general but sometimes seems to fall short of completing complex tasks properly. | 1 | 0 | 2026-03-02T17:56:43 | indrasmirror | false | null | 0 | o89ltmw | false | /r/LocalLLaMA/comments/1rizlkn/qwen_27b_is_a_beast_but_not_for_agentic_work/o89ltmw/ | false | 1 |
t1_o89lsu1 | You really need to set the prescense penalty just like in the qwen docs. I don’t know why unsloth doc left this setting out as it prevents the overthinking issue.
https://huggingface.co/Qwen/Qwen3.5-27B
Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition... | 1 | 0 | 2026-03-02T17:56:37 | CATLLM | false | null | 0 | o89lsu1 | false | /r/LocalLLaMA/comments/1rit2fy/reverted_from_qwen35_27b_back_to_qwen3_8b/o89lsu1/ | false | 1 |
t1_o89lrm6 | I've had similar issue using ollama | 1 | 0 | 2026-03-02T17:56:27 | Mountain-Grade-1365 | false | null | 0 | o89lrm6 | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o89lrm6/ | false | 1 |
t1_o89lp4k | Thanks! This didn't work quite right for me in openwebui, the thinking tags were not being captured and thinking process was being output along with the nonthinking. I modified it slightly so that /think in the system prompt lets the model operate without additional input (thereby enabling thinking).
[https://pastebin... | 1 | 0 | 2026-03-02T17:56:08 | No_Information9314 | false | null | 0 | o89lp4k | false | /r/LocalLLaMA/comments/1regq10/qwen_35_2735122b_jinja_template_modification/o89lp4k/ | false | 1 |
t1_o89lo7k | On what hardware? | 1 | 0 | 2026-03-02T17:56:01 | Pille5 | false | null | 0 | o89lo7k | false | /r/LocalLLaMA/comments/1rizlkn/qwen_27b_is_a_beast_but_not_for_agentic_work/o89lo7k/ | false | 1 |
t1_o89lexx | Isn't what the OP described basically training embeddings/soft-prompting? It is a legit technique that exists since late 2021 (HF PEFT started with soft prompting) but something totally forgotten past 2022 since LoRA becomes the thing. More recently it has been revisited by model-integrated embeddings look-up technique... | 1 | 0 | 2026-03-02T17:54:49 | NandaVegg | false | null | 0 | o89lexx | false | /r/LocalLLaMA/comments/1rif789/injecting_skills_into_the_kv_cache_not_as_stupid/o89lexx/ | false | 1 |
t1_o89ldzs | I only tested Kokoro and Faster Whisper, Other models are there but not fully tested. Everything local does help reduce the latency. | 1 | 0 | 2026-03-02T17:54:42 | Small-Matter25 | false | null | 0 | o89ldzs | false | /r/LocalLLaMA/comments/1rie2ww/stop_letting_your_gpu_sit_idle_make_it_answer/o89ldzs/ | false | 1 |
t1_o89ldbp | *[Recurso] Até $300 em Créditos para GPU Cloud - Vultr**
Pessoal, pra quem tá buscando GPUs acessíveis pra rodar modelos de IA:
A Vultr tá com um programa de créditos que dá até $300 pra novas contas.
Dá pra usar em:
• GPUs A100 80GB e H100 80GB
• Qualquer serviço da plataforma
... | 1 | 0 | 2026-03-02T17:54:36 | Brilliant_Treat7936 | false | null | 0 | o89ldbp | false | /r/LocalLLaMA/comments/1qoucgz/gpu_advice_for_entry_level_ai/o89ldbp/ | false | 1 |
t1_o89lcwp | do you know if a coder version will come? | 1 | 0 | 2026-03-02T17:54:33 | Impossible_Art9151 | false | null | 0 | o89lcwp | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o89lcwp/ | false | 1 |
t1_o89lcc4 | I still only use them for diffusion and small param models and mostly so use pcie vram for bigger models in my stack. Although the new qwens might worth it there now | 1 | 0 | 2026-03-02T17:54:29 | doradus_novae | false | null | 0 | o89lcc4 | false | /r/LocalLLaMA/comments/1ptakw0/2x_dgx_spark_vs_rtx_pro_6000_blackwell_for_local/o89lcc4/ | false | 1 |
t1_o89l860 | I'm on a 5090 as well and thinking about throwing it some tasks I was feeding the 35b. What have you used it on, and has it gone well? | 1 | 0 | 2026-03-02T17:53:57 | _-_David | false | null | 0 | o89l860 | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o89l860/ | false | 1 |
t1_o89l5dz | I'm just testing the BF16 version now using LM Studio (windows) version LM Studio0.4.6 (Build 1) with the Cuda12 plugin (v2.5.1) and it's behaving like an instruct model (answers straight away - I never see any think blocks).
I'm guessing something is wrong, has anyone else seen this behavior? | 1 | 0 | 2026-03-02T17:53:36 | neil_555 | false | null | 0 | o89l5dz | false | /r/LocalLLaMA/comments/1rirts9/unslothqwen354bgguf_hugging_face/o89l5dz/ | false | 1 |
t1_o89l4m7 | There's the Gemini watermark + looks like a screenshot of this thread + "turn this into a meme/comic" | 1 | 0 | 2026-03-02T17:53:30 | Mickenfox | false | null | 0 | o89l4m7 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89l4m7/ | false | 1 |
t1_o89l3dd | I compiled llama.cpp with CUDA target on Xubuntu 22.04. RTX 2060, 6GB VRAM.
35B-A3B:
./build/bin/llama-server \\
\-hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q2\_K\_XL \\
\-c 72000 \\
\-b 4092 \\
\-fit on \\
\--port 8129 \\
\--host [0.0.0.0](http://0.0.0.0) \\
\--flash-attn on \\
\--cache-type... | 1 | 0 | 2026-03-02T17:53:20 | AppealSame4367 | false | null | 0 | o89l3dd | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89l3dd/ | false | 1 |
t1_o89kzyk | Yea you're right. Looking into how to set it up correctly | 1 | 0 | 2026-03-02T17:52:53 | utsavsarkar | false | null | 0 | o89kzyk | false | /r/LocalLLaMA/comments/1riyi54/i_am_using_qwen_ai_model_for_openclaw_and_i/o89kzyk/ | false | 1 |
t1_o89kxv4 | What quant ? Context ? KV quant ?
You should use at least Q8 and BF16 cache. | 1 | 0 | 2026-03-02T17:52:37 | TacGibs | false | null | 0 | o89kxv4 | false | /r/LocalLLaMA/comments/1rizlkn/qwen_27b_is_a_beast_but_not_for_agentic_work/o89kxv4/ | false | 1 |
t1_o89kwb8 | Ha, my bad on the double reply. Will check out your summary. Always interested in seeing different approaches to the same problem. | 1 | 0 | 2026-03-02T17:52:25 | RickClaw_Dev | false | null | 0 | o89kwb8 | false | /r/LocalLLaMA/comments/1rgelk1/the_supply_chain_problem_nobody_talks_about_agent/o89kwb8/ | false | 1 |
t1_o89kw0f | Valid point. Static analysis catches the obvious stuff (known injection patterns, suspicious URLs, encoded payloads), but behavioral analysis at runtime is the harder problem. A skill that looks clean in code review can absolutely behave differently based on inputs it receives.
The static scanner is the first layer, n... | 1 | 0 | 2026-03-02T17:52:23 | RickClaw_Dev | false | null | 0 | o89kw0f | false | /r/LocalLLaMA/comments/1rgelk1/the_supply_chain_problem_nobody_talks_about_agent/o89kw0f/ | false | 1 |
t1_o89kvir | Using openclaw to launch multiple web apps | 1 | 0 | 2026-03-02T17:52:19 | utsavsarkar | false | null | 0 | o89kvir | false | /r/LocalLLaMA/comments/1riyi54/i_am_using_qwen_ai_model_for_openclaw_and_i/o89kvir/ | false | 1 |
t1_o89ktms | Using 35B A3B with latest llama.cpp with zero issues. Lightning fast. | 1 | 0 | 2026-03-02T17:52:04 | Not4Fame | false | null | 0 | o89ktms | false | /r/LocalLLaMA/comments/1rizlkn/qwen_27b_is_a_beast_but_not_for_agentic_work/o89ktms/ | false | 1 |
t1_o89ks66 | Thinking about this more...
Some of the aggressive compression techniques are likely to just destroy such subtle tuning.
If you pick a REAP model, the seldom used experts are likely where such poisoning is likely to be manifest. This will very probably be culled in a REAP. Making them dramatically safer.
This change... | 1 | 0 | 2026-03-02T17:51:53 | MaybeOk4505 | false | null | 0 | o89ks66 | false | /r/LocalLLaMA/comments/1rfg3kx/american_closed_models_vs_chinese_open_models_is/o89ks66/ | false | 1 |
t1_o89kqrc | weird comment | 1 | 0 | 2026-03-02T17:51:42 | prescorn | false | null | 0 | o89kqrc | false | /r/LocalLLaMA/comments/1rh0bkz/tempted_to_prompt_qwen_on_this_craigslist_rig_but/o89kqrc/ | false | 1 |
t1_o89ko5w | TLM | 1 | 0 | 2026-03-02T17:51:22 | SimultaneousPing | false | null | 0 | o89ko5w | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89ko5w/ | false | 1 |
t1_o89kn9k | Why was this removed? | 1 | 0 | 2026-03-02T17:51:15 | Gueleric | false | null | 0 | o89kn9k | false | /r/LocalLLaMA/comments/1rirjg1/qwen_35_small_just_dropped/o89kn9k/ | false | 1 |
t1_o89kgfg | Which model would be best for arabic? I have to run on many arabic legal documents containing tables as well. | 1 | 0 | 2026-03-02T17:50:22 | Scary-Motor-6551 | false | null | 0 | o89kgfg | false | /r/LocalLLaMA/comments/1rivzcl/qwen_35_2b_is_an_ocr_beast/o89kgfg/ | false | 1 |
t1_o89kekp | I just noticed this is 0.8B I thought it was 8B lol | 1 | 0 | 2026-03-02T17:50:08 | PhotographerUSA | false | null | 0 | o89kekp | false | /r/LocalLLaMA/comments/1rizjco/qwen3508b_released_today_speed_is_insane_157tksec/o89kekp/ | false | 1 |
t1_o89k8d1 | the qwen3-next-thinking variant is not the model that should compared against. The instruct variant is the excellent one.
Whenever I read from bad qwen3-next performance it was due to wrong model choice.
I guess many here are running the thinking variant ny accident....
| 1 | 0 | 2026-03-02T17:49:20 | Impossible_Art9151 | false | null | 0 | o89k8d1 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89k8d1/ | false | 1 |
t1_o89jz8v | > extensible up to 1,010,000 tokens
Anyone wanna do the math on how much memory that would take? | 1 | 0 | 2026-03-02T17:48:10 | MoffKalast | false | null | 0 | o89jz8v | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o89jz8v/ | false | 1 |
t1_o89jz4q | LMStudio is pretty bad at everything. you should use Bodega. | 1 | 0 | 2026-03-02T17:48:09 | EmbarrassedAsk2887 | false | null | 0 | o89jz4q | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o89jz4q/ | false | 1 |
t1_o89jxzr | Meh. I think when i tried Llama 3.2-3B awhile back on my mi50 i was getting like 170. And qwen3-4b i get like 120. And the mi50 isnt particularly powerful | 1 | 0 | 2026-03-02T17:47:59 | Schlick7 | false | null | 0 | o89jxzr | false | /r/LocalLLaMA/comments/1rizjco/qwen3508b_released_today_speed_is_insane_157tksec/o89jxzr/ | false | 1 |
t1_o89jlbx | Of course, but I never got any module to ever run at this speed. The max was 80tk/sec | 1 | 0 | 2026-03-02T17:46:18 | PhotographerUSA | false | null | 0 | o89jlbx | false | /r/LocalLLaMA/comments/1rizjco/qwen3508b_released_today_speed_is_insane_157tksec/o89jlbx/ | false | 1 |
t1_o89jl4j | Ur first and second seem fine but ur third is so slow. It feels u not taking advantage of moe, i dont use llama.cpp so cant tell u what transfers to what from lms, but im getting 27t/s at 60k/128k context on 35b at q5km from aesidai on 3060 + 32gb 5600x. Unless u using very high context lenght then mine is slow wnd urs... | 1 | 0 | 2026-03-02T17:46:16 | KURD_1_STAN | false | null | 0 | o89jl4j | false | /r/LocalLLaMA/comments/1ritcfr/imrpove_qwen35_performance_on_weak_gpu/o89jl4j/ | false | 1 |
t1_o89jinc | curious to know what you using openclaw for
| 1 | 0 | 2026-03-02T17:45:57 | EmbarrassedAsk2887 | false | null | 0 | o89jinc | false | /r/LocalLLaMA/comments/1riyi54/i_am_using_qwen_ai_model_for_openclaw_and_i/o89jinc/ | false | 1 |
t1_o89jhp8 | Well... yeah? Smaller models are dumber and faster, while bigger models are the complete opposite. | 1 | 0 | 2026-03-02T17:45:49 | HyperWinX | false | null | 0 | o89jhp8 | false | /r/LocalLLaMA/comments/1rizjco/qwen3508b_released_today_speed_is_insane_157tksec/o89jhp8/ | false | 1 |
t1_o89jfh2 | Nah, they're speaking for me | 1 | 0 | 2026-03-02T17:45:32 | PANIC_EXCEPTION | false | null | 0 | o89jfh2 | false | /r/LocalLLaMA/comments/1riy7cw/lmao/o89jfh2/ | false | 1 |
t1_o89jfie | Its a god send, on 16gb vram it runs really really well. Good tool calling, good agentic workfllow and fas as hell. (Rx 9070 xt) My brother made it work with 10 gb on his evga rtx 3080 using flash attention + kv cache quantization to q4. | 1 | 0 | 2026-03-02T17:45:32 | Suitable_Currency440 | false | null | 0 | o89jfie | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89jfie/ | false | 1 |
t1_o89jbhe | I mean we do have models even starting from 90M to 0.9b, which are amazing at tool calling and long context horizon tasks.
16GB MBA M4 is perfeclty fine for axe as well | 1 | 0 | 2026-03-02T17:45:00 | EmbarrassedAsk2887 | false | null | 0 | o89jbhe | false | /r/LocalLLaMA/comments/1riypvk/axe_a_precision_agentic_coder_large_codebases/o89jbhe/ | false | 1 |
t1_o89j7yn | I love 27B with 100K context, vision and SDXS Model all on a single 24GB card | 1 | 0 | 2026-03-02T17:44:33 | Prestigious-Use5483 | false | null | 0 | o89j7yn | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89j7yn/ | false | 1 |
t1_o89j6jd | this chart be like.. 'all colours look the same' | 1 | 0 | 2026-03-02T17:44:22 | udayalawa | false | null | 0 | o89j6jd | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89j6jd/ | false | 1 |
t1_o89j5d0 | See, I don't get the logic of this. Everyone seems to say every single model is benchmaxxed. And no one ever explains why it is *this* bar in the graph that is a lie, instead of the ones it stands next to which are all pure.
Frankly if you can "benchmaxx" on basically the entire modern suite of benchmarks, that kind of... | 1 | 0 | 2026-03-02T17:44:13 | _-_David | false | null | 0 | o89j5d0 | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o89j5d0/ | false | 1 |
t1_o89j536 | The 40× figure reflects the decode-attention stage in isolation and is primarily driven by eliminating two specific overheads in the baseline: repeated pack-to-dense operations and KV head replication under certain backends. We are not claiming that full end-to-end inference becomes 40× faster. In practice, the overall... | 1 | 0 | 2026-03-02T17:44:10 | Upset-Presentation28 | false | null | 0 | o89j536 | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89j536/ | false | 1 |
t1_o89j245 | No, you are not alone. | 1 | 0 | 2026-03-02T17:43:46 | Subject-Tea-5253 | false | null | 0 | o89j245 | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o89j245/ | false | 1 |
t1_o89iveh | The FP8 of the 27B is almost indistinguishable from the base FP16, so the answer is obvious here. Wish they had a \~50B equivalent of this model, it's so good. | 1 | 0 | 2026-03-02T17:42:53 | sgmv | false | null | 0 | o89iveh | false | /r/LocalLLaMA/comments/1riz9zz/qwen35_9b_fp16_vs_27b_fp8_have_64gb_unified_m1/o89iveh/ | false | 1 |
t1_o89it53 | Llama 2 7b is not even a coherent model tbh, only the 13B and up were ever usable at the time, and even those were pretty bad. Mistral 7B would be a more interesting comparison. | 1 | 0 | 2026-03-02T17:42:35 | MoffKalast | false | null | 0 | o89it53 | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o89it53/ | false | 1 |
t1_o89iref | I was wondering where these were at, this is exciting | 1 | 0 | 2026-03-02T17:42:22 | camracks | false | null | 0 | o89iref | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89iref/ | false | 1 |
t1_o89incp | [removed] | 1 | 0 | 2026-03-02T17:41:50 | [deleted] | true | null | 0 | o89incp | false | /r/LocalLLaMA/comments/1riyfg2/qwen35_model_series_thinking_onoff_does_it_matter/o89incp/ | false | 1 |
t1_o89im8a | I think what you explained through this post is fair, but it does not sound like there is something more to actually achieve 40x speedup or 90% VRAM reduction, unless I am missing something (please refer to Craygen9's replies). | 1 | 0 | 2026-03-02T17:41:41 | NandaVegg | false | null | 0 | o89im8a | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89im8a/ | false | 1 |
t1_o89ijkg | Real story nobody's talking about: 27B dense fits comfortably at Q8_0 in 24GB VRAM. You're getting near-full precision on consumer hardware iirc. MoE models need the same VRAM for total params but only activate a fraction. Dense means every parameter works every forward pass — that's why coherence feels better at this ... | 1 | 0 | 2026-03-02T17:41:20 | tom_mathews | false | null | 0 | o89ijkg | false | /r/LocalLLaMA/comments/1rhw16v/dense_nonthinking_moe_qwen3527b_is_blowing_me/o89ijkg/ | false | 1 |
t1_o89ie4a | That is a fair request. The current results focus on decode-time attention because that is the component being optimized, but we are now running full end-to-end generation benchmarks on real models, including the new Qwen3.5 9B, 4B, 2B, and 0.8B variants, measuring per-token latency, tokens per second, peak VRAM usage,... | 1 | 0 | 2026-03-02T17:40:36 | Upset-Presentation28 | false | null | 0 | o89ie4a | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89ie4a/ | false | 1 |
t1_o89i19j | I'm running on a Mini M2 Pro 32GB so it doesn't interfere with other LLMs on my M3 Ultra. I can use the big machine if necessary, but I would rather not.
35B (20GB) crashed the Mac at moderate load. So I switched to Qwen3 VL 8b instruct. Does Qwen 3.5 handle image/video files differently than Qwen3 VL?
Slow is OK as... | 1 | 0 | 2026-03-02T17:38:54 | zipzag | false | null | 0 | o89i19j | false | /r/LocalLLaMA/comments/1riv5kc/whats_possible_with_video_now/o89i19j/ | false | 1 |
t1_o89hzpu | Fair enough, here is the raw data that the chart is based on: [Google Sheet](https://docs.google.com/spreadsheets/d/1A5jmS7rDJe114qhRXo8CLEB3csKaFnNKsUdeCkbx_gM/edit?usp=sharing) | 1 | 0 | 2026-03-02T17:38:41 | Jobus_ | false | null | 0 | o89hzpu | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89hzpu/ | false | 1 |
t1_o89hwm8 | Decode attention is not 0.1% of inference time in long-context generation. In typical autoregressive serving workloads with growing KV caches, decode attention is often one of the dominant per-token costs once the prompt has been prefetched. We are currently adding full end-to-end inference benchmarks on real models to... | 1 | 0 | 2026-03-02T17:38:16 | Upset-Presentation28 | false | null | 0 | o89hwm8 | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89hwm8/ | false | 1 |
t1_o89hvkl | Yep, try something from the Qwen3.5 family instead, nice instructions here: https://unsloth.ai/docs/models/qwen3.5
(I like llama.cpp more than ollama, it just seems smoother/faster/easier, but consider LMStudio if you want an "easier" method) | 1 | 0 | 2026-03-02T17:38:08 | huffalump1 | false | null | 0 | o89hvkl | false | /r/LocalLLaMA/comments/1rixlj6/new_to_local_llm_which_model_to_use_with_a_4090/o89hvkl/ | false | 1 |
t1_o89huvz | What's your front end? I find it doesn't think long at all in openwebui (when attached to a tool harness). | 1 | 0 | 2026-03-02T17:38:02 | Ok-Mongoose-3614 | false | null | 0 | o89huvz | false | /r/LocalLLaMA/comments/1rit2fy/reverted_from_qwen35_27b_back_to_qwen3_8b/o89huvz/ | false | 1 |
t1_o89hr1i | i like the initiative. but what do you think about the lower end machines which actually can’t run the local llms.
i get the axe dig part where even whilst using the cloud llms it will make sure to fetch what it precisely needs but what about running locally?
for example i have a MBA 16gb m4 | 1 | 0 | 2026-03-02T17:37:32 | drip_lord007 | false | null | 0 | o89hr1i | false | /r/LocalLLaMA/comments/1riypvk/axe_a_precision_agentic_coder_large_codebases/o89hr1i/ | false | 1 |
t1_o89hkhs | Holy shit. This is my "we're there" moment.
I loaded Qwen3.5-9B-UD-Q5_K_XL in VSCodium, gave it a workspace, and it's...incredible.
It makes mistakes, but it's so fast that it can iterate on itself over and over again until it figures things out.
I've got it copying Claude Code's approach to CLAUDE.md to make it sma... | 1 | 0 | 2026-03-02T17:36:39 | No-Name-Person111 | false | null | 0 | o89hkhs | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o89hkhs/ | false | 1 |
t1_o89hi4k | Different smaller model I know, but Qwen3.5-9B runs at 40~55t/s on RTX 4070 (llama.cpp) | 1 | 0 | 2026-03-02T17:36:20 | huffalump1 | false | null | 0 | o89hi4k | false | /r/LocalLLaMA/comments/1riy5x6/qwen_35_nonthinking_mode_benchmarks/o89hi4k/ | false | 1 |
t1_o89hgru | Speak for yourself, not for others. This is a place for friendly exchange of views and observations. | 1 | 0 | 2026-03-02T17:36:09 | mossy_troll_84 | false | null | 0 | o89hgru | false | /r/LocalLLaMA/comments/1riy7cw/lmao/o89hgru/ | false | 1 |
t1_o89hd4u | [https://docs.together.ai/docs/adapter-upload#supported-base-models](https://docs.together.ai/docs/adapter-upload#supported-base-models) | 1 | 0 | 2026-03-02T17:35:40 | Legitimate_Site2320 | false | null | 0 | o89hd4u | false | /r/LocalLLaMA/comments/180igkf/paypertoken_service_with_finetuned_model_and_lora/o89hd4u/ | false | 1 |
t1_o89h7t0 | > • Start from Llama 3.1 70B or Mixtral 8x7B as teacher
thanks for asking here prior to wasting cycles on random fine-tunes of prehistoric models. My serious suggestion is to use models released at least in 2025. | 1 | 0 | 2026-03-02T17:34:57 | MelodicRecognition7 | false | null | 0 | o89h7t0 | false | /r/LocalLLaMA/comments/1riyktj/access_to_dgx_h200_looking_for_best_model_to/o89h7t0/ | false | 1 |
t1_o89h77l | Might as well just train it into the model lol | 1 | 0 | 2026-03-02T17:34:53 | CATLLM | false | null | 0 | o89h77l | false | /r/LocalLLaMA/comments/1rif789/injecting_skills_into_the_kv_cache_not_as_stupid/o89h77l/ | false | 1 |
t1_o89h78f | Thank you. Was looking for this. Got thinking loops problem too | 1 | 0 | 2026-03-02T17:34:53 | asimovreak | false | null | 0 | o89h78f | false | /r/LocalLLaMA/comments/1rit2fy/reverted_from_qwen35_27b_back_to_qwen3_8b/o89h78f/ | false | 1 |
t1_o89h6v5 | If you happy with the context go for dense highest quant model u can fit | 1 | 0 | 2026-03-02T17:34:50 | sagiroth | false | null | 0 | o89h6v5 | false | /r/LocalLLaMA/comments/1riz9zz/qwen35_9b_fp16_vs_27b_fp8_have_64gb_unified_m1/o89h6v5/ | false | 1 |
t1_o89h6lw | Will be looking forward to an abliteration | 1 | 0 | 2026-03-02T17:34:48 | FoxDeFleurs | false | null | 0 | o89h6lw | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o89h6lw/ | false | 1 |
t1_o89h5md | You can test it yourself with llamacpp. You need 128gb ram though. The speed will be ~ 15 to 20 tkps. | 1 | 0 | 2026-03-02T17:34:40 | qwen_next_gguf_when | false | null | 0 | o89h5md | false | /r/LocalLLaMA/comments/1riz0db/qwen35_397ba17b_1bit_quantization_udtq1_0_vs_27b/o89h5md/ | false | 1 |
t1_o89h4kb | 27b barely too big for my 4080, but 9b significantly too small. Wondering which one I’m better off with. | 1 | 0 | 2026-03-02T17:34:31 | ragnore | false | null | 0 | o89h4kb | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89h4kb/ | false | 1 |
t1_o89h2m6 | on a 64GB M1 Max, go with the 27B FP8. the quality jump from 9B to 27B is much bigger than the quality loss from FP16 to FP8 at that model size.
FP8 at 27B will give you near-FP16 quality with about 27GB memory footprint, leaving you plenty of headroom for context. the 9B even at FP16 just can't match the 27B's reason... | 1 | 0 | 2026-03-02T17:34:15 | Equivalent_Bed4134 | false | null | 0 | o89h2m6 | false | /r/LocalLLaMA/comments/1riz9zz/qwen35_9b_fp16_vs_27b_fp8_have_64gb_unified_m1/o89h2m6/ | false | 1 |
t1_o89gzt1 | > The script is a targeted decode-attention benchmark for paged KV caches, not a full model end-to-end benchmark, doesn't describe itself as such.
No, but it was certainly implied. Before your edit, there was *zero* mention anywhere in the post that this was a targeted benchmark of JUST the decode attention process.... | 1 | 0 | 2026-03-02T17:33:53 | suicidaleggroll | false | null | 0 | o89gzt1 | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89gzt1/ | false | 1 |
t1_o89gzjo | Yep it's just parsing, and depends on your shell; the second one is what works for me in powershell btw. | 1 | 0 | 2026-03-02T17:33:50 | huffalump1 | false | null | 0 | o89gzjo | false | /r/LocalLLaMA/comments/1rit2fy/reverted_from_qwen35_27b_back_to_qwen3_8b/o89gzjo/ | false | 1 |
t1_o89gzc1 | Sometimes things should be presented simply as a table... | 1 | 0 | 2026-03-02T17:33:49 | mtmttuan | false | null | 0 | o89gzc1 | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89gzc1/ | false | 1 |
t1_o89gvnw | The problem is supposedly with the 0.6b and 2b models, which is why they also mention that these models come with instant mode by default and recommend fine-tuning to correct it. | 1 | 0 | 2026-03-02T17:33:19 | sammoga123 | false | null | 0 | o89gvnw | false | /r/LocalLLaMA/comments/1riyfg2/qwen35_model_series_thinking_onoff_does_it_matter/o89gvnw/ | false | 1 |
t1_o89gu0m | That quant is too low to be of any practical use. Just use Minimax M2.5.
Or better yet if you want to fit entirely in the GPU then Qwen 122B is an excellent option.
If the Blackwell 6000 is priced decently then get it regardless. | 1 | 0 | 2026-03-02T17:33:05 | Monad_Maya | false | null | 0 | o89gu0m | false | /r/LocalLLaMA/comments/1riz0db/qwen35_397ba17b_1bit_quantization_udtq1_0_vs_27b/o89gu0m/ | false | 1 |
t1_o89gt3z | beware: the nightly is missing the tool call fix - you might get incorrect tool calls at times!
I'm curious, have you tried this driver? it might improve performance further! https://github.com/tinygrad/open-gpu-kernel-modules | 1 | 0 | 2026-03-02T17:32:58 | JohnTheNerd3 | false | null | 0 | o89gt3z | false | /r/LocalLLaMA/comments/1rianwb/running_qwen35_27b_dense_with_170k_context_at/o89gt3z/ | false | 1 |
t1_o89gsii | Looks like nano-banana. | 1 | 0 | 2026-03-02T17:32:53 | Bakoro | false | null | 0 | o89gsii | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89gsii/ | false | 1 |
t1_o89gsgt | You’re right that paged decode attention itself isn’t new. Systems like vLLM and FlashInfer already implement paged KV caching. What we’re adding is not “paged attention again,” but a few practical differences. We make the block traversal order an explicit, controllable parameter so you can improve locality without cha... | 1 | 0 | 2026-03-02T17:32:52 | Upset-Presentation28 | false | null | 0 | o89gsgt | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89gsgt/ | false | 1 |
t1_o89gqzq | what are your settings? | 1 | 0 | 2026-03-02T17:32:41 | lordlestar | false | null | 0 | o89gqzq | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89gqzq/ | false | 1 |
t1_o89goup | I like running large context sizes 120k to 240k and that quickly gets slow when you're offloading to cpu in my experience | 1 | 0 | 2026-03-02T17:32:24 | Certain-Cod-1404 | false | null | 0 | o89goup | false | /r/LocalLLaMA/comments/1rihhw6/questions_on_awq_vs_gguf_on_a_5090/o89goup/ | false | 1 |
t1_o89glq1 | 1 | 0 | 2026-03-02T17:31:59 | Iory1998 | false | null | 0 | o89glq1 | false | /r/LocalLLaMA/comments/1riyfg2/qwen35_model_series_thinking_onoff_does_it_matter/o89glq1/ | false | 1 | |
t1_o89gk3b | Qwen 3.5 thinking is absurd | 1 | 0 | 2026-03-02T17:31:46 | Oren_Lester | false | null | 0 | o89gk3b | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89gk3b/ | false | 1 |
t1_o89ghyd | It is incredible seeing the comparative performance of the Qwen 3.5 lineup considering the size of the models. They are punching way above their weight (pun intended). Just goes to prove that size of model isn't necessarily a direct correlation to quality. I feel that LLM model size is the new castle moat keeping playe... | 1 | 0 | 2026-03-02T17:31:29 | mrinterweb | false | null | 0 | o89ghyd | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o89ghyd/ | false | 1 |
t1_o89ghk0 | [deleted] | 1 | 0 | 2026-03-02T17:31:25 | [deleted] | true | null | 0 | o89ghk0 | false | /r/LocalLLaMA/comments/1refvmr/qwen_3_27b_is_impressive/o89ghk0/ | false | 1 |
t1_o89gguf | Why ? 20 - 40tkps is too slow for you ? | 1 | 0 | 2026-03-02T17:31:19 | qwen_next_gguf_when | false | null | 0 | o89gguf | false | /r/LocalLLaMA/comments/1rihhw6/questions_on_awq_vs_gguf_on_a_5090/o89gguf/ | false | 1 |
t1_o89g9h5 | I use 35B A3B Q6 and I flip thinking on or off depending on the task at hand, especially for chained multi tool calls I find thinking delivers more consistency | 1 | 0 | 2026-03-02T17:30:20 | Not4Fame | false | null | 0 | o89g9h5 | false | /r/LocalLLaMA/comments/1riyfg2/qwen35_model_series_thinking_onoff_does_it_matter/o89g9h5/ | false | 1 |
t1_o89g63c | Had mine on flash attention + quantized it to q8. (Rx 9070 xt): absurd level of context window, ridiculous even. | 1 | 0 | 2026-03-02T17:29:53 | Suitable_Currency440 | false | null | 0 | o89g63c | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89g63c/ | false | 1 |
t1_o89g4g5 | Okay smartass which one and what did you feed it lmao | 1 | 0 | 2026-03-02T17:29:40 | Long_comment_san | false | null | 0 | o89g4g5 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89g4g5/ | false | 1 |
t1_o89fw6y | Tiny chat is simplest. Or for example headline generation as someone here said | 1 | 0 | 2026-03-02T17:28:34 | stopbanni | false | null | 0 | o89fw6y | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89fw6y/ | false | 1 |
t1_o89fvlb | how do you disable thinking in llama.cpp? | 1 | 0 | 2026-03-02T17:28:29 | Zhelgadis | false | null | 0 | o89fvlb | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89fvlb/ | false | 1 |
t1_o89fvlz | Perhaps it might help your case to show results of a different existing benchmark, or ESPECIALLY a real-world-use case. Like, literally just an example conversation.
What is the actual, specific benefit of your work for users? | 1 | 0 | 2026-03-02T17:28:29 | huffalump1 | false | null | 0 | o89fvlz | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89fvlz/ | false | 1 |
t1_o89fvci | There isn't going to be separate instructs. They went back to a hybrid-reasoning model. It thinks by default, but you can turn it off by putting `{%- set enable_thinking = false %}` at the top of your chat template, or by adding `--reasoning-budget 0` to llama.cpp args. | 1 | 0 | 2026-03-02T17:28:27 | ayylmaonade | false | null | 0 | o89fvci | false | /r/LocalLLaMA/comments/1rivzcl/qwen_35_2b_is_an_ocr_beast/o89fvci/ | false | 1 |
t1_o89fvd1 | Models aren't just "correct" or not. It's about probabilities. You'd likely need to run dozens to hundreds of tests to see a statistically significant difference between thinking and non-thinking modes. | 1 | 0 | 2026-03-02T17:28:27 | DeProgrammer99 | false | null | 0 | o89fvd1 | false | /r/LocalLLaMA/comments/1riyfg2/qwen35_model_series_thinking_onoff_does_it_matter/o89fvd1/ | false | 1 |
t1_o89fuc4 | Agree, this family so far has been a blessing and working wonders, i would not believed if i had not tried. | 1 | 0 | 2026-03-02T17:28:19 | Suitable_Currency440 | false | null | 0 | o89fuc4 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89fuc4/ | false | 1 |
t1_o89ftud | The speed is freaking amazing when you turn off thinking. | 1 | 0 | 2026-03-02T17:28:15 | PhotographerUSA | false | null | 0 | o89ftud | false | /r/LocalLLaMA/comments/1ri60l3/qwen_35_35b_a3b_lmstudio_settings/o89ftud/ | false | 1 |
t1_o89fr8z | I don't know, I admit I did not fully read the code (it is fairly long, also the remaining codes in the repo seem unrelated from LLM inference) so I could be wrong, but paged-naive decode sounds exactly like PagedAttention or FlashInfer. Could the OP explain what is the difference from existing techniques? | 1 | 0 | 2026-03-02T17:27:53 | NandaVegg | false | null | 0 | o89fr8z | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89fr8z/ | false | 1 |
t1_o89fmte | That's strange, I've tried the 2b and 4b and they skip thinking altogether for simple queries. Using koboldcpp backend, one with a simple CLI client and the other with PicoClaw agentic client.
One problem with the latter is that for some reason it tends to reprocesses the entire context while iterating tool calls, I'm... | 1 | 0 | 2026-03-02T17:27:18 | hum_ma | false | null | 0 | o89fmte | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o89fmte/ | false | 1 |
t1_o89fjut | I don't care about upvote percentages or credit or karma. I never said it's used for full inference, every part of the post and the figure said it's a decode attention kernel that strictly replaces the **decode-time attention** step only. I wanted people to verify it on their own favourite models and see if it added an... | 1 | 0 | 2026-03-02T17:26:55 | Upset-Presentation28 | false | null | 0 | o89fjut | false | /r/LocalLLaMA/comments/1rixhj9/40_speedup_and_90_vram_reduction_on_vllms/o89fjut/ | false | 1 |
t1_o89fhtc | It worked so far amazingly well with my openclaw, better than anything before. Only cloud gigantic B numbers had same kind of performance. This 9B just slapped my qwen3-14 and gpt-oss20b on the face two times and made them sit on the bench, thats the level of disrespect. | 1 | 0 | 2026-03-02T17:26:39 | Suitable_Currency440 | false | null | 0 | o89fhtc | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o89fhtc/ | false | 1 |
t1_o89fhei | I'm finding Qwen3.5-27B-GGUF:Q4\_K\_S very capable, more so than Qwen3.5-35B-A3B-GGUF:Q6\_K. | 1 | 0 | 2026-03-02T17:26:35 | BuffMcBigHuge | false | null | 0 | o89fhei | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o89fhei/ | false | 1 |
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