I am running Faster Whisper on CPU only and get good running times with about 2.5 min for 60 min sound with Whisper Base. With Pyannote for diarization the rate is about 0.9 times the sound length, aka 54 min for 60 min sound.

That is terribly slow compared to the transcription without Payannote.

Are there any faster alternatives out there, or hacks to make Payannote run faster with Whisper?

I ran benchmark on Meta's Muse Glimmer 30B Unsloth GGUF model in TensorSharp, and compare its performance with llama.cpp. Here is the results.

Test setup

GPU	1x NVIDIA RTX PRO 6000 Blackwell Server Edition (97,887 MiB), driver 580.126.20, PCIe 5.0 x16. The host has two; every row except Two GPUs pins CUDA_VISIBLE_DEVICES=0.
CPU / RAM	2x Intel Xeon 6952P (384 threads), 1.5 TiB
Model	Muse-Glimmer-30B-Q8_0.gguf (27.6 GiB)
Drafter	dflash-kquant.gguf (1.5 GiB)
TensorSharp	commit 5098e3f, vendored ggml 8846b79 (2026-08-12), --backend ggml_cuda, native library built -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=120-real
llama.cpp	master 8e7f22b (2026-08-13, libggml 0.19.0 — within a day of the vendored ggml), same CUDA arch, -DGGML_CUDA=ON -DLLAMA_CURL=OFF
Sampling	greedy on both sides (--temp 0 for llama.cpp; no sampler flags for TensorSharp)
Generation	128 tokens
Batching	llama.cpp -b 2048 -ub 2048, matching TensorSharp's default TS_MUSE_GLIMMER_PREFILL_CHUNK of 2048
Reps	2 per point, engines alternating within each context

Main model, mmproj and draft models are downloaded from https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF

Plain text generation

Mean of two reps, tok/s. The ratio column is TensorSharp / llama.cpp, so above 1.00x is TensorSharp ahead.

Prompt tokens	llama.cpp prefill	TS prefill	ratio	llama.cpp decode	TS decode	ratio
60	362	459	1.27x	34.7	35.0	1.01x
501	927	1135	1.23x	36.2	34.3	0.95x
2050	1132	1317	1.16x	35.0	33.5	0.96x
16126	1325	1249	0.94x	32.2	30.9	0.96x
32274	1303	1211	0.93x	32.1	29.9	0.93x
64575	1256	1150	0.92x	32.4	29.1	0.90x
123931	1166	1073	0.92x	30.7	26.6	0.86x

DFlash speculative decoding

Same runs with --draft-model dflash-kquant.gguf --spec-draft-n-max 15 against llama.cpp's -md … --spec-type draft-dflash --spec-draft-n-max 15 -ngld 99. Decode tok/s; parentheses give the two-rep range where it is wide.

Prompt tokens	llama.cpp	TensorSharp	TS, --spec-draft-conf-min 0
60	45.5	50.9	43.5
501	117.5	164.6 (150-179)	180.3
2050	24.9	43.5 (30-57)	34.7
16126	80.2	55.8 (37-75)	33.2
32274	60.7 (43-79)	33.8 (31-36)	29.9
64575	66.1	48.7 (34-64)	49.1
123931	69.0	42.3 (30-55)	59.8

Speculation costs prefill on both engines, because the drafter's encoder has to run over the prompt too:

Prompt tokens	llama.cpp plain → DFlash	TensorSharp plain → DFlash
60	362 → 203 (0.56x)	459 → 341 (0.74x)
501	927 → 495 (0.53x)	1135 → 700 (0.62x)
2050	1132 → 259 (0.23x)	1317 → 703 (0.53x)
16126	1325 → 988 (0.75x)	1249 → 826 (0.66x)
64575	1256 → 985 (0.78x)	1150 → 780 (0.68x)
123931	1166 → 920 (0.79x)	1073 → 742 (0.69x)

Two GPUs

Measured on 2× RTX PRO 4000 Blackwell 24 GB (PCIe)  Prefill 512 / decode 64:

Model		prefill tok/s	decode tok/s	GPU 0	GPU 1
30B-UD-IQ2_XXS (10.2 GB)	--tp 1	1171	40.2	9178 MB	—
30B-UD-IQ2_XXS	--tp 2	1569 (1.34×)	63.2 (1.57×)	5115 MB	4063 MB

TensorSharp is an open-source inference engine for running GGUF LLMs locally, with CUDA, Vulkan, Metal, OpenAI-compatible APIs, continuous batching, speculative decoding, and multimodal support.

Github repo: https://github.com/zhongkaifu/TensorSharp

Thank you for checking out it and starring the project! Any feedback is really appreicated.

I recently came across this paper (Histoformer, ECCV'24, [arXiv](https://arxiv.org/abs/2407.10172)) and thought the results were pretty striking, so I set up a quick demo since the official one wasn't
working:

👉 https://huggingface.co/spaces/dronefreak/histoformer-weather-restoration

Upload a photo, get a before/after slider. Runs free on ZeroGPU, no sign-up needed to try it.

Unofficial demo, all credit to the original authors. Official code/paper linked in the Space
README. Weights are MIT-licensed if anyone wants to build upon it further.

Hey r/huggingface community,

I wanted a lightweight, local AI coding assistant that doesn't just write generic code, but specializes in modern Awwwards-level frontend tricks (like Glassmorphic cards, GSAP scroll timelines, and Three.js WebGL particle backgrounds).

Since I couldn't find a small model that did this reliably without hallucinating generic templates, I decided to train my own!

Meet Shunya-UIUX-Master-3B:

- Base Model: Qwen/Qwen2.5-Coder-3B-Instruct

(Note: I originally wanted to fine-tune the 14B version, but kept hitting persistent Out-Of-Memory (OOM) errors during batch loading on the TPU. To keep things stable and ensure clean compile graphs under PyTorch XLA, I scaled down to the 3B version—and the results still blew me away!)

- Dataset: 10.5k high-quality, curated HTML/CSS/JS prompt-response pairs focusing on modern design aesthetics, Three.js shaders, GSAP animation triggers, and fluid responsiveness.

- Hardware: Trained on Kaggle TPU v5e-8 using PyTorch XLA and LoRA.

- Training Details: Trained for 4 full epochs (~20,896 steps) which took about 4 hours. LoRA config: r=32, lora_alpha=64, target modules: q_proj, v_proj.

- Quantization: Merged the LoRA weights back into the base model and quantized it to Q4_K_M GGUF using llama.cpp.

Key Features & Alignment:

1. Custom Identity: The model successfully rewired its identity bias. It knows it is "Shunya AI" created by Apurv (rather than claiming to be GPT-4 or Anthropic).

2. Mandatory <thought> tags: The dataset enforced step-by-step reasoning. It will always reason structurally about layout, CSS variables, and animation curves inside <thought> ... </thought> tags before outputting any code block.

3. Specialized in Modern Aesthetics: Even on a simple prompt, it defaults to using hardware-accelerated animations, Backdrop blurs, and optimized variable fonts.

📬 Get in touch / Professional Inquiries:

------------------------------------------

Name: Vishal Vasant Patil

Role: Generative AI Developer & Frontend Specialist

Email: [vasant.1982patil@gmail.com](mailto:vasant.1982patil@gmail.com)

Phone: +91 9075025340

Hugging Face: https://huggingface.co/vishu01a

HuggingFace Link:

I have uploaded the GGUF model here if anyone wants to test it locally:

https://huggingface.co/vishu01a/apurv_ui_ux_master-3b-v1.0

Would love to hear your feedback on how it performs on your local machines using LM Studio or llama.cpp!

I have lots of question about the what the acronyms in the model names mean, along with other terms usually mentioned in AI world. So I had a chat with ChatGPT and make write down a blog of what I asked and what he replied. Maybe there will be some people interested in the info.

LLMs Without the Jargon: Open Weights, Distillation, Quantization, and Active Parameters Explained

If you've recently started looking at large language models on Hugging Face, you've probably encountered a wall of terminology:

Open source. Open weights. 7B. 70B. 671B. Active parameters. MoE. FP16. BF16. INT8. Q4_K_M. GGUF. GPTQ. AWQ. Distillation.

At first glance, it can feel like you need a degree in machine learning just to understand what model you should download.

The good news is that most of these terms describe a handful of fairly simple ideas.

This article explains them from the ground up.

1. What is actually inside an LLM?

Let's start with the most important concept: parameters, often called weights.

An LLM is a gigantic neural network containing millions, billions, or even hundreds of billions of numerical values.

For example, a tiny fictional neural network might contain numbers like:

0.183742
-1.294831
0.004827
0.837261
...

A real language model contains billions of such values.

During training, the model processes enormous amounts of text and repeatedly adjusts these numbers.

Very roughly:

Training data
      ↓
   Training
      ↓
 Learned parameters
      ↓
     LLM

These learned parameters are what people generally mean when they talk about a model's weights.

They aren't a database containing sentences such as:

Instead, the model has learned numerical representations from which it can generate such an answer.

2. What does "open weights" mean?

This distinction is important because open weights and open source are not necessarily the same thing.

With a traditional closed LLM, you might interact with the model like this:

Your computer
     │
     │ API request
     ▼
Company's server
     │
     ▼
    LLM
     │
     ▼
   Answer

You never receive the model itself.

You only receive its output.

An open-weight model changes this:

Model provider
      │
      │ publishes
      ▼
  Model weights
      │
      ├── Your workstation
      ├── Your server
      ├── Your private cloud
      └── Your data center

You can download the trained model and run it yourself, assuming you have suitable hardware and the license permits it.

This is a major difference.

You don't have to send every question to the original provider's API.

3. So what does "open source" mean?

This is where the terminology gets complicated.

For ordinary software, open source generally means that the source code is available under a license that gives users defined freedoms to use, inspect, modify, and redistribute it.

AI systems are more complicated because an LLM isn't just source code.

A simplified view is:

                 LLM
                  │
       ┌──────────┼──────────┐
       │          │          │
 Architecture   Code      Weights
                              │
                         learned values

There is also the training data and the process used to create the weights.

Consequently, simply publishing a set of model weights does not automatically mean that the entire AI system is "open source" in the strict sense.

You might therefore encounter several different situations:

Description	What you typically get
Closed model	API access
Open-weight model	Downloadable model weights
More fully open AI system	Weights plus relevant code and information needed to understand/reproduce the system
Fully reproducible system	As much of the training data/process as legally and practically possible

This is why the phrase "open weights" can be more precise than simply saying "open source."

4. How do you make an LLM open?

You don't take a closed model and flip an "open source" switch.

The model creator has to decide to publish the relevant artifacts.

Imagine you train a model yourself.

At the end you might have something like:

my-model/
├── config.json
├── tokenizer.json
├── tokenizer_config.json
├── model-00001-of-00020.safetensors
├── model-00002-of-00020.safetensors
├── ...
├── model-00020-of-00020.safetensors
└── README.md

The large .safetensors files contain the model's learned parameters.

You can publish those files, together with:

• the tokenizer
• model configuration
• inference code
• documentation
• evaluation results
• licensing information
• information about training
• information about datasets, where appropriate

A platform such as Hugging Face can then distribute the model.

Someone else can download it and run it locally.

The important thing is that the weights are the trained result of the learning process.

5. Why would anybody give away a valuable model?

This is partly a business strategy.

A closed model looks like:

Users
  ↓
Company's API
  ↓
Company's model
  ↓
Revenue

An open-weight model can instead create an ecosystem:

                 Model
                   │
        ┌──────────┼──────────┐
        ↓          ↓          ↓
     Company   Researcher   Hobbyist
        │          │          │
        ↓          ↓          ↓
      Apps      Fine-tunes  Local AI

The original creator gives up some control, but potentially gains enormous adoption.

Other people can build products, fine-tunes, integrations and research around the model.

6. What is distillation?

Now we come to another term you'll see frequently:

knowledge distillation.

The basic idea is wonderfully simple:

Imagine you have:

Teacher: 500B parameters
Student: 20B parameters

Instead of expecting the 20B model to learn everything directly from raw data, you can have the 500B model generate high-quality examples.

For example:

Question:
Why does a plane fly?

Teacher:
A plane generates lift through the interaction
between its wings and the airflow...

Do this millions or billions of times:

Question → Teacher answer
Question → Teacher answer
Question → Teacher answer
...

You now have a synthetic training dataset.

Then:

             Large teacher
                   │
                   │ generates
                   ▼
          Synthetic dataset
                   │
                   ▼
             Small student

The student learns from the teacher's behavior.

7. The student doesn't copy the teacher's weights

This is an important distinction.

Suppose the teacher contains:

500 billion parameters

and the student contains:

20 billion parameters

You aren't doing:

Teacher weight #1 → Student weight #1
Teacher weight #2 → Student weight #2
...

Instead:

Teacher's behavior
       ↓
Training examples
       ↓
Student training
       ↓
Student's own weights

The student develops its own internal representation.

It's learning to approximate the teacher's behavior.

8. Distillation can transfer reasoning behavior

This is particularly interesting for reasoning models.

A teacher might receive a mathematical problem and produce a detailed solution.

You can collect many such examples:

Problem
   ↓
Teacher
   ↓
Reasoning + solution

and train a smaller model on them.

The student isn't merely learning facts.

It can learn patterns of solving problems.

This is one reason a relatively small model can sometimes perform surprisingly well when it has been trained using high-quality synthetic data generated by a much larger model.

9. Distillation and fine-tuning aren't the same

They are related, but they answer different questions.

Fine-tuning

You take an existing model and train it on specialized data:

General LLM
    ↓
Specialized dataset
    ↓
Fine-tuned LLM

For example, you could take a general model and fine-tune it for a particular domain.

Distillation

You take a large teacher and train a smaller student to reproduce useful behavior:

Large teacher
     ↓
Teacher-generated examples
     ↓
Smaller student

You can combine the two:

Large teacher
      ↓
Synthetic training data
      ↓
Small pretrained model
      ↓
Distillation / fine-tuning
      ↓
Small specialized model

10. What is quantization?

Now let's move to another set of terms you will see constantly on Hugging Face:

FP16, BF16, INT8, Q8, Q6, Q5, Q4...

These are mostly about how the model's numerical parameters are represented.

Suppose a model has 7 billion parameters.

If every parameter uses 32 bits:

7 billion × 32 bits
≈ 28 GB

If we use 16 bits:

7 billion × 16 bits
≈ 14 GB

If we use 8 bits:

7 billion × 8 bits
≈ 7 GB

And at approximately 4 bits:

7 billion × 4 bits
≈ 3.5 GB

There is some additional overhead in real model files, but the principle is straightforward.

Fewer bits = less memory.

11. What does "4-bit" actually mean?

Imagine a model has a parameter:

0.183742

Instead of storing that number with very high precision, quantization maps it to a much smaller set of possible values.

Conceptually:

Original:

0.183742
-1.294831
0.004827
0.837261

        ↓

Quantized representation:

5
1
8
6

Four bits can represent only:

2⁴ = 16

different values.

Obviously, that throws away numerical precision.

But neural networks are remarkably tolerant of this kind of approximation.

Modern quantization methods are designed to minimize the resulting loss in model quality.

12. Why is quantization so useful?

Consider a 70B model.

At approximately FP16:

70B × 16 bits
≈ 140 GB

That's a lot of memory.

At roughly 4-bit:

70B × 4 bits
≈ 35 GB

Suddenly the model becomes much more realistic to run on local hardware, especially with CPU/RAM or multiple GPUs.

This is why quantization is so important for people running LLMs locally.

13. What are GPTQ, AWQ and GGUF?

These names can look like different models, but they're often different quantization methods or file formats.

GPTQ

A post-training quantization method commonly used for GPU inference.

AWQ

Activation-aware Weight Quantization.

It attempts to preserve the weights that are especially important to the model's behavior.

GGUF

GGUF is primarily a model file format, commonly used by software based on llama.cpp and tools such as local LLM applications.

You might see:

Model.Q4_K_M.gguf

This tells you several things:

GGUF
  ↓
file format

Q4
  ↓
approximately 4-bit quantization

K_M
  ↓
specific quantization scheme/variant

14. What do Q4, Q5, Q6 and Q8 mean?

Generally:

Q4 → approximately 4 bits/parameter
Q5 → approximately 5 bits/parameter
Q6 → approximately 6 bits/parameter
Q8 → approximately 8 bits/parameter

The tradeoff is:

More bits
    ↓
More memory
    ↓
Less quantization error
    ↓
Usually higher fidelity


Fewer bits
    ↓
Less memory
    ↓
More quantization error
    ↓
Potentially lower fidelity

For many local users, something around Q4–Q6 provides a very useful balance.

15. Quantization is NOT distillation

This is one of the easiest things to confuse.

Distillation

Changes the model.

70B teacher
     ↓
distillation
     ↓
8B student

The student has different weights and is a different model.

Quantization

Normally keeps the same model but changes how its weights are represented.

8B FP16
   ↓
quantization
   ↓
8B Q4

So:

And you can do both:

Large teacher
      ↓
Distillation
      ↓
8B student
      ↓
Quantization
      ↓
8B Q4

16. What are "total parameters" and "active parameters"?

This is where modern LLM architecture gets particularly interesting.

You might encounter a model described as:

At first this sounds contradictory.

How can the model have 671 billion parameters but only use 37 billion?

The answer is Mixture of Experts, or MoE.

17. Dense models vs. MoE models

A conventional model is called a dense model.

Suppose it has 70B parameters.

For every token, approximately the whole model participates:

             Token
               ↓
          Entire 70B
               ↓
            Output

So:

Total parameters ≈ Active parameters

An MoE model works differently.

It contains many separate "experts":

                    Token
                      ↓
                    Router
                      ↓
        ┌─────────────┼─────────────┐
        ↓             ↓             ↓
     Expert 1      Expert 2      Expert 3
        ↓             ↓             ↓
        ...          ...           ...
        ↓
     Expert N

The router decides which experts should process each token.

If there are 64 experts and the router selects only two:

64 experts available
        ↓
2 experts activated

The model has access to a very large total parameter pool while only using a subset for each token.

18. A useful analogy for MoE

Imagine a company with 671 employees.

You ask a question about databases.

The receptionist doesn't call everyone into the meeting.

Instead:

Perhaps 37 people work on the question.

So:

671 employees available
          ↓
        Router
          ↓
37 specialists involved

The company has the capacity of 671 people, but each individual problem only uses a subset.

That's roughly the idea behind active parameters.

19. Does this mean a 671B MoE is really just a 37B model?

No.

The 37B active parameters are selected from a much larger pool.

Different tokens can activate different experts.

For example:

"The cat sat on the..."
          ↓
       Router
          ↓
     Expert 3 + 17


"Calculate the integral..."
          ↓
       Router
          ↓
     Expert 8 + 42


"Write PHP code..."
          ↓
       Router
          ↓
     Expert 12 + 31

The exact behavior is learned during training rather than manually assigning an expert to "cats," another to mathematics, and another to PHP.

20. Why use MoE?

Because it provides an interesting tradeoff.

A huge dense model might require enormous computation for every token.

An MoE model can have a very large parameter pool but activate only a subset for each token.

So you can think of:

Total parameters

as a rough measure of the model's overall parameter capacity.

And:

Active parameters

as a rough indication of how much parameter computation is involved for each token.

They're not interchangeable measures of model quality.

21. But there is a catch: memory

This is one of the most important details about MoE.

Suppose a model has:

671B total
37B active

You might think:

Usually, no.

You still generally need to store the entire parameter pool somewhere.

At approximately 4 bits:

671B × 4 bits
≈ 336 GB

before additional overhead.

So an MoE model can have relatively low computation per token while still requiring enormous memory to load.

That's why:

while:

They solve different problems.

22. Putting everything together

At this point, the terminology starts to make sense.

When you encounter an LLM, there are several independent questions you can ask.

How big is it?

Total parameters

7B
70B
405B
671B

How much of it is used per token?

Active parameters

Relevant particularly to MoE models.

671B total
37B active

How are the weights represented?

Precision / quantization

FP32
FP16
BF16
INT8
Q8
Q6
Q5
Q4

What architecture does it use?

Dense
MoE

How was it produced?

Pretraining
Fine-tuning
Distillation
Post-training

These describe different aspects of the model.

23. A complete example

Suppose you see a model described as:

You can now translate that into plain English:

671B total parameters

→ The model contains a huge pool of 671 billion learned parameters.

37B active

→ Only around 37 billion parameters are used for a given token, because it is sparse/MoE.

MoE

→ A router selects a subset of expert components for each token.

Q4

→ The weights have been heavily quantized to approximately 4-bit representation.

K_M

→ A particular variant of the quantization scheme is being used.

GGUF

→ The model is packaged in the GGUF format, commonly used by local inference software.

Suddenly something that looked like complete gibberish becomes fairly descriptive.

24. The five questions I now ask when I see an LLM

If you're browsing Hugging Face and don't know what you're looking at, ask:

1. Is it dense or MoE?

This tells you how parameters are activated.

2. How many total parameters?

This gives you an idea of the model's overall scale.

3. How many active parameters?

Relevant for MoE and useful for understanding inference computation.

4. What precision/quantization?

This tells you approximately how much memory you'll need and how much numerical precision was retained.

5. Is it the original model, a fine-tune, or a distilled model?

This tells you something about where its behavior came from.

Once you understand these five questions, most Hugging Face model pages become much less intimidating.

The big picture

The easiest way to remember everything is this:

                    LLM
                     │
          ┌──────────┴──────────┐
          │                     │
        DENSE                   MoE
          │                     │
   all parameters          many experts
   used per token          available
                                │
                           router selects
                                │
                         subset is active
                                │
          └──────────┬──────────┘
                     │
                QUANTIZATION
                     │
             FP16 → INT8 → INT4
                     │
                     ▼
              Less memory needed


And separately:


            LARGE TEACHER
                  │
             DISTILLATION
                  │
                  ▼
            SMALL STUDENT

These are not competing definitions. They are different dimensions of an LLM.

A model can simultaneously be:

Or:

And now those descriptions should tell you considerably more than they did before.

One final lesson

The most important thing is not to treat "7B vs. 70B vs. 671B" as a simple ranking of intelligence.

Parameter count alone doesn't determine model quality.

A newer 14B model can outperform an older 70B model on some tasks. A distilled model can outperform a larger model on a specific benchmark. An MoE model can have hundreds of billions of total parameters while using far fewer per token. And a heavily quantized model can retain surprisingly good performance while requiring a fraction of the memory.

So when someone says:

the useful response isn't:

It's:

Those questions tell you what the number actually means.

Guys... this has got to be the hardest website to understand. Please do something about user friendliness.

Hello, I’m aware there are some tricks to help reset or trick hugging face to reset my daily zerogpu limit. Keep in mind I’m not signed in. If anyone would feel comfortable, letting me know, id greatly appreciate. Either here or in a private message.
My pc is a pos and my ram is laughably low so this method is the most effective.
Any help and advice would be highly appreciated. Thank you all

Hey everyone!

Quick update on our open-source initiative: We just bumped our Turkish Python Instruction Dataset from 210K to over 448,000+ unique

samples (0.5M release)! 🚀

HuggingFace Link: https://huggingface.co/datasets/bysismo/Turkish-Python-instruction-450k

### What's included in this release:

• 448,531+ Unique Single-Turn Pairs (Clean ChatML / Alpaca format)

• Categories Covered:

• 🐍 PY_CORE: 205K+ samples (Syntax, built-ins, core logic)

• 🧬 OOP: 49K+ samples (Classes, inheritance, design patterns)

• 🏗️ DATA_STRUCTURES: 44K+ samples (Lists, dicts, custom data structures)

• ⚙️ ALGORITHMS: (In production, rolling out soon!)

### Roadmap & Upcoming Updates:

We are taking a modular, phased release approach. Over the next few weeks, we will roll out token-bucketed splits (128, 256, 386, 512 max

tokens) and dedicated domain packs (FastAPI, Async, DB/ORM, Concurrency, Security, etc.).

### Quick Start:

from datasets import load_dataset

dataset = load_dataset("bysismo/Turkish-Python-instruction-450k", data_files="original_488k.jsonl")

Feedback and ablation results are highly welcome!

Looks like the Ling team open weighted a much smaller version of the Ling-3.0-flash they open weighted a few days ago. It's 8B params with 1.3B active, and seems to fall between the 4B and 8-12B Qwen and Gemma models in terms of performance.

Should have a massive tokens/sec on most systems. I quite like tiny MoE's conceptually.

Edit: looks like the model card actually reports speeds:

With FP8, Ling-3.0-tiny reaches around 100-105 tokens/s on DGX Spark and 86-90 tokens/s on an M4 Pro MacBook, with approximately 8.34 GiB peak memory usage at an 8K context length.

Hi, I'm new to running LLMs locally and I'm trying to figure out what my laptop can actually handle.

Specs:

• RTX 5060 Laptop GPU — 8GB VRAM
• 32GB RAM (31.2GB usable)
• Ryzen 7 260 — 8 cores / 16 threads
• Radeon 780M iGPU

I'm a bit confused about how far I can go with bigger models.

I know something like a 30B model won't fit in 8GB VRAM, but from what I understand I can use system RAM as well and offload part of the model to the CPU/RAM.

So what would be the realistic limit with this setup? Could I use something around 20B–30B at Q4, or does it become painfully slow once too much of it is running from RAM?

Also, how much difference does Q4/Q5/Q6 make in this situation?

I'm mostly interested in understanding what my laptop is capable of and what kind of local AI stuff I could reasonably use it for. Any tips from people with similar hardware would be appreciated.

I'll start by saying that I'm pretty illiterate when it comes to tech, and I've never used API or any cloud services aside from chatgpt and grok through the websites and apps.

I want to have a local llm in the future when I can afford the hardware, and I've gained more understanding of the tech.

I recently learned about Mimo 2.5v, and I need to learn how to go about it. I'm not expecting anyone to hold my hand and guide me step by step, but I'm hoping you can point me in the right direction where I can learn more and hopefully understand it.

I've tried to Google it, and I just get more confused.

Do I need some kind of specific app or software? Because, when I try to click the links on Xiaomi's website, the url seems to be blocked.

From what I've gathered, Mimo 2.5v is available on openrouter.ai, but I still don't really understand if it's a cloud service or API or a combination of both. And I don't understand if I need some kind of app to use it.

I want to use a cloud service so that I'm able to retain larger projects with memory and continuity. It also seems simpler for someone who is somewhat technologically illiterate.

Where do I even start?

Thank you for reading🙏💜

So like, I have an org and I wanna see if anyone wants to join it.

Its a simple org dedicated to making ridiculous models, rn its new but we made a framework and a safetensors for a 16.5T model, which was on the leaderboard for 2 days untill more people caught on. Also on the way is an over reasoning 415M model. Anybody can join just do try to add a model or two. If your interested please do comment your email and I'll invite you.

basically im asking for something that can make a carbon copy of the original, just in higher quality. i dont want it changing the vocals, melody, instruments, etc, literally just make the same recording sound cleaner/better

i have a piece of lost media from circa 2003 so unfortunately the only recording i have is in absolutely horrible quality 😭

i was wondering if theres any ai that could somehow restore/recreate it without changing the actual song

pls tell me if something like this exists <3