AI

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Revision as of 10:19, 3 October 2025 by 192.168.1.2 (talk) (→‎Models)
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See also: AI Art

New Shit

MOE Benchmark

https://www.librechat.ai/ https://github.com/danny-avila/LibreChat - Replace open-webui

geechan - SillyTavern GLM System prompts

https://github.com/github/spec-kit https://www.youtube.com/watch?v=em3vIT9aUsg

https://joeyagreco.medium.com/reverse-engineering-the-hottest-new-game-5362cfe7c452

https://blog.plasticlabs.ai/blog/YouSim%3B-Explore-The-Multiverse-of-Identity?utm_source=chatgpt.com

https://worldsim.nousresearch.com/console

残心 / Zanshin - Navigate through media by speaker

LLM from Scratch Tutorial – Code & Train Qwen 3

miniflux-ai

glm_45_preset

simstudioai/sim Install Sim Locally with Ollama: AI Agent Workflow Builder

sdxl-emoji

gabber

RamaLama - Ollama alternative

qwen-code-cli-wrapper

Docling, DOTS OCR, and Ollama OCR

https://agents.md/

awesome-comfyui http rest

https://github.com/sakalond/StableGen - generate textures for blender

what_upscaler_is_the_best_now/

You can use `-ngl 49` and just pass `--n-cpu-moe 20`. Also add `-fa` and `-ctk q8_0 -ctv q8_0`.

PIP_BREAK_SYSTEM_PACKAGES=1 comfy install

https://overpass-turbo.eu/

Qwen-3 Coder CLI Forgets Everything. I Gave It a Perfect Memory.

https://docs.vllm.ai/en/latest/getting_started/installation/cpu.html#related-runtime-environment-variables

https://modal.com/blog/fast-cheap-batch-transcription

Make your AI Agents 10x Smarter with GraphRAG (n8n)

https://huggingface.co/rednote-hilab/dots.ocr

YOLOE: Next Gen Computer Vision - Zero Training Required!

Cipher - Cipher is an opensource memory layer specifically designed for coding agents.

https://smcleod.net/2024/12/bringing-k/v-context-quantisation-to-ollama/

Local LightRAG: A GraphRAG Alternative but Fully Local with Ollama

Graph RAG Evolved: PathRAG (Relational Reasoning Paths)

UIGEN-X-4B-0729

The Only Embedding Model You Need for RAG

https://ollama.com/library/smallthinker - Can be used as a draft model for QwQ-32B giving a %70 speed up.

sqrt(params * active) - A rule of thumb to calculate the equivalent number of parameters that a dense model would have.

Direct3D‑S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention

Make RAG 100x Better with Real-Time Knowledge Graphs

dots1

mikupad

GPT‐SoVITS‐features (各版本特性)

Are there any free working voice cloning AIs?

privategpt - privategpt imho Is the best for rag if you need the source, It not only lists the PDF used for the answer but also the page, and Is quite precise. So for studyb and search in a library Is the best i know

FLUX Model Quantization Challenge

dont_offload_gguf_layers_offload_tensors_200_gen

I Built the Ultimate RAG MCP Server for AI Coding (Better than Context7)

NEW FramePack F1 Model - Much Better Results - Bonus How to Install Sage

Qwen3-30B-A6B-16-Extreme

https://docs.google.com/document/d/12ATcyjCEKh8T-MPDZ-VMiQ1XMa9FUvvk2QazrsKoiR8/edit?tab=t.0

This AI Model has me excited about the future of Local LLM's | Qwen3-30B-A3B

https://www.reddit.com/r/LocalLLaMA/comments/1ev8n2s/exclude_top_choices_xtc_a_sampler_that_boosts/

https://blog.runpod.io/upscaling-videos-using/

koboldcpp wiki

Mixtral for Retards


koboldcpp.exe 13B-HyperMantis.ggmlv3.q4_K_M.bin --debug --usecublas --usemlock --contextsize 8192 --blasbatchsize 512 --psutil_set_threads --threads 6 --blasthreads 10 --gpulayers 5 --highpriority --stream --usemlock --unbantokens --smartcontext

If you're running 1.35 and a superHOT model, you should also add --linearrope which should make them perform better.

RP recommened models: "superhot, airoboros, wizard-vicuna, guanaco, chronos are a few commonly discussed models off the top of my head. For me, it's superhot or guanaco (one or the other, not the merge though)"

Programs

UI

jan.ai

open webui

CLI

ngpt

mcp-client-cli

mcptools - For inspecting mcp servers.

Client Name Description Key Features Implementation URL
oterm A text-based terminal client for Ollama with MCP tools, prompts, and sampling. Supports MCP tools, prompts, sampling; Streamable HTTP & WebSocket transports. TUI (Terminal UI) GitHub
ollama-mcp-client Python-based client for integrating local Ollama models with MCP servers. Seamless MCP integration, Git operations support, tool discovery. Python CLI GitHub
mcp-client-for-ollama TUI client for interacting with MCP servers using Ollama, offering interactivity. Multi-server support, streaming responses, fuzzy autocomplete. TUI (Terminal UI) GitHub
Mcp-cli General-purpose CLI for interacting with MCP servers, supporting Ollama. Supports multiple providers, modular chat, context-aware completions. Command-line Source
Mcp Client Ollama Python-based CLI for connecting Ollama to MCP servers, focusing on tool execution. stdio and SSE transports, JSON configuration, multiple server support. Python CLI Source
GUI

Tasksel

Not sure if this is common knowledge, but some advice to all fellow VRAMlets who are offloading to RAM. Setting the number of threads is not good enough, you can get extra speed by manually setting core affinity.

For context:

I have a 13600K which has 6 P-cores. I had read that you should set --threads to that number, so I would run koboldcpp with --threads 6 and from some testing this was indeed the best option with that argument alone.

BUT, I looked at which cores were actually used and found e-cores also being used sometimes.

So the next step was to set the core affinity to just P-cores. Each P-core has two threads and CPU0-11 was P-cores, CPU12-19 was E-cores. Thus, I ran koboldcpp with one thread from each core: 

taskset -c 0,2,4,6,8,10 python kobodcpp.py [args]

My speed running command-r went from ~2.3 T/s to 2.67 T/s Pretty good. But, what if I use them fully, I thought. So I set --threads 12 and taskset -c 0,1,2,3,4,5,6,7,8,9,10,11 And I get a generation with 3.09 T/s That's a whooping 33% increase from my initial. Hope this is helpful, it actually had my basedfacing captcha: pic rel

Benchmarks

LMArena

MCPMark

https://scale.com/leaderboard

https://artificialanalysis.ai/

https://livebench.ai/#/

https://eqbench.com/creative_writing.html

terminal-bench

BFCL: From Tool Use to Agentic Evaluation of Large Language Models

MTEB Leaderboard - Embedding models

Open ASR Leaderboard

📢UGI-Leaderboard - Uncensored General Intelligence

Artificial Analysis Long Context Reasoning Benchmark Leaderboard

AI Model Evaluations

Vision Benchmarks

https://huggingface.co/spaces/opencompass/open_vlm_leaderboard

https://dubesor.de/visionbench

MCP Servers

Context7

awesome-mcp-clients/

https://smithery.ai/

https://mcp.so/servers

mcptools

https://www.reddit.com/r/RooCode/comments/1ijgk2x/roo_code_mcps_best_mcp_configs/mbej58g/

Prompts

https://cookbook.openai.com/examples/enhance_your_prompts_with_meta_prompting

https://www.prompthub.us/blog/a-complete-guide-to-meta-prompting

rubric prompting

https://generateprompt.ai/en/

awesome-chatgpt-prompts

https://alidocs.dingtalk.com/i/nodes/EpGBa2Lm8aZxe5myC99MelA2WgN7R35y

https://github.com/elder-plinius/L1B3RT4S

Video

ComfyUI - Wan 2.2 & FFLF with Flux Kontext for Quick Keyframes for Video

🤿 One-Step Video Upscaling: Complete ComfyUI SeedVR2 Guide (Free workflow included) | AInVFX July 11

Upscaling Maximizing VRAM | Free ComfyUI Workflow!

Easy Creation with One Click - AI Videos - Wan cheatsheet.

TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation

Voice

Step-Audio-2-mini - an 8 billion parameter (8B) speech-to-speech model. It outperforms GPT-4o-Audio

There are other tts that are much better like Spark-tts and Higgs-tts. Keep in mind that higss tts full model with voice clone need 18gb of vram an it much slower then spark-tts

https://www.reddit.com/r/speechtech/

marvis-tts

https://github.com/nuvious/coqui-ai-api

Microsoft VibeVoice TTS LOCAL Testing – A Multi-Speaker Podcast TTS!

Dia, Fish Speech

Agentic AI Dungeon

n8n - Web tool for AI agents

chroma - Vector database

pgvector

lmstudio

LLama

/lmg/

Models

Upcoming:


Other:

Misc

lammacpp server?

"There was a tokenizer caching error, some people said. Redownload the hf_output files from the repo or just change the use_cache line in the config.json to say: "use_cache": true," for the Vicuna13B-free https://github.com/stochasticai/xturing/tree/main/examples/int4_finetuning

https://wiki.installgentoo.com/wiki/Home_server#Expanding_Your_Storage

https://rentry.org/llama-tard-v2

https://rentry.org/llamaaids

https://hackmd.io/@reneil1337/alpaca


https://find.4chan.org/?q=AI+Dynamic+Storytelling+General

https://find.4chan.org/?q=AI+Chatbot+General

https://find.4chan.org/?q=%2Flmg%2F (local models general)

https://boards.4channel.org/g/thread/92400764#p92400764

https://rentry.org/llamaaids


https://files.catbox.moe/lvefgy.json

https://pytorch.org/hub/nvidia_deeplearningexamples_tacotron2/


python server.py --model llama-7b-4bit --wbits 4

python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128

https://github.com/qwopqwop200/GPTQ-for-LLaMa/issues/59 for installing with out of space error

https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model#4-bit-mode


https://github.com/pybind/pybind11/discussions/4566

https://lmsysvicuna.miraheze.org/wiki/How_to_use_Vicuna#Use_with_llama.cpp%3A

https://huggingface.co/anon8231489123/vicuna-13b-GPTQ-4bit-128g


Here's the uncucked Vicuna model (trained on the dataset that don't have the moralistic bullshit anymore) Too bad it's just the CPU quantized version

Vicuna generating its own prompts

https://huggingface.co/TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g - python3 llama.py vicuna-AlekseyKorshuk-7B c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors vicuna-AlekseyKorshuk-7B-GPTQ-4bit-128g.safetensors


≈65% speedup of the AVX-512 implementation of ggml_vec_dot_q4_0() #933

"Speaking of which, for any 30b anons struggling with context size, I figured something out. If you use the Triton branch on WSL, go into GPTQ_loader.py and comment out make_quant_attn like so" from here


Just grab the CUDA branch of qwop's GPTQ for LLaMA (or Triton if you want to be a dickhole) or if you have webui installed, go into the folder for GPTQ. Make sure all the requirements are installed and run this line:

python llama.py /path/to30b c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors alpacino-4bit-128g.safetensors

And it'll run. For windows, obviously flip the slashes the right way. And for linux, you may need to add CUDA_VISIBLE_DEVICES=0 to the front of the command.

GGML Quantization

Some tables on Reddit

Relative quantization

Papers

Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision

http://attentionviz.com/

Local Benchmarks

TODO: Try different cublas batch sizes

Main

Older

Interface Model GPTQ Xformers? HW Load Speed
text-gen anon8231489123-vicuna-13b-GPTQ-4bit-128g GPTQ-for-LLaMa-triton yes 240gb SSD, 16gb,desktop off 10.53 7.97 tokens/s
text-gen anon8231489123-vicuna-13b-GPTQ-4bit-128g GPTQ-for-LLaMa-triton No xformers 240gb SSD, 16gb,desktop off 10.22s 7.55 tokens/s
text-gen anon8231489123-vicuna-13b-GPTQ-4bit-128g GPTQ-for-LLaMa-cuda No xformers 240gb SSD, 16gb,desktop off 16.68s 4.03 tokens/s
text-gen anon8231489123-vicuna-13b-GPTQ-4bit-128g GPTQ-for-LLaMa-cuda yes 240gb SSD, 16gb,desktop off 9.34s 4.01 tokens/s
text-gen llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml no no 2TB SSD, 64gb ? 0.67 tokens/s
text-gen llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml no no 2TB SSD, 64gb, --threads 8 maybe 30s? 0.51 tokens/s
text-gen llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml no no 2TB SSD, 64gb, --threads 7 0.68 tokens/s
text-gen llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml no no 2TB SSD, 64gb, --threads 6 0.61 tokens/s
text-gen anon8231489123-vicuna-13b-GPTQ-4bit-128g-ggml no no 2TB SSD, 64gb 1.17 tokens/s
text-gen anon8231489123-vicuna-13b-GPTQ-4bit-128g GPTQ-for-LLaMa-triton yes 2TB SSD, 64gb, --pre_layer 25 45.69 0.25 tokens/s
text-gen anon8231489123-vicuna-13b-GPTQ-4bit-128g GPTQ-for-LLaMa-triton yes 2TB SSD, 64gb 36.47 9.63 tokens/s
llama.cpp llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml 2TB SSD, 64gb 10317.90 ms 1096.21 ms per token
llama.cpp-modern-avx512 llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml 2TB SSD, 64gb 9288.69 ms 1049.03 ms per token
llama.cpp-avx512-pr833 llama-30b-sft-oa-alpaca-epoch-2-4bit-ggml 2TB SSD, 64gb 13864.06 ms 0.89 tokens/s, 820.68 ms per token
text-gen TheBloke-gpt4-alpaca-lora-30B-4bit-GGML/ggml-model-q4_0 2TB SSD, 64gb 0.78 tokens/s
text-gen+avx512-pr833 TheBloke-gpt4-alpaca-lora-30B-4bit-GGML/ggml-model-q4_0 2TB SSD, 64gb 1.04 tokens/s
2023-04-24 text-gen anon8231489123-vicuna-13b-GPTQ-4bit-128g GPTQ-for-LLaMa-triton yes 2TB SSD, 64gb, also running llama.cpp with another model 16.36 5.07 tokens/s
2023-04-26 koboldcpp gozfarb-llama-30b-supercot-ggml/ggml-model-q4_0.bin clblast n/a 2TB SSD, 64gb, --threads 8 1073ms/T
2023-04-29 koboldcpp Alpacino-30b-q4_0.bin clblast n/a 2TB SSD, 64gb 700ms/T
2023-07-13 koboldcpp llama-33b-supercot-ggml-q5_1 (complains about old format) cublas n/a 2TB SSD, 64gb, --nommap --smartcontext --usecublas --gpulayers 18 643ms/T 1.4T/s
2023-07-13 koboldcpp llama-33b-supercot-ggml-q5_1 (complains about old format) clblast n/a 2TB SSD, 64gb, --nommap --smartcontext --useclblast 0 0 --gpulayers 18 685ms/T 1.2T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --nommap --smartcontext --usecublas --gpulayers 18 (probably space for more) 652ms/T 1.5T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --nommap --smartcontext --usecublas --gpulayers 26 (I note 3 threads are set by default) 593ms/T 1.6T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --nommap --smartcontext --usecublas --gpulayers 26 --psutil _set_threads (4 threads) 514ms/T 1.8T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --smartcontext --usecublas --gpulayers 26 --psutil _set_threads (removed nommap) 508ms/T 1.9T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --smartcontext --usecublas --gpulayers 26 --threads 5 454ms/T 2.1T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --smartcontext --usecublas --gpulayers 26 --threads 6 422ms/T 2.2T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --smartcontext --usecublas --gpulayers 26 --threads 7 509ms/T 1.8T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --smartcontext --usecublas --gpulayers 26 --threads 8 494ms/T 1.7T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.2.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --smartcontext --usecublas --gpulayers 26 --threads 6 --linearrope (no difference, needs supercot?) 425ms/T 2.2T/s
2023-07-13 koboldcpp airoboros-33b-gpt4-1.4.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --smartcontext --usecublas --gpulayers 26 --threads 6 400ms/T 2.3T/s
2023-07-13 koboldcpp airoboros-65b-gpt4-1.4.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --nommap --smartcontext --usecublas --gpulayers 13 --threads 6 1366ms/T 0.7T/s
2023-07-14 koboldcpp airoboros-65b-gpt4-1.4.ggmlv3.q2_K.bin cublas n/a 2TB SSD, 64gb, --nommap --smartcontext --usecublas --gpulayers 13 --threads 6 765ms/T - 1.2T/s
2023-09-06 koboldcpp guanaco-33B.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --stream --smartcontext --usecublas --gpulayers 29 --threads 6 562ms/T - 1.3T/s
2023-09-06 koboldcpp guanaco-33B.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --nommap --stream --smartcontext --usecublas --gpulayers 29 --threads 6 567ms/T), Total:70.7s (1.4T/s
2023-09-06 koboldcpp guanaco-33B.ggmlv3.q4_K_M.bin cublas n/a 2TB SSD, 64gb, --nommap --stream --smartcontext --usecublas --gpulayers 25 --threads 6 563ms/T), Total:70.2s (1.4T/s
2023-12-03 koboldcpp guanaco-33B.q4_K_M.gguf cublas n/a 2TB SSD, 64gb, --nommap --smartcontext --usecublas --threads 6 --gpulayers 27 330.7ms/T), Total:40.79s 2.94T/s
2023-12-07 koboldcpp guanaco-33B.q4_K_M.gguf cublas n/a 7950x3d, 2TB SSD, 64gb, --nommap --smartcontext --usecublas --threads 6 --gpulayers 27 202.1ms/T, 4.78T/s
2023-12-07 koboldcpp guanaco-33B.q4_K_M.gguf cublas n/a 7950x3d, 2TB SSD, 64gb, --nommap --smartcontext --usecublas --threads 32 --gpulayers 27 360.8ms/T, 2.68T/s
2023-12-07 koboldcpp guanaco-33B.q4_K_M.gguf cublas n/a 7950x3d, 2TB SSD, 64gb, --nommap --smartcontext --usecublas --threads 16 --gpulayers 27 202.6ms/T, 4.82T/s
2023-12-07 koboldcpp guanaco-33B.q4_K_M.gguf cublas n/a 7950x3d, 2TB SSD, 64gb, --nommap --smartcontext --usecublas --threads 15 --gpulayers 27 195.0ms/T, 5.03T/s
2023-12-16 koboldcpp mistral-7b-instruct-v0.2.Q8_0.gguf cublas n/a 7950x3d, 2TB SSD, 64gb, --nommap --smartcontext --usecublas --threads 15 --gpulayers 33 22.9ms/T, 42.90T/s
2023-12-17 koboldcpp mixtral-8x7b-moe-rp-story.Q8_0.gguf cublas n/a 7950x3d, 2TB SSD, 64gb, --nommap --smartcontext --usecublas --threads 15 --gpulayers 6 214.9ms/T, 4.47T/s
2024-02-04 SillyTavern miqu 70b gpu layers 9 1.4T/s


Newer

Interface Model Notes HW Speed
2025-08-01 ollama qwen3-coder:30b-a3b-q4_K_M 17.01T/s
2025-08-01 ollama hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M 16.84T/s
2025-08-01 ollama hf.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF:Q4_K_M 14.39T/s
2025-08-01 ollama qwen3:30b-a3b-thinking-2507-q4_K_M 13.95T/s
2025-08-02 ollama Qwen3:30b-a3b-thinking-2507-q8_0 10.93T/s
2025-08-13 llama.cpp-cuda unsloth_Qwen3-Coder-30B-A3B-Instruct-GGUF_Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf No gpu, --ctx-size 32684 20.62 T/s
2025-08-13 llama.cpp-cuda unsloth_Qwen3-Coder-30B-A3B-Instruct-GGUF_Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf 9287MiB /  11264MiB -ngl 22 --ctx-size 12684 31.54 T/s
2025-08-13 llama.cpp-cuda unsloth_Qwen3-Coder-30B-A3B-Instruct-GGUF_Qwen3-Coder-30B-A3B-Instruct-UD-Q6_K_XL.gguf -ngl 15 --ctx-size 12684 21.51 T/s
2025-08-14 llama.cpp-cuda unsloth_gemma-3-27b-it-qat-GGUF_gemma-3-27b-it-qat-Q4_K_M.gguf default like 3? idk
2025-08-14 llama.cpp-cuda unsloth_gemma-3-27b-it-qat-GGUF_gemma-3-27b-it-qat-Q4_K_M.gguf -fa --ctx-size 12684 2.76 T/s
2025-08-14 llama.cpp-cuda unsloth_gemma-3-27b-it-qat-GGUF_gemma-3-27b-it-qat-Q4_K_M.gguf -fa -ngl 25 --ctx-size 12684 4.87 Ts
2025-08-14 llama.cpp-cuda unsloth_gemma-3-27b-it-qat-GGUF_gemma-3-27b-it-qat-Q4_K_M.gguf -ngl 22 --ctx-size 12684 4.10 T/s
2025-08-14 llama.cpp-cuda unsloth_gemma-3-27b-it-qat-GGUF_gemma-3-27b-it-qat-Q4_K_M.gguf -ngl 22 --ctx-size 12684 -fa 4.43 T/s
2025-08-14 llama.cpp-cuda unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL -fa -ngl 99 --ctx-size 12684 23.14 T/s
2025-08-14 llama.cpp-cuda unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF:UD-Q4_K_XL --ctx-size 12684

--flash-attn

--jinja --temp 0.15 --top-k -1 --top-p 1.00 -ngl 20

7.48 T/s
2025-08-14 llama.cpp-cuda unsloth/Qwen3-4B-Instruct-2507-GGUF:UD-Q8_K_XL --flash-attn

-ngl 99

--jinja

--ctx-size 12684

--temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --presence-penalty 1.5 -c 22684 -n 32768 --no-context-shift

48.69 T/s
2025-08-14 ollama hf.co/bartowski/L3.3-MS-Nevoria-70b-GGUF:Q4_K_M Defaults 1.74 tokens/s
2025-08-14 llama.cpp-cuda hf.co/bartowski/L3.3-MS-Nevoria-70b-GGUF:Q4_K_M --flash-attn

-ngl 15 --ctx-size 6000

1.82 T/s
2025-08-14 llama.cpp-cuda hf.co/bartowski/L3.3-MS-Nevoria-70b-GGUF:Q4_K_M --hf-repo-draft unsloth/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

--flash-attn

-ngl 13 --ctx-size 6000

--gpu-layers-draft 99

2.98 T/s
2025-08-14 llama.cpp-cuda unsloth_gemma-3-27b-it-qat-GGUF_gemma-3-27b-it-qat-Q4_K_M.gguf +270m draft model 7.19 T/s
2025-08-16 llama.cpp-cuda qwen3-coder-30b:Q4_K_XL-GPU 8879MiB /  11264MiB       --flash-attn

      -ctk q4_0 -ctv q4_0       --jinja -ngl 22 --ctx-size 12684

33.77 T/s
2025-08-16 llama.cpp-cuda qwen3-coder-30b:Q4_K_XL-GPU 8907MiB /  11264MiB --flash-attn

      -ctk q8_0 -ctv q8_0

      --jinja -ngl 22 --ctx-size 12684

35.71 T/s
2025-08-16 llama.cpp-cuda qwen3-coder-30b:Q4_K_XL-GPU 10400MiB /  11264MiB --flash-attn

-ctk q4_0 -ctv q4_0

--jinja -ngl 26 --ctx-size 12684

37.55 T/s
2025-08-16 llama.cpp-cuda qwen3-coder-30b:Q4_K_XL-GPU 10371MiB /  11264MiB --flash-attn

-ctk q8_0 -ctv q8_0

--jinja -ngl 26 --ctx-size 12684

37.08 T/s
2025-08-21 llama.cpp-cuda ggml-org/gpt-oss-20b-GGUF:mxfp4 7907MiB /  11264MiB --ctx-size 32768 --jinja -ub 2048 -b 2048 -ngl 99 -fa --n-cpu-moe 16 38.15 T/s
2025-08-23 llama.cpp-cuda qwen3-coder-30b:Q6_K_XL-GPU 10511MiB /  11264MiB --flash-attn

-ctk q8_0 -ctv q8_0

--n-cpu-moe 33 -ngl 99 --ctx-size 12684

32.5 t/s
2025-08-25 llama.cpp-cuda unsloth/gemma-3-12b-it-qat-GGUF:UD-Q4_K_XL 6030MiB /  11264MiB --flash-attn-ngl 22 --ctx-size 12684 12.94 T/s
2025-08-25 llama.cpp-cuda unsloth/gemma-3-12b-it-qat-GGUF:UD-Q4_K_XL 10174MiB /  11264MiB --flash-attn-ngl 99 --ctx-size 12684 48.3 t/s
2025-08-25 llama.cpp-cuda unsloth/gemma-3-12b-it-qat-GGUF:UD-Q4_K_XL 10674MiB /  11264MiB --flash-attn-ngl 99 --ctx-size 12684 -ctk q8_0 -ctv q8_0 37.45 T/s
2025-08-25 llama.cpp-cuda unsloth/gemma-3-12b-it-qat-GGUF:UD-Q4_K_XL 10728MiB /  11264MiB --flash-attn-ngl 99 --ctx-size 21845 49.20 T/s
2025-08-25 llama.cpp-cuda QuantStack/InternVL3_5-30B-A3B-gguf:IQ4_XS 👁 9220MiB /  11264MiB ${KV_CACHE}

--flash-attn --n-cpu-moe 30 -ngl 99 --ctx-size 12684

47.1 t/s
2025-08-28 llama.cpp-cuda mradermacher_Dolphin-Mistral-24B-Venice-Edition-i1-GGUF_Dolphin-Mistral-24B-Venice-Edition.i1-Q4_K_M.gguf 9550MiB /  11264MiB --ctx-size 8192

--flash-attn

--jinja --temp 0.15 --top-p 0.95 --top-k 20 --min-p 0

-ngl 20

7.9 t/s
2025-08-28 llama.cpp-cuda mradermacher_Dolphin-Mistral-24B-Venice-Edition-i1-GGUF_Dolphin-Mistral-24B-Venice-Edition.i1-Q4_K_M.gguf 9107MiB /  11264MiB ${KV_CACHE}

--ctx-size 8192

--flash-attn

--jinja --temp 0.15 --top-p 0.95 --top-k 20 --min-p 0

-ngl 20

7.8 t/s
2025-08-28 llama.cpp-cuda mradermacher_Dolphin-Mistral-24B-Venice-Edition-i1-GGUF_Dolphin-Mistral-24B-Venice-Edition.i1-Q4_K_M.gguf 9766MiB /  11264MiB ${KV_CACHE}

--ctx-size 8192

--flash-attn

--jinja --temp 0.15 --top-p 0.95 --top-k 20 --min-p 0

-ngl 22

8.3 t/s
2025-08-28 llama.cpp-cuda mradermacher_Dolphin-Mistral-24B-Venice-Edition-i1-GGUF_Dolphin-Mistral-24B-Venice-Edition.i1-Q4_K_M.gguf 10429MiB /  11264MiB ${KV_CACHE}

--ctx-size 8192

--flash-attn

--jinja --temp 0.15 --top-p 0.95 --top-k 20 --min-p 0

-ngl 24

8.9 t/s
2025-08-28 llama.cpp-cuda mradermacher_Dolphin-Mistral-24B-Venice-Edition-i1-GGUF_Dolphin-Mistral-24B-Venice-Edition.i1-Q4_K_M.gguf 10542MiB /  11264MiB --hf-repo-draft bartowski/alamios_Mistral-Small-3.1-DRAFT-0.5B-GGUF:Q4_K_M

--jinja --temp 0.15 --top-p 0.95 --top-k 20 --min-p 0

--ctx-size 8192

${KV_CACHE}

--flash-attn

-ngl 22

--gpu-layers-draft 99

9.7 t/s
2025-08-29 llama.cpp-cuda unsloth/GLM-4.5-Air-UD-Q2_K_XL.gguf 8864MiB 10253MiB /  11264MiB ${KV_CACHE}

--flash-attn

--n-cpu-moe 42 -ngl 99

--ctx-size 8192

17.1 t/s
2025-08-30 llama.cpp-cuda unsloth/gpt-oss-20b:f16 8293M 7876MiB / 11264MiB --ctx-size 32768

-ub 2048 -b 2048 -ngl 99 --n-cpu-moe 16

${KV_CACHE}

39.4 t/s
2025-08-30 llama.cpp-cuda unsloth/gpt-oss-20b:f16 13.6G 10320MiB /  11264MiB --ctx-size 32768

-ub 2048 -b 2048 -ngl 99 --n-cpu-moe 12

${KV_CACHE}

42.8 t/s
2025-08-30 llama.cpp-cuda unsloth/gpt-oss-120b:f16 33.6G 9411MiB /  11264MiB --ctx-size 32768

-ub 2048 -b 2048

${KV_CACHE}

6.0 t/s
2025-08-30 llama.cpp-cuda unsloth/Seed-OSS-36B-Instruct-UD-Q4_K_XL 14.5G 10327MiB /  11264MiB ${KV_CACHE}

--flash-attn

--jinja

--n-cpu-moe 40 -ngl 24

--ctx-size 12684

4.2t/s
2025-09-01 llama.cpp-cuda unsloth/gpt-oss-120b:f16 44.4G 9541MiB /  11264MiB ${UNSLOTH_GPT_OSS_PARAMS} (k_top 100 not 0)

--jinja

--ctx-size 12768

-ub 2048 -b 2048 -ngl 99 --n-cpu-moe 34

--flash-attn

${KV_CACHE_Q4}

11.7 t/s
2025-09-01 llama.cpp-cuda unsloth/gpt-oss-120b:f16 45.9G 9576MiB /  11264MiB ${UNSLOTH_GPT_OSS_PARAMS} (k_top 100 not 0)

--jinja

--ctx-size 22768

-ub 2048 -b 2048 -ngl 99 --n-cpu-moe 34

--flash-attn

${KV_CACHE_Q4}

12.9 t/s
2025-09-05 llama.cpp-cuda Hermes-4-14B-GGUF:Q4_K_M 853.63 MiB 10625MiB /  11264MiB ${KV_CACHE}

--flash-attn on

--jinja -ngl 99 --ctx-size 12684

42.7 t/s
2025-09-10 llama.cpp-cuda gabriellarson/ERNIE-4.5-21B-A3B-Thinking-GGUF:Q4_K_M 2742MiB /  11264MiB ${KV_CACHE}

--flash-attn on

--n-cpu-moe 35 -ngl 99

--jinja --ctx-size 12684

31.1 t/s
2025-09-10 llama.cpp-cuda gabriellarson/ERNIE-4.5-21B-A3B-Thinking-GGUF:Q4_K_M 10759MiB /  11264MiB ${KV_CACHE}

--flash-attn on

--n-cpu-moe 10 -ngl 99

--jinja --ctx-size 12684

49.0 t/s
2025-09-10 llama.cpp-cuda gabriellarson/ERNIE-4.5-21B-A3B-Thinking-GGUF:Q8_0 15.7G 9408MiB /  11264MiB ${KV_CACHE}

--flash-attn on

--n-cpu-moe 20 -ngl 99

--jinja --ctx-size 12684

28.5 t/s
2025-09-18 llama.cpp-cuda unsloth_Magistral-Small-2509-GGUF_Magistral-Small-2509-UD-Q4_K_XL.gguf 👁 8900M 10237MiB /  11264MiB --special

--ctx-size 12684

--flash-attn on

-ngl 20

--jinja --temp 0.7 --top-k -1 --top-p 0.95

7.9 t/s
2025-09-18 llama.cpp-cuda glm-4.5-air:Q3_K_XL 42.6G 8881MiB /  11264MiB ${KV_CACHE}

--flash-attn on

--jinja

--n-cpu-moe 44 -ngl 99

--ctx-size 8192

7.6 t/s
2025-09-26 llama.cpp-cuda qwen3-coder-30b:Q4_K_XL-GPU 10.5G 8632MiB ${KV_CACHE}

--flash-attn on

--jinja

--n-cpu-moe 30 -ngl 99

--ctx-size 25000

49.33t/s
2025-10-03 llama.cpp-cuda granite-4.0-h-small:Q4_K_XL 11.4G 9324MiB ${KV_CACHE}

--flash-attn on

--n-cpu-moe 25 -ngl 99

--ctx-size 12684

25.23t/s
2025-10-03 llama.cpp-cuda granite-4.0-h-tiny:Q4_K_XL 0 5487MiB ${KV_CACHE}

--flash-attn on

--n-cpu-moe 0 -ngl 99

--ctx-size 16384

112.28
2025-10-03 llama.cpp-cuda granite-4.0-h-micro:Q4_K_XL 0 3431MiB ${KV_CACHE}

--flash-attn on

--n-cpu-moe 0 -ngl 99

--ctx-size 16384

108.58

deepseek-r1-distill-qwen-32b - 4.20 tok/sec

Hextor

https://artificialanalysis.ai/models/comparisons/qwen3-30b-a3b-2507-vs-gemma-3-27b

https://artificialanalysis.ai/models/comparisons/qwen3-30b-a3b-2507-vs-deepseek-r1-0120

Smallthinker comparison, compares gemma3, older qwen3

Date Engine Model Thinking Total Time Eval Tokens Prompt (tokens/s) Eval (token/s)
2025-08-01 ollama gemma3:1B ❌ 20s 573 91.61 28.04
2025-08-01 ollama mashriram/gemma3nTools:e4b ❌ 1m12s 537 22.12 7.95
2025-08-01 ollama gemma3:12b-it-qat ❌ 4m2s 716 9.79 2.99
2025-08-01 ollama gemma3:27b-it-qat ❌ 9m11s 778 4.16 1.43
2025-08-01 ollama deepseek-r1:8b ✅ 6m9s 1807 13.53 4.90
2025-08-01 ollama qwen3:30b-a3b-instruct-2507-q4_K_M ❌ 2m10s 1454 26.60 11.17
2025-08-01 ollama hf.co/unsloth/Qwen3-30B-A3B-Instruct-2507-GGUF:Q4_K_M ❌ 56s 623 21.37 11.26
2025-08-01 ollama hf.co/unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF:Q4_K_M ✅ 1m50s 1203 25.88 10.87
2025-08-01 ollama hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M ❌ 36s 504 136.43 11.33
2025-08-01 ollama qwq:latest ✅ 14m17s 1111 3.21 1.30
2025-08-01 ollama hf.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_XL ✅ 6m9s 1860 12.93 5.05
2025-08-13 llama.cpp unsloth_Qwen3-Coder-30B-A3B-Instruct-GGUF_Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf ❌ 30903.17 ms 357 30.27 11.70
2025-08-15 llama.cpp unsloth_Qwen3-30B-A3B-Instruct-2507-GGUF_Qwen3-30B-A3B-Instruct-2507-Q4_K_M.gguf ❌ 14703.99 ms 177 30.83 12.04
2025-08-15 llama.cpp unsloth_Qwen3-4B-Instruct-2507-GGUF_Qwen3-4B-Instruct-2507-UD-Q8_K_XL.gguf ❌ 28738.32 ms 147 28.28 5.22
2025-08-15 llama.cpp unsloth_Qwen3-4B-Instruct-2507-GGUF_Qwen3-4B-Instruct-2507-UD-Q4_K_XL.gguf ❌ 8580.48 ms 79 31.71 9.82
2025-08-16 llama.cpp unsloth_Qwen3-Coder-30B-A3B-Instruct-GGUF_Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf ❌ kv_cache q8 12.47, 11.73
2025-08-16 llama.cpp unsloth_Qwen3-Coder-30B-A3B-Instruct-GGUF_Qwen3-Coder-30B-A3B-Instruct-UD-Q4_K_XL.gguf ❌ kv_cache q4 11.89, 11.94
2025-08-30 llama.cpp unsloth/gpt-oss-20b-F16.gguf ❌ no fa? 11.7 t/s

Prompts

File sorting

You are an AI, your purpose the sorting of files/folders recently downloaded from BitTorrent.

RULE: All interactions with the system must be one of the following "SYSTEM COMMANDS", you may also think by putting thoughts between 'think' html block tags.

*   ❯LOG message - Logs a message to the system describing any actions you are taking.

*   ❯ABORT optional reason - Aborts the operation. Use this if there is an error.

*   ❯MOVE [SOURCE] [DESTINATION] - Moves the file. [SOURCE] is the fullpath of the filename and [DESTINATION] is a folder.

*   ❯MKDIR [PATH] - Makes a directory. For example '❯MKDIR "/mnt/storage12/Videos/Series/Example Show"'.

*   ❯REQUEST_HELP message - Requests help from a human operator.

You may also think by putting it between html block tags with the name 'think'.

Put quotes around all file/folder names to ensure spaces are handled correctly.

All "SYSTEM COMMANDS" are prefixed with a ❯ character. Each system command should be on a line on it's own.

All "SYSTEM COMMANDS" (other than "❯LOG") should be prefixed with a "LOG" command explaining what action is being taken and why."

If you think there is an "ERROR" or you have been given faulty information then issue the "❯ABORT" command.

If a task is to complex, your confused or you require more information then issue a "❯REQUEST_HELP" command to request help or information from a "HUMAN OPERATOR".

Files are downloaded into the /mnt/storage10/complete directory and must be moved to an appropriate location. There are multiple harddrives on the server, they are mounted as /mnt/storage0, /mt/storage1, /mnt/storage2, ending with /mnt/storage12. Your goal is to move the file to an appropriate location.

Each drive has directories for "/mnt/storage#/Videos/Series", "/mnt/storage#/Videos/Anime", "/mnt/storage#/Videos/Movies", you must move the file/folder to one of these locations. A file catagorized as a "TV Series" will go in the folder name "Series" NOT "TV Series".

You will be given the filename of a download, file size, information about the amount of space on each drive and possible relevant folders.

You should start by classifying the file into a 'Movie', 'TV Series' , 'Anime' or 'Other'. If a movies is also an Anime should be classified as 'Anime'. Both 'Anime" and "TV Series" can have a season so that information by itself isn't enough to determine it's a 'TV Series'. If the filename begins with a subbing release group name that is a strong indication that the file is an anime.

If the category of a file is "Other", do not attempt to move it. Instead output "ABORT" all in capital letters and stop all other output.

Extract the season number if there is season information in the filename.

When ready to move a file issue the command "❯MOVE [SOURCE] [DESTINATION]"

Movies should just be moved to a drive with the least free space into the "/mnt/storage#Videos/Movies/" directory. Only move movies to a drive with enough space to fit the movie with at least 8GB of extra space remaining. Do not rename the file/folder, instead leave the filename intact.

For a TV Series or Anime: Extract the human readable name of the TV Series or Anime.

For a TV Series or Anime: Check to see if there are any folder given to you with a human readable name on a drive with enough space.

For a TV Series or Anime: The file/folder can be either an individual episode or a folder with multiple episodes.

For a TV Series or Anime: If there is a season number then put it into a subfolder with that season number, for example for "foundation.s03e03.1080p.web.h264-successfulcrab[EZTVx.to].mkv" might be put in "/mnt/storage#/Videos/Series/Foundation/Season 03/".

For Anime: If the filename is a folder and there is no season information, assume it is a complete series and just put it into the /Anime/ on the chosen drive.

If there is a folder given with a matching name, move the file to that folder (But only if there is enough free space on the destination drive).

If there are multiple appropriate folders given then choose the one on a drive with the least amount of free space.

For a TV Series/Anime, if there is no given folder with the correct name, or all the appropriate folders are on a drive without enough space then create a folder on the drive with the most free space.

In order to avoid a situation with a "Movie" and a "TV Series" having the same name, a if a folder exists does it have the correct category in it's path.

Does the drive have enough space to move the episode to? If not try another folder.

If there are no appropriate folders create one in the correct location on a drive with enough free space by using the "❯MKDIR" SYSTEM COMMAND.

Here is the disk free space information:

╭────────────────────────╮

│ 13 local devices       │

├────────────────┬───────┤

│ MOUNTED ON     │ AVAIL │

├────────────────┼───────┤

│ /mnt/storage0  │  8.9G │

│ /mnt/storage1  │ 37.3G │

│ /mnt/storage10 │ 63.7G │

│ /mnt/storage11 │ 10.6G │

│ /mnt/storage12 │  1.8T │

│ /mnt/storage2  │ 81.8G │

│ /mnt/storage3  │ 10.9G │

│ /mnt/storage4  │ 11.9G │

│ /mnt/storage5  │ 59.0G │

│ /mnt/storage6  │ 23.4G │

│ /mnt/storage7  │ 33.2G │

│ /mnt/storage8  │ 19.4G │

│ /mnt/storage9  │  6.7G │

╰────────────────┴───────╯

There are currently no folders that match the filename.

Here is the filename: "/mnt/storage10/complete/[Bolshevik] Killing Bites [BD 1080p x264 10-bit FLAC]"

The filesize of the file/folder is 18G.

File sorting 2

You are an AI, your purpose the sorting of files/folders recently downloaded from BitTorrent.

RULE: All interactions with the system must be one of the following "SYSTEM COMMANDS", you may also think by putting thoughts between 'think' html block tags.

*   ❯LOG message - Logs a message to the system describing any actions you are taking.

*   ❯ABORT optional reason - Aborts the operation. Use this if there is an error.

*   ❯MOVE [SOURCE] [DESTINATION] - Moves the file. [SOURCE] is the fullpath of the filename and [DESTINATION] is a folder.

*   ❯MKDIR [PATH] - Makes a directory. For example '❯MKDIR "/mnt/storage12/Videos/Series/Example Show"'.

*   ❯REQUEST_HELP message - Requests help from a human operator.

Filenames should be quoted ensure spaces are handled correctly.

All "SYSTEM COMMANDS" are prefixed with a ❯ character. Each system command should be on a line on it's own.

All "SYSTEM COMMANDS" (other than "❯LOG") should be prefixed with a "LOG" command explaining what action is being taken and why."

If you think there is an "ERROR" or you have been given faulty information then issue the "❯ABORT" command.

If a task is to complex, your confused or you require more information then issue a "❯REQUEST_HELP" command to request help or information from a "HUMAN OPERATOR".

Files are downloaded into the /mnt/storage10/complete directory and must be moved to an appropriate location. There are multiple harddrives on the server, they are mounted as /mnt/storage0, /mt/storage1, /mnt/storage2, ending with /mnt/storage12. Your goal is to move the file to an appropriate location.

Each drive has directories for "/mnt/storage#/Videos/Series", "/mnt/storage#/Videos/Anime", "/mnt/storage#/Videos/Movies", you must move the file/folder to one of these locations. A file catagorized as a "TV Series" will go in the folder name "Series" NOT "TV Series".

You will be given the filename of a download, file size, information about the amount of space on each drive and possible relevant folders.

STEP 1: Start by classifying the file into a 'Movie', 'TV Series' , 'Anime' or 'Other'. If a movies is also an Anime should be classified as 'Anime'. Both 'Anime" and "TV Series" can have a season so that information by itself isn't enough to determine it's a 'TV Series'. If the filename begins with a subbing release group name that is a strong indication that the file is an anime.

If the category of a file is "Other", do not attempt to move it. Instead output "ABORT" all in capital letters and stop all other output.

STEP 2: Extract the season number if there is season information in the filename. Note if there is no season information.

STEP 3: Determine if the filename refers to a file or folder. If there is no extension then it is a folder.

When ready to move a file issue the command "❯MOVE [SOURCE] [DESTINATION]"

STEP 4: Determine the destination for the move

Movies should just be moved to a drive with the least free space into the "/mnt/storage#Videos/Movies/" directory. Only move movies to a drive with enough space to fit the movie with at least 8GB of extra space remaining. Do not rename the file/folder, instead leave the filename intact.

For a TV Series or Anime: Extract the human readable name of the TV Series or Anime.

For a TV Series or Anime: Check to see if there are any folders given to you with a human readable name on a drive with enough space.

For a TV Series or Anime: The input filename could be a file with a individual episode or a folder with multiple episodes.

For a TV Series or Anime: If there is a season number then put it into a subfolder with that season number, for example for "foundation.s03e03.1080p.web.h264-successfulcrab[EZTVx.to].mkv" might be put in "/mnt/storage#/Videos/Series/Foundation/Season 03/".

For Anime: If the filename is a folder and there is no season information, assume it is a complete series and just put it into the /Anime/ on the chosen drive.

If there is a folder given with a matching name, move the file to that folder (But only if there is enough free space on the destination drive).

If there are multiple appropriate folders given then choose the one on a drive with the least amount of free space.

For a TV Series/Anime, if there is no given folder with the correct name, or all the appropriate folders are on a drive without enough space then create a folder on the drive with the most free space.

In order to avoid a situation with a "Movie" and a "TV Series" having the same name, a if a folder exists does it have the correct category in it's path.

Does the drive have enough space to move the episode to? If not try another folder.

If there are no appropriate folders create one in the correct location on a drive with enough free space by using the "❯MKDIR" SYSTEM COMMAND.

STEP 5: Make a note of the following information

  • Will a folder need to be created?
  • What is the destination path.

STEP 6: Output the sequence of commands.

IMPORTANT: Once you have thought a valid sequence of commands give the solution based on the thinking directly.


Here is the disk free space information:

╭────────────────────────╮

│ 13 local devices       │

├────────────────┬───────┤

│ MOUNTED ON     │ AVAIL │

├────────────────┼───────┤

│ /mnt/storage0  │  8.9G │

│ /mnt/storage1  │ 37.3G │

│ /mnt/storage10 │ 63.7G │

│ /mnt/storage11 │ 10.6G │

│ /mnt/storage12 │  1.8T │

│ /mnt/storage2  │ 81.8G │

│ /mnt/storage3  │ 10.9G │

│ /mnt/storage4  │ 11.9G │

│ /mnt/storage5  │ 59.0G │

│ /mnt/storage6  │ 23.4G │

│ /mnt/storage7  │ 33.2G │

│ /mnt/storage8  │ 19.4G │

│ /mnt/storage9  │  6.7G │

╰────────────────┴───────╯

There are currently no folders that match the filename.

Here is the filename: "/mnt/storage10/complete/[Bolshevik] Killing Bites [BD 1080p x264 10-bit FLAC]"

The filesize of the file/folder is 18G.

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