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_o8al8rl | Thank you so much for sharing! With this 35B local beast, I am getting over 100 tps speed for even bs=1. With webdev tool and scheduler plugin, this is an agent for real! | 1 | 0 | 2026-03-02T20:46:11 | Heavy_Buyer | false | null | 0 | o8al8rl | false | /r/LocalLLaMA/comments/1rgxr0v/qwen_35_is_multimodal_here_is_how_to_enable_image/o8al8rl/ | false | 1 |
t1_o8al3br | Which 70b quantized model are you using? | 1 | 0 | 2026-03-02T20:45:27 | Frequent_Project_718 | false | null | 0 | o8al3br | false | /r/LocalLLaMA/comments/18f6sae/got_myself_a_4way_rtx_4090_rig_for_local_llm/o8al3br/ | false | 1 |
t1_o8al12y | If you run the model through hf flag it resolves the mmproj for you, if you're running from cache you have to also pass the mmproj which is in the cache as well | 1 | 0 | 2026-03-02T20:45:09 | OakShortbow | false | null | 0 | o8al12y | false | /r/LocalLLaMA/comments/1rj4ktw/qwen3535ba3b_vision_capabilties_in_llamacpp/o8al12y/ | false | 1 |
t1_o8al0j8 | Qwen 3.5 models have a draf-model included but in the case of 122B I found that it actually makes it slower, perhaps its not optimized yet, or 122B is already quite fast. But other models, for example, qwen3.5-27B, the included draft model makes it faster. | 1 | 0 | 2026-03-02T20:45:05 | ortegaalfredo | false | null | 0 | o8al0j8 | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8al0j8/ | false | 1 |
t1_o8akrf8 | You misunderstood. You still very much need the mmproj file. | 1 | 0 | 2026-03-02T20:43:50 | SarcasticBaka | false | null | 0 | o8akrf8 | false | /r/LocalLLaMA/comments/1rj4ktw/qwen3535ba3b_vision_capabilties_in_llamacpp/o8akrf8/ | false | 1 |
t1_o8akpac | How’d you get NPU support working on Linux? I thought the drivers still weren’t public from AMD. For gpt-oss-20b, you definitely shouldn’t be using a Q4_0 quant. Use the native MXFP4. FastFlowLM has some benchmarks, and with a less powerful computer they were seeing 450+ PP, which seems more in-line with what I’ve observed on Windows with my laptop. Are you sure you’re using the NPU? The PP and TG numbers being so close is suspicious. The TG seems to be right about what they were measuring. | 1 | 0 | 2026-03-02T20:43:34 | EffectiveCeilingFan | false | null | 0 | o8akpac | false | /r/LocalLLaMA/comments/1rj3i8m/strix_halo_npu_performance_compared_to_gpu_and/o8akpac/ | false | 1 |
t1_o8akoyz | Seems interesting, hope it’ll be good. Any advice for a 4070 Super? | 1 | 0 | 2026-03-02T20:43:31 | tableball35 | false | null | 0 | o8akoyz | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8akoyz/ | false | 1 |
t1_o8akkz5 | custom trained classifier models are so dead | 1 | 0 | 2026-03-02T20:42:59 | Western_Objective209 | false | null | 0 | o8akkz5 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8akkz5/ | false | 1 |
t1_o8akkyb | Nice to see NPU data like this! i do wonder how much optimization in software can improve those token rates in the future | 1 | 0 | 2026-03-02T20:42:58 | smwaqas89 | false | null | 0 | o8akkyb | false | /r/LocalLLaMA/comments/1rj3i8m/strix_halo_npu_performance_compared_to_gpu_and/o8akkyb/ | false | 1 |
t1_o8akjz6 | Maybe if you joined two years ago or so you could experience it like this. Now there is a million people here, lots of them mean and snarky. Shit's crazy, beyond control. | 1 | 0 | 2026-03-02T20:42:51 | fairydreaming | false | null | 0 | o8akjz6 | false | /r/LocalLLaMA/comments/1riy7cw/lmao/o8akjz6/ | false | 1 |
t1_o8akgvb | Thanks for the feedback, I really appreciate it! | 1 | 0 | 2026-03-02T20:42:26 | Pr0tuberanz | false | null | 0 | o8akgvb | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o8akgvb/ | false | 1 |
t1_o8akeap | That is the non-reasoning opus 4.6 | 1 | 0 | 2026-03-02T20:42:05 | Potential_Block4598 | false | null | 0 | o8akeap | false | /r/LocalLLaMA/comments/1rj3bh0/qwen35_397b_vs_27b/o8akeap/ | false | 1 |
t1_o8ak9q2 | I’m sure I was already getting close to that speed anyway. What were you getting without using 2b? | 1 | 0 | 2026-03-02T20:41:27 | And-Bee | false | null | 0 | o8ak9q2 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8ak9q2/ | false | 1 |
t1_o8ak98r | Yeah but can it be that good in a 5GB file, we’ll see.
DDR prices are so high because the memory manufacturers are booked for years for datacenter ai chips. | 1 | 0 | 2026-03-02T20:41:23 | Traditional-Card6096 | false | null | 0 | o8ak98r | false | /r/LocalLLaMA/comments/1rj39se/intelligence_density_per_gb_is_increasing_and_i/o8ak98r/ | false | 1 |
t1_o8ak983 | It just auto does alliteration, it has a bunch of prompts and it checks the layers as the model refuses those prompts, a lot like dynamic quants from unsloth. The layers that light up and say no are all lowered bit by bit in their power and the model stops throwing the refusals. It isn't a finetune, it's way easier than that. | 1 | 0 | 2026-03-02T20:41:23 | ArtfulGenie69 | false | null | 0 | o8ak983 | false | /r/LocalLLaMA/comments/1rixh53/qwen35122b_heretic_ggufs/o8ak983/ | false | 1 |
t1_o8ak4fi | [removed] | 1 | 0 | 2026-03-02T20:40:44 | [deleted] | true | null | 0 | o8ak4fi | false | /r/LocalLLaMA/comments/1rj39se/intelligence_density_per_gb_is_increasing_and_i/o8ak4fi/ | false | 1 |
t1_o8ak2xk | I feel like when I tried it I was getting 5tok/sec where I get 50+ on MLX models like OSS 120B (macOS) | 1 | 0 | 2026-03-02T20:40:32 | Virtamancer | false | null | 0 | o8ak2xk | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o8ak2xk/ | false | 1 |
t1_o8ajx38 | Can still speculate. | 1 | 0 | 2026-03-02T20:39:44 | sourceholder | false | null | 0 | o8ajx38 | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8ajx38/ | false | 1 |
t1_o8ajwp9 | Me too. Now I’m not only GPU poor but also money poor. | 1 | 0 | 2026-03-02T20:39:40 | ul90 | false | null | 0 | o8ajwp9 | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8ajwp9/ | false | 1 |
t1_o8aju9g | Flashinfer + tensor parallel, mtp disabled. | 1 | 0 | 2026-03-02T20:39:20 | Nepherpitu | false | null | 0 | o8aju9g | false | /r/LocalLLaMA/comments/1rii2pd/current_state_of_qwen35122ba10b/o8aju9g/ | false | 1 |
t1_o8ajtis | ok, will delete. | 1 | 0 | 2026-03-02T20:39:14 | alichherawalla | false | null | 0 | o8ajtis | false | /r/LocalLLaMA/comments/1rj4ee5/qwen35_on_off_grid/o8ajtis/ | false | 1 |
t1_o8ajnfq | This is not the appropriate forum for advertising your own project | 1 | 0 | 2026-03-02T20:38:25 | BumbleSlob | false | null | 0 | o8ajnfq | false | /r/LocalLLaMA/comments/1rj4ee5/qwen35_on_off_grid/o8ajnfq/ | false | 1 |
t1_o8ajlu7 | I'm sorry.
On Linux, I use Llama-Swap, and on Windows, I use Ollama. Here is my Llama-Swap configuration, if it's useful to you:
\`\`\`code
"qwen3.5-4b-q8":
description: "Qwen3.5 4B VL (Q8)"
filters:
stripParams: "temperature, top\_k, top\_p, repeat\_penalty, min\_p, presence\_penalty, chat\_template\_kwargs"
setParamsByID:
"${MODEL\_ID}-thinking":
temperature: 0.6
top\_p: 0.95
presence\_penalty: 0.0
chat\_template\_kwargs:
enable\_thinking: true
"${MODEL\_ID}-instruct":
temperature: 1.0
top\_p: 0.95
presence\_penalty: 1.5
chat\_template\_kwargs:
enable\_thinking: false
metadata:
model: "https://huggingface.co/unsloth/Qwen3.5-4B-GGUF/resolve/main/Qwen3.5-4B-Q8\_0.gguf?download=true"
mmproj: "https://huggingface.co/unsloth/Qwen3.5-4B-GGUF/resolve/main/mmproj-BF16.gguf?download=true"
ttl: 900
cmd: |
${server\_cmd}
\--model ${models\_dir}/unsloth/Qwen3.5-4B-Q8\_0.gguf
\--mmproj ${models\_dir}/unsloth/Qwen3.5-4B-Q8\_0.mmproj.gguf
\--ctx-size 262144
\--n-predict 32768
\--top-p 0.95
\--top-k 20
\--min-p 0.0
\--temp 1.0
\--repeat-penalty 1
\--image-min-tokens 1024
\`\`\` | 1 | 0 | 2026-03-02T20:38:12 | vk3r | false | null | 0 | o8ajlu7 | false | /r/LocalLLaMA/comments/1rj3ocy/question_regarding_model_parameters_and_memory/o8ajlu7/ | false | 1 |
t1_o8ajlsf | Any eta on instruct? | 1 | 0 | 2026-03-02T20:38:11 | Busy-Chemistry7747 | false | null | 0 | o8ajlsf | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8ajlsf/ | false | 1 |
t1_o8ajioz | Multi token prediction. Same basically as eagle3 spec . I am currently training one for minimax m25 | 1 | 0 | 2026-03-02T20:37:46 | getfitdotus | false | null | 0 | o8ajioz | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8ajioz/ | false | 1 |
t1_o8ajbx2 | Yeah got it thank you
I will try with the dense 27b model and share results asap
Thanks again | 1 | 0 | 2026-03-02T20:36:50 | Potential_Block4598 | false | null | 0 | o8ajbx2 | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8ajbx2/ | false | 1 |
t1_o8aj8nu | What is mtp?! | 1 | 0 | 2026-03-02T20:36:23 | Potential_Block4598 | false | null | 0 | o8aj8nu | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8aj8nu/ | false | 1 |
t1_o8aj6xz | Yeah totally agree just tried it and it is not that great (I think incomparable)
I will try with the 27B model though (since it is a “dense” model and allegedly slightly better on some benchmarks (thanks MoEs!) | 1 | 0 | 2026-03-02T20:36:08 | Potential_Block4598 | false | null | 0 | o8aj6xz | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8aj6xz/ | false | 1 |
t1_o8aj63i | [removed] | 1 | 0 | 2026-03-02T20:36:02 | [deleted] | true | null | 0 | o8aj63i | false | /r/LocalLLaMA/comments/1rj3i8m/strix_halo_npu_performance_compared_to_gpu_and/o8aj63i/ | false | 1 |
t1_o8aj66b | Yeah totally agree just tried it and it is not that great (I think incomparable)
I will try with the 27B model though (since it is a “dense” model and allegedly slightly better on some benchmarks (thanks MoEs!) | 1 | 0 | 2026-03-02T20:36:02 | Potential_Block4598 | false | null | 0 | o8aj66b | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8aj66b/ | false | 1 |
t1_o8aj2mk | How it is even work? Oo | 1 | 0 | 2026-03-02T20:35:32 | raiffuvar | false | null | 0 | o8aj2mk | false | /r/LocalLLaMA/comments/1rj2rec/new_qwen_models_for_speculative_decoding/o8aj2mk/ | false | 1 |
t1_o8aj2gd | Same here 32tkps same quant and rtx 2070 too! More than usable tbh if you ignore cloud models. | 1 | 0 | 2026-03-02T20:35:30 | sagiroth | false | null | 0 | o8aj2gd | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8aj2gd/ | false | 1 |
t1_o8aj0kt | How is that ?
I was going to run the smaller model as draft models
Could you explain more please (and I don’t mean self-speculation here tbh) | 1 | 0 | 2026-03-02T20:35:15 | Potential_Block4598 | false | null | 0 | o8aj0kt | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8aj0kt/ | false | 1 |
t1_o8aiyk3 | hahahaha | 1 | 0 | 2026-03-02T20:34:58 | EmbarrassedAsk2887 | false | null | 0 | o8aiyk3 | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8aiyk3/ | false | 1 |
t1_o8aiyg7 | I just tried out the 4b with a playwright mcp and a search interface and it did amazingly well. I've not found a really useful 4b model before. It doing great as the brains of my home assistant install right now. Turned off thinking and its very snappy, even on an amd gpu. getting 3000+ pp and 113t/s.
Using parrot instead of whisper in the stack and this feels as responsive as responsive as alexa and it can answer basic questions and has done decently at home assistant device control in my initial testing.
The entire qwen 3.5 release has really been impressive so far. | 1 | 0 | 2026-03-02T20:34:57 | ravage382 | false | null | 0 | o8aiyg7 | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8aiyg7/ | false | 1 |
t1_o8aiy7z | I think it's very capable to do it with Qwen | 1 | 0 | 2026-03-02T20:34:55 | sagiroth | false | null | 0 | o8aiy7z | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8aiy7z/ | false | 1 |
t1_o8aivjv | I just tried, 28 tps with 2B draft and ctx 32768 on 3090, llama.cpp (q4\_k\_m both 27b and 2b) | 1 | 0 | 2026-03-02T20:34:33 | Hougasej | false | null | 0 | o8aivjv | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8aivjv/ | false | 1 |
t1_o8aiv3t | Thanks! This is amazing and works with qwen3.5-9b. Is there a way to auto load a model on startup of llama-swap u/No-Statement-0001 ?
config.yaml:
includeAliasesInList: true
models:
"Qwen":
# This is the command llama-swap will use to spin up llama.cpp in the background.
cmd: >
llama-server
--port ${PORT}
--host 127.0.0.1
--model /models/Qwen.gguf
--mmproj /models/mmproj-BF16.gguf
--image-min-tokens 1024
--n-gpu-layers 99
--threads 4
--ctx-size 16576
--flash-attn on
--parallel 1
--batch-size 4096
--no-mmap
--logit-bias 151645+1
-r "<|im_end|>"
-n 2048
filters:
# Strip incoming parameters from your chat UI to enforce our optimal mode-specific settings
stripParams: "temperature, top_p, top_k, min_p, presence_penalty, repeat_penalty"
setParamsByID:
# Virtual Model 1: Standard Thinking Mode
"${MODEL_ID}:thinking":
chat_template_kwargs:
enable_thinking: true
temperature: 1.0
top_p: 0.95
top_k: 20
min_p: 0.0
presence_penalty: 1.5
repeat_penalty: 1.0
# Virtual Model 2: Instruct Mode (No Thinking)
"${MODEL_ID}:instruct":
chat_template_kwargs:
enable_thinking: false
temperature: 0.7
top_p: 0.8
top_k: 20
min_p: 0.0
presence_penalty: 1.5
repeat_penalty: 1.0
docker-compose:
version: '3.8'
services:
llama-swap:
image: ghcr.io/mostlygeek/llama-swap:cuda
container_name: llama-swap-qwen35
restart: unless-stopped
ports:
- "8880:8080" # Maps Host 8880 to Container 8080
volumes:
- /mnt/AI/models/qwen35/9b:/models
# Mount the config file into the container
- /mnt/AI/models/config.yaml:/app/config.yaml
environment:
- NVIDIA_VISIBLE_DEVICES=all
- NVIDIA_DRIVER_CAPABILITIES=all
# Instruct llama-swap to run using our config file
command: --config /app/config.yaml --listen 0.0.0.0:8080
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu] | 1 | 0 | 2026-03-02T20:34:29 | andy2na | false | null | 0 | o8aiv3t | false | /r/LocalLLaMA/comments/1rhohqk/how_to_switch_qwen_35_thinking_onoff_without/o8aiv3t/ | false | 1 |
t1_o8aiu82 | Here's my experience briefly this morning:
With an RTX 4070 (12gb VRAM), on Windows, in llama.cpp webui I'm getting ~56 t/s. `Qwen3.5-9B-UD-Q4_K_XL` uses ~8gb VRAM at 32k context; I can definitely go longer!
running on llama.cpp with the unsloth guide: https://unsloth.ai/docs/models/qwen3.5#how-to-enable-or-disable-reasoning-and-thinking
__________
here's my example powershell command to run (need `llama-server` for thinking, otherwise you can use `llama-cli` without thinking):
```
.\llama-server.exe -m ".\models\Qwen3.5-9B-UD-Q4_K_XL.gguf" -ngl 99 --ctx-size 32768 --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.00 --port 8080 --chat-template-kwargs '{"enable_thinking":true}'
``` | 1 | 0 | 2026-03-02T20:34:22 | huffalump1 | false | null | 0 | o8aiu82 | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o8aiu82/ | false | 1 |
t1_o8ais1k | 8vram 32ram, for side projects gemini, kimi, github copilot whatever is trendy. Locally Qwen 3.5 35 A3B (Q4_K_M) at 64k context and 32tkps output (62tkp read) | 1 | 0 | 2026-03-02T20:34:04 | sagiroth | false | null | 0 | o8ais1k | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8ais1k/ | false | 1 |
t1_o8aiqb2 | hahahahah tell me about it. vibe coders are the ones leading the engineers to misery by undermining what we actually do.
they dont know if engineering is actually building websites or is it to serve a service to millions and maintain them over a long period of time is actually called engineering.
jbtw here is our open source coding agent we released as well-- can be used with closed sourced llms too, or local as well.
We built it specifcailly for large codebases and not for greenfield projects. [https://github.com/SRSWTI/axe](https://github.com/SRSWTI/axe) | 1 | 0 | 2026-03-02T20:33:51 | EmbarrassedAsk2887 | false | null | 0 | o8aiqb2 | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o8aiqb2/ | false | 1 |
t1_o8aimxc | What are you guys using these models for on your phones? Genuinely curious about possibilities :) | 1 | 0 | 2026-03-02T20:33:21 | valkiii | false | null | 0 | o8aimxc | false | /r/LocalLLaMA/comments/1riv3wv/qwen_35_2b_on_android/o8aimxc/ | false | 1 |
t1_o8aimcb | Yeah, I did not refer to that heuristic because I've heard it's outdated.
Now, I still don't expect an 80B A3B model to perform as a 80B dense model, but I am surprised it barely seems better than the 30B A3B one. | 1 | 0 | 2026-03-02T20:33:16 | z_latent | false | null | 0 | o8aimcb | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o8aimcb/ | false | 1 |
t1_o8ailkp | Yes they're both english words. | 1 | 0 | 2026-03-02T20:33:10 | ImmenseFox | false | null | 0 | o8ailkp | false | /r/LocalLLaMA/comments/1riuywe/genuinely_fascinating_but_also_kind_of_terrifying/o8ailkp/ | false | 1 |
t1_o8aikym | When I was building out my home workstation (Dual 3090s) I would test potential cards by bringing a test bench or spare PC and AC power with me (my truck has 120V AC or you can bring a battery/inverter) to test with at whatever random location the sale was happening at. I would plugin the GPU and make sure it can post, had the correct details in GPU-Z, and could run inference or a game for a min or two without crapping out. I would ask if the seller was okay with on-location testing beforehand to save time/grief.
If someone doesn't want me to test their GPU its either because a) they know its broken, or b) they're afraid I'll break it testing. Either way I just say thank you and move on to the next card. I never, ever, ever, ever trusted a word anyone told me about how the GPU ran or how it was just working yesterday when they pulled it from their PC, etc. etc. | 1 | 0 | 2026-03-02T20:33:05 | RedKnightRG | false | null | 0 | o8aikym | false | /r/LocalLLaMA/comments/1rianwb/running_qwen35_27b_dense_with_170k_context_at/o8aikym/ | false | 1 |
t1_o8aikj2 | > start using axe
At first I thought you're being mean to OP, made me giggle haha | 1 | 0 | 2026-03-02T20:33:02 | Xantrk | false | null | 0 | o8aikj2 | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8aikj2/ | false | 1 |
t1_o8aik8y | I don't know if it's a language barrier or what.
I want to see real numbers you collected, not marketing. This video is useless | 1 | 0 | 2026-03-02T20:32:59 | JamesEvoAI | false | null | 0 | o8aik8y | false | /r/LocalLLaMA/comments/1riypvk/axe_a_precision_agentic_coder_large_codebases/o8aik8y/ | false | 1 |
t1_o8aifey | Hehe, so funny. The only way the ablit models can be bad is that they don't refuse anymore. Like there is no resistance to the user, but that's just in chat and can maybe be told in a system prompt to be more aggressive or whatever. In a more agent oriented system where they are a writing staff or something they can still follow directions and because they are willing to write anything you can get some really wild stuff. | 1 | 0 | 2026-03-02T20:32:19 | ArtfulGenie69 | false | null | 0 | o8aifey | false | /r/LocalLLaMA/comments/1rixh53/qwen35122b_heretic_ggufs/o8aifey/ | false | 1 |
t1_o8aib17 | Then no need for this post either. | 1 | 0 | 2026-03-02T20:31:43 | DinoAmino | false | null | 0 | o8aib17 | false | /r/LocalLLaMA/comments/1rj3kfq/im_tired/o8aib17/ | false | 1 |
t1_o8ai7qw | I look forward to seeing these claims tested side by side. I know I'm coming off as hostile but I do genuinely want competition in this space. Especially if it draws attention away from Ollama.
That said I also have an extremely sensitive bullshit meter as everyone and their dog is out here vibe coding "the next best thing" | 1 | 0 | 2026-03-02T20:31:16 | JamesEvoAI | false | null | 0 | o8ai7qw | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o8ai7qw/ | false | 1 |
t1_o8ai7eu | definitely, i was shocked to see that on artificial analysis website
| 1 | 0 | 2026-03-02T20:31:14 | SennVacan | false | null | 0 | o8ai7eu | false | /r/LocalLLaMA/comments/1rj3bh0/qwen35_397b_vs_27b/o8ai7eu/ | false | 1 |
t1_o8ai4ma | I hope I am doing something wrong that can be corrected, but I've been using larger updated quants and tweaked all the params, and the new ones will still think for 8 minutes if the question is mildly complex | 1 | 0 | 2026-03-02T20:30:51 | segfawlt | false | null | 0 | o8ai4ma | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o8ai4ma/ | false | 1 |
t1_o8ai2jm | the retrieval approach is probably the right starting point. fine-tuning on personal data has a slow feedback loop and mistakes get baked in before you notice, whereas with retrieval you can actually inspect and fix what is being pulled | 1 | 0 | 2026-03-02T20:30:34 | BC_MARO | false | null | 0 | o8ai2jm | false | /r/LocalLLaMA/comments/1rj1sbq/ai_agents_dont_have_a_context_problem_they_have_a/o8ai2jm/ | false | 1 |
t1_o8ai0q8 | glm-ocr is supposed to use together wth paddle-layout. TLDR; Clone [https://github.com/zai-org/GLM-OCR](https://github.com/zai-org/GLM-OCR) and use their SDK
glmocr parse | 1 | 0 | 2026-03-02T20:30:20 | adam444555 | false | null | 0 | o8ai0q8 | false | /r/LocalLLaMA/comments/1rivzcl/qwen_35_2b_is_an_ocr_beast/o8ai0q8/ | false | 1 |
t1_o8ahzqh | yes please. i would love to have feedbacks. face value is the most imp thing we aim for. for example here is one yt video to show its speed and tool calling capabilties. just made a quick vid to upload
[https://youtu.be/\_Dc7VZXL7xo](https://youtu.be/_Dc7VZXL7xo) | 1 | 0 | 2026-03-02T20:30:12 | EmbarrassedAsk2887 | false | null | 0 | o8ahzqh | false | /r/LocalLLaMA/comments/1riypvk/axe_a_precision_agentic_coder_large_codebases/o8ahzqh/ | false | 1 |
t1_o8ahz6b | slop on the x axis, OP's poop output on the y axis. | 1 | 0 | 2026-03-02T20:30:08 | One-Employment3759 | false | null | 0 | o8ahz6b | false | /r/LocalLLaMA/comments/1rj3kfq/im_tired/o8ahz6b/ | false | 1 |
t1_o8ahqq2 | I'll second Aider here. It's your best bet.
That being said, I think your machine is a bit short of real viability for local coding. Maybe try Qwen3-30B-Coder at IQ2? | 1 | 0 | 2026-03-02T20:29:00 | Tai9ch | false | null | 0 | o8ahqq2 | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8ahqq2/ | false | 1 |
t1_o8ahq02 | I'll give it a try if you suggest it's great. I am certain 9b will be good given Qwen 3 line up for dense models was solid. | 1 | 0 | 2026-03-02T20:28:54 | ScoreUnique | false | null | 0 | o8ahq02 | false | /r/LocalLLaMA/comments/1rirlyb/qwenqwen359b_hugging_face/o8ahq02/ | false | 1 |
t1_o8ahogl | So qwen3.5 4b failed by a lot, so didn't bother to try 2b and 0.8b. Also tried using 0.8b for speculative decoding but found no speedup so far. Might work exclusively for 27b. | 1 | 0 | 2026-03-02T20:28:42 | Windowsideplant | false | null | 0 | o8ahogl | false | /r/LocalLLaMA/comments/1ri39a4/qwen35_35b_a3b_first_small_model_to_not/o8ahogl/ | false | 1 |
t1_o8ahnki | You're probably thinking about Qwen next coder version. The one in the benchmarks was release ages ago. | 1 | 0 | 2026-03-02T20:28:35 | axiomatix | false | null | 0 | o8ahnki | false | /r/LocalLLaMA/comments/1rirtyy/qwen35_9b_and_4b_benchmarks/o8ahnki/ | false | 1 |
t1_o8ahmyu | "frightening" "terrifying"
Are you OK? | 1 | 0 | 2026-03-02T20:28:30 | Dry_Yam_4597 | false | null | 0 | o8ahmyu | false | /r/LocalLLaMA/comments/1riuywe/genuinely_fascinating_but_also_kind_of_terrifying/o8ahmyu/ | false | 1 |
t1_o8ahmxi | > their centanario model which beats the other 20b models out of proportions on BENCHMRKS
Can you provide data to back that claim up? I don't care about culture, I care about numbers and real world performance.
So far all I've gotten is a lot of words and no data. | 1 | 0 | 2026-03-02T20:28:30 | JamesEvoAI | false | null | 0 | o8ahmxi | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o8ahmxi/ | false | 1 |
t1_o8ahm5c | 4o wasn't that good, we already have multiple models under 150B that beat it including models that can run on a Mac Mini.
The models are already here, the hardware shortages are why we can't run them. It's absurd that DDR prices keep going up | 1 | 0 | 2026-03-02T20:28:24 | TokenRingAI | false | null | 0 | o8ahm5c | false | /r/LocalLLaMA/comments/1rj39se/intelligence_density_per_gb_is_increasing_and_i/o8ahm5c/ | false | 1 |
t1_o8ahl47 | Judging by your screenshot, Opus 4.6 at the Gemini 3 Flah level, seriously? If it were only in the Vision test, I might believe it, but in real-world tasks, the difference is like night and day. | 1 | 0 | 2026-03-02T20:28:15 | DenZNK | false | null | 0 | o8ahl47 | false | /r/LocalLLaMA/comments/1rj3bh0/qwen35_397b_vs_27b/o8ahl47/ | false | 1 |
t1_o8ahjpj | No idea on llamacpp but in production serving software vllm / sglang it works great can double tks | 1 | 0 | 2026-03-02T20:28:04 | getfitdotus | false | null | 0 | o8ahjpj | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8ahjpj/ | false | 1 |
t1_o8ahjcm | no 14b ? | 1 | 0 | 2026-03-02T20:28:01 | celsowm | false | null | 0 | o8ahjcm | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o8ahjcm/ | false | 1 |
t1_o8ahil5 | Since 3.5 uses MoE, drafting doesnt make that much sense | 1 | 0 | 2026-03-02T20:27:55 | TechnicSonik | false | null | 0 | o8ahil5 | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8ahil5/ | false | 1 |
t1_o8ahcdk | I'm using a `--models-preset` file, which has the following *assorted* entries - use at your own risk - I don't recall if they all work. (the `ts = 47,48` is because I have two 24GB GPUs, and GPU0 usually has a ~0.5GB of VRAM taken by a whisper model.)
I have probably used a few other settings, but I also did give this a try with vLLM last night - the rates were better, **but** it still had the large delay between the end of prompt processing and the beginning of apparent generation.
```
[Qwen3.5-27B-heretic-20k-ctx:Q4_K_M]
ctx-size = 20000
model = /home/myusername/.cache/llama.cpp/mradermacher_Qwen3.5-27B-heretic-GGUF_Qwen3.5-27B-heretic.Q4_K_M.gguf
mmproj = /home/myusername/.cache/llama.cpp/mradermacher_Qwen3.5-27B-heretic-GGUF_Qwen3.5-27B-heretic.mmproj-Q8_0.gguf
[Qwen3.5-27B-heretic-20k-ctx-ts2:Q4_K_M]
ctx-size = 20000
model = /home/myusername/.cache/llama.cpp/mradermacher_Qwen3.5-27B-heretic-GGUF_Qwen3.5-27B-heretic.Q4_K_M.gguf
mmproj = /home/myusername/.cache/llama.cpp/mradermacher_Qwen3.5-27B-heretic-GGUF_Qwen3.5-27B-heretic.mmproj-Q8_0.gguf
ts = 47,48
[Qwen3.5-27B-heretic-32k-ctx-nothink-tuned:Q4_K_M]
ctx-size = 32768
model = /home/myusername/.cache/llama.cpp/mradermacher_Qwen3.5-27B-heretic-GGUF_Qwen3.5-27B-heretic.Q4_K_M.gguf
mmproj = /home/myusername/.cache/llama.cpp/mradermacher_Qwen3.5-27B-heretic-GGUF_Qwen3.5-27B-heretic.mmproj-Q8_0.gguf
temp = 0.7
top-p = 0.8
top-k = 20
min-p = 0
presence-penalty = 1.5
repeat-penalty = 1
chat-template-kwargs = "{\"enable_thinking\": \"false\"}"
reasoning-budget = 0
[Qwen3.5-27B-heretic-32k-ctx-think-tuned:Q4_K_M]
ctx-size = 32768
model = /home/myusername/.cache/llama.cpp/mradermacher_Qwen3.5-27B-heretic-GGUF_Qwen3.5-27B-heretic.Q4_K_M.gguf
mmproj = /home/myusername/.cache/llama.cpp/mradermacher_Qwen3.5-27B-heretic-GGUF_Qwen3.5-27B-heretic.mmproj-Q8_0.gguf
temp = 1.0
top-p = 0.95
top-k = 20
min-p = 0
presence-penalty = 1.5
repeat-penalty = 1
``` | 1 | 0 | 2026-03-02T20:27:05 | overand | false | null | 0 | o8ahcdk | false | /r/LocalLLaMA/comments/1rianwb/running_qwen35_27b_dense_with_170k_context_at/o8ahcdk/ | false | 1 |
t1_o8ahc7n | Mtp is a built in draft model . | 1 | 0 | 2026-03-02T20:27:04 | getfitdotus | false | null | 0 | o8ahc7n | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8ahc7n/ | false | 1 |
t1_o8ahbek | On Ollama and LM studio using as chat, its super fast, but through Roo Code or Claude code (launched through Ollama) its just so slow, and just gives up half way through a response fairly often. | 1 | 0 | 2026-03-02T20:26:57 | JoeyJoeC | false | null | 0 | o8ahbek | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8ahbek/ | false | 1 |
t1_o8ah9q4 | not quite, I tried one shot ecommerce website with basic item listing, item details, basket, checkout. A3B performed much better | 1 | 0 | 2026-03-02T20:26:44 | sagiroth | false | null | 0 | o8ah9q4 | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o8ah9q4/ | false | 1 |
t1_o8ah5or | Is making your own benchmark particularly time-consuming? | 1 | 0 | 2026-03-02T20:26:10 | CodProfessional3712 | false | null | 0 | o8ah5or | false | /r/LocalLLaMA/comments/1rj3kfq/im_tired/o8ah5or/ | false | 1 |
t1_o8ah556 | They're not going to be running the models at q3. More likely the consideration is what memory the 8/16-bit versions fit with maximum context. | 1 | 0 | 2026-03-02T20:26:06 | Middle_Bullfrog_6173 | false | null | 0 | o8ah556 | false | /r/LocalLLaMA/comments/1rj3cku/why_qwen_35_27b/o8ah556/ | false | 1 |
t1_o8ah3hj | You didn't answer my question though. Do you have quantitative evaluations on the performance of your model?
I understand your claim that other models are benchmaxing, does that mean I should take your models quality at face value with *no supporting data*? | 1 | 0 | 2026-03-02T20:25:53 | JamesEvoAI | false | null | 0 | o8ah3hj | false | /r/LocalLLaMA/comments/1riypvk/axe_a_precision_agentic_coder_large_codebases/o8ah3hj/ | false | 1 |
t1_o8ah2sy |
Im using LMStudio and its memory estimator shows these memory requirements. Currently running Qwen 3.5 9B with only 30k context length and it already takes around 11.5gb vram. How do i configure it correctly?
Also im using UD Q4 K XL quant by unsloth | 1 | 0 | 2026-03-02T20:25:47 | IPC300 | false | null | 0 | o8ah2sy | false | /r/LocalLLaMA/comments/1rj3ocy/question_regarding_model_parameters_and_memory/o8ah2sy/ | false | 1 |
t1_o8ah2b4 | [removed] | 1 | 0 | 2026-03-02T20:25:43 | [deleted] | true | null | 0 | o8ah2b4 | false | /r/LocalLLaMA/comments/1rj3kfq/im_tired/o8ah2b4/ | false | 1 |
t1_o8ah0q1 | LOL you actually have clue what Bodega is? they have more culture and tech than any other AI labs.
For some reason, because of them i was able to run a full blown file indexer all LOCALLY built with their centanario model which beats the other 20b models out of proportions on BENCHMRKS and runs phenomenally on my 32gb m3 | 1 | 0 | 2026-03-02T20:25:30 | drip_lord007 | false | null | 0 | o8ah0q1 | false | /r/LocalLLaMA/comments/1riwhcf/psa_lm_studios_parser_silently_breaks_qwen35_tool/o8ah0q1/ | false | 1 |
t1_o8agwfu | You gotta be doing something wrong. I have 24gb pooled and I can get the first token within a few seconds with qwen3.5-27b | 1 | 0 | 2026-03-02T20:24:55 | fulgencio_batista | false | null | 0 | o8agwfu | false | /r/LocalLLaMA/comments/1rj1ni2/gpu_poor_folks16gb_whats_your_setup_for_coding/o8agwfu/ | false | 1 |
t1_o8agvhs | Yesterday, I was chatting about a situation with qwen3.5-397b. The law has changed, and it refused to believe that it's 2026 and the law has changed. It refused to answer and said I need to talk to a lawyer and CPA since I might be trying to commit fraud. That's why some of us want these models, nothing to do with roleplay. Lots of things, legal, medical, computing, politics, religion get censored... | 1 | 0 | 2026-03-02T20:24:47 | MotokoAGI | false | null | 0 | o8agvhs | false | /r/LocalLLaMA/comments/1rixh53/qwen35122b_heretic_ggufs/o8agvhs/ | false | 1 |
t1_o8agv5h | I'm using LMStudio and its memory estimator shows these memory requirements. Currently running Qwen 3.5 9B with only 30k context length and it already takes around 11.5gb vram. How do i configure it correctly? | 1 | 0 | 2026-03-02T20:24:44 | IPC300 | false | null | 0 | o8agv5h | false | /r/LocalLLaMA/comments/1rj3ocy/question_regarding_model_parameters_and_memory/o8agv5h/ | false | 1 |
t1_o8agv1r | Maybe you're already reaching the limits of the local area, qwen remains qwen, it's not Gemini 3 Pro | 1 | 0 | 2026-03-02T20:24:43 | SamLeCoyote_Fix_1 | false | null | 0 | o8agv1r | false | /r/LocalLLaMA/comments/1r9be56/i_ran_a_forensic_audit_on_my_local_ai_assistant/o8agv1r/ | false | 1 |
t1_o8agu4v | It's slop, you're replying to a spambot | 1 | 0 | 2026-03-02T20:24:36 | huffalump1 | false | null | 0 | o8agu4v | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o8agu4v/ | false | 1 |
t1_o8agsm7 | 45 minutes for 200 pages is rough. honestly on 18gb ram you are going to be pretty limited with the heavier vlm based models because they eat memory fast especially on multi page docs. the ones that fit in that footprint tend to sacrifice accuracy for speed or the other way around.
if you want to stay local, something like marker (pip install marker-pdf torch) is probably your best bet for that hardware. it runs on cpu fine and it is way faster than glmocr while still giving decent quality markdown output. for a 200 page doc you are probably looking at a few minutes instead of 45. the quality on tables is not perfect but for general text and structure it holds up pretty well.
the tricky part with local on a macbook is that you are always going to be making tradeoffs between speed and accuracy. if accuracy matters more than running locally, an api based approach will crush anything you can run on 18gb in both speed and quality. just depends on what matters more for your use case. | 1 | 0 | 2026-03-02T20:24:23 | Ok-Potential-333 | false | null | 0 | o8agsm7 | false | /r/LocalLLaMA/comments/1r0ser2/any_latest_ocr_model_i_can_run_locally_in_18gb_ram/o8agsm7/ | false | 1 |
t1_o8agqp2 | Speculative decoding isn’t nearly as useful for MoE models. Also, as far as I know, the Qwen3.5 models have a form of multi-token prediction built-in, although I don’t think it’s working yet in the most recent llama.cpp. | 1 | 0 | 2026-03-02T20:24:07 | EffectiveCeilingFan | false | null | 0 | o8agqp2 | false | /r/LocalLLaMA/comments/1rj3oue/any_advice_for_using_draft_models_with_qwen35_122b/o8agqp2/ | false | 1 |
t1_o8agoct | context + kv cache depends on model architecture. While there is some relationship with model size, there's also a lot of variability from model to model. For example, Qwen3-Coder-Next (an 80B model) needs just 10 GB for 128k, while MiniMax-M2.5 (a 229B model) needs over 100 GB for the same 128k. Less than 3x the number of parameters, but over 10x the VRAM required for context. | 1 | 0 | 2026-03-02T20:23:48 | suicidaleggroll | false | null | 0 | o8agoct | false | /r/LocalLLaMA/comments/1rj3ocy/question_regarding_model_parameters_and_memory/o8agoct/ | false | 1 |
t1_o8agk2e | i'm frustrated with the new models. try to prompt them with just: hello. they will overthink reeeeally hard | 1 | 0 | 2026-03-02T20:23:13 | asraniel | false | null | 0 | o8agk2e | false | /r/LocalLLaMA/comments/1rivckt/visualizing_all_qwen_35_vs_qwen_3_benchmarks/o8agk2e/ | false | 1 |
t1_o8agiib | Which is really weird because basically _nobody_ asked about speculative decoding with Qwen3. The sudden interest and - 4 posts about it today alone - is pretty odd yeah. | 1 | 0 | 2026-03-02T20:23:01 | DinoAmino | false | null | 0 | o8agiib | false | /r/LocalLLaMA/comments/1rj2mzy/is_speculative_decoding_available_with_the_qwen/o8agiib/ | false | 1 |
t1_o8agft7 | LMAO, you seriously haven’t tried Bodega yet? It has more culture and tech than these AI labs will ever do | 1 | 0 | 2026-03-02T20:22:39 | drip_lord007 | false | null | 0 | o8agft7 | false | /r/LocalLLaMA/comments/1riypvk/axe_a_precision_agentic_coder_large_codebases/o8agft7/ | false | 1 |
t1_o8agdta | Yeah, that works! But I still question why unsloth turned it off in their template. Thinking is **enabled** by default in the original Qwen files. | 1 | 0 | 2026-03-02T20:22:22 | thejoyofcraig | false | null | 0 | o8agdta | false | /r/LocalLLaMA/comments/1rirts9/unslothqwen354bgguf_hugging_face/o8agdta/ | false | 1 |
t1_o8agcc4 | For work stuff, I almost always have to do some cleanup afterwards, but I use the planning stage to get the overall shape of things roughly right. I don't have different types of projects at work really, we're a small team working on a data visualisation app.
I usually actually just hardly give any spec, and just say broadly what I want, let the model explore the code base itself to figure out the details. Then if there are things I don't like in there, I iterate. I find it's better to let the model understand the code itself than me to try to explain everything and possibly have misunderstandings. | 1 | 0 | 2026-03-02T20:22:10 | -dysangel- | false | null | 0 | o8agcc4 | false | /r/LocalLLaMA/comments/1rj1sbq/ai_agents_dont_have_a_context_problem_they_have_a/o8agcc4/ | false | 1 |
t1_o8agbf2 | Probably Kling | 1 | 0 | 2026-03-02T20:22:03 | nerdlord420 | false | null | 0 | o8agbf2 | false | /r/LocalLLaMA/comments/1rj326g/any_idea_what_is_being_used_for_these_generations/o8agbf2/ | false | 1 |
t1_o8ag7tu | Which is especially ironic since everything we're doing here is built on free information sharing... Everything from the models, oss frameworks, tips and techniques, etc.
Then someone uses allll of this free&open knowledge to do something insignificant and then would rather be snarky than just say what they're doing.
Yes, you don't have to post and share what you did. But it takes just as much effort to be an asshole as it does to be helpful | 1 | 0 | 2026-03-02T20:21:32 | huffalump1 | false | null | 0 | o8ag7tu | false | /r/LocalLLaMA/comments/1riwy9w/is_qwen359b_enough_for_agentic_coding/o8ag7tu/ | false | 1 |
t1_o8ag6of | beautiful | 1 | 0 | 2026-03-02T20:21:23 | LackingAGoodName | false | null | 0 | o8ag6of | false | /r/LocalLLaMA/comments/1rirlau/breaking_the_small_qwen35_models_have_been_dropped/o8ag6of/ | false | 1 |
t1_o8ag5wz | a thing of beauty. 2026 is the year ondevice Ai explodes | 1 | 0 | 2026-03-02T20:21:17 | ElectricalBar7464 | false | null | 0 | o8ag5wz | false | /r/LocalLLaMA/comments/1riv3wv/qwen_35_2b_on_android/o8ag5wz/ | false | 1 |
t1_o8ag56j | Do you know any such program, I would try it, I have an RTX 4070 for now | 1 | 0 | 2026-03-02T20:21:11 | Deep-Island5895 | false | null | 0 | o8ag56j | false | /r/LocalLLaMA/comments/1ranbod/best_local_software_for_realtime_deepfakes_face/o8ag56j/ | false | 1 |
t1_o8ag19t | It's 16GB VRAM as well, the 27B model was also a Q3 Quant which should've fit on VRAM, the 35B-A3B is loads better (not that 21 tkps is great) so will stick with that.. | 1 | 0 | 2026-03-02T20:20:38 | mrstrangedude | false | null | 0 | o8ag19t | false | /r/LocalLLaMA/comments/1q0mg6w/how_is_running_local_ai_models_on_amd_gpus_today/o8ag19t/ | false | 1 |
t1_o8afxo7 | \>>> hello
Thinking...
Hmm, the user just said “hello” with a simple lowercase. Okay, this is probably the first interaction, so they
might be testing the waters or just greeting casually.
The tone seems neutral—no urgency or specific requests yet. They might be new to chatting with AI assistants or
just starting a general conversation. Since they didn’t provide any context, I should keep my response friendly
but open-ended to encourage them to share more if they want to.
I’ll match their casual tone with a warm greeting and a prompt to keep the conversation flowing. The smiley emoji
feels appropriate here to convey approachability. Maybe they’ll respond with something specific, or this could be
the start of a longer interaction.
No need to dive deep yet—just set a positive tone and leave the door open for whatever they need.
...done thinking.
Hello! 😊 How can I help you today? | 1 | 0 | 2026-03-02T20:20:09 | CrewIndependent6042 | false | null | 0 | o8afxo7 | false | /r/LocalLLaMA/comments/1ri635s/13_months_since_the_deepseek_moment_how_far_have/o8afxo7/ | false | 1 |
t1_o8afv0q | that would be fantastic! | 1 | 0 | 2026-03-02T20:19:47 | milkipedia | false | null | 0 | o8afv0q | false | /r/LocalLLaMA/comments/1riyfg2/qwen35_model_series_thinking_onoff_does_it_matter/o8afv0q/ | false | 1 |
t1_o8afqy1 | Qwen 3.5 is using standard Multi-Head Attention (MHA) which means high KV head counts. it keeps large hidden dimensions. It stores KV cache in fp16 by default. And in VL models, it may allocate extra buffers for vision embeddings. | 1 | 0 | 2026-03-02T20:19:14 | Old-Satisfaction-420 | false | null | 0 | o8afqy1 | false | /r/LocalLLaMA/comments/1rj3ocy/question_regarding_model_parameters_and_memory/o8afqy1/ | false | 1 |
t1_o8afpod | But would 24B at q4 be so much worse than 27B at q3? | 1 | 0 | 2026-03-02T20:19:04 | dreamyrhodes | false | null | 0 | o8afpod | false | /r/LocalLLaMA/comments/1rj3cku/why_qwen_35_27b/o8afpod/ | false | 1 |
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