Good luck!
Dmitrii Kostakov PRO
kostakoff
AI & ML interests
MLOps
Recent Activity
liked
a model
about 2 hours ago
zai-org/GLM-Image
Organizations
reacted to
nicolay-r's
post with 👍
about 3 hours ago
Post
121
📢 I know this space is mosly for sharing works, but in this case I am open to work 💼🇬🇧
I know there are outstanding research labs and teams following this space 🙌
I would genuinely love to learn on ways to contribute, learn from strong lab environments, and help shape ideas into working systems.
What I bring (https://nicolayr.com):
• Applied NLP & deployment LLM-powered worflows with reasoning for IR (LangChain, LiteLLM)
• Architectures Engeneering: Transformers and / or backends (PyTorch, Tensorflow, flaxformer)
• End-to-end engineering: Frontend (JS, ReactJS) → Backend REST APIs (FastAPI) / Keycloak → Docker / NGINX → Cloud / MLOps
• Domain-specific experience in Healthcare (deploy & handle: DICOM-SR/SEG, NIFTI, databases: ORTHANC, Frontend: OHIF / cornerstone)
• Pasion about open-source NLP tooling for handling data (https://github.com/nicolay-r)
Would be happy to connect or hear any relevant suggestions on seeking for team 🧩
I know there are outstanding research labs and teams following this space 🙌
I would genuinely love to learn on ways to contribute, learn from strong lab environments, and help shape ideas into working systems.
What I bring (https://nicolayr.com):
• Applied NLP & deployment LLM-powered worflows with reasoning for IR (LangChain, LiteLLM)
• Architectures Engeneering: Transformers and / or backends (PyTorch, Tensorflow, flaxformer)
• End-to-end engineering: Frontend (JS, ReactJS) → Backend REST APIs (FastAPI) / Keycloak → Docker / NGINX → Cloud / MLOps
• Domain-specific experience in Healthcare (deploy & handle: DICOM-SR/SEG, NIFTI, databases: ORTHANC, Frontend: OHIF / cornerstone)
• Pasion about open-source NLP tooling for handling data (https://github.com/nicolay-r)
Would be happy to connect or hear any relevant suggestions on seeking for team 🧩
-
- 1 reply
reacted to
darkc0de's
post with 👀
about 3 hours ago
Post
332
1440GB of VRAM is incredibly satisfying 😁
-
- 4 replies
AGPL-3.0 license - is a death sentence for software
https://github.com/p-e-w/heretic?tab=AGPL-3.0-1-ov-file#readme
Teams or Organizations will never use it
Excellent set!
I'll probably have something similar, but in my next life.
reacted to
mitkox's
post with ❤️
about 16 hours ago
Post
1667
My USB charger has a Blackwell GPU and 128GB RAM.
What. A. Time. To. Be. Alive.
People in Sofia: “It’s freezing.”
Me: sitting next to 3kW of space AI heaters on my desk 👀
1x GLM-5, 2x MiniMax-M2.5, 1x Qwen3 Coder Next; all on single Aibrix/K8s cluster
What. A. Time. To. Be. Alive.
People in Sofia: “It’s freezing.”
Me: sitting next to 3kW of space AI heaters on my desk 👀
1x GLM-5, 2x MiniMax-M2.5, 1x Qwen3 Coder Next; all on single Aibrix/K8s cluster
-
- 5 replies
posted
an
update
2 days ago
Post
2462
I found it very funny that the Hugging Face profile has a specific section where we can share our hardware.
It really brings back memories of the good old days when we used to flex our custom PC specs on enthusiast forums 20 years ago! That inspired me to fill out my own profile and share it here.
And this is my first set of GPUs that I am using to learn MLOps:
- RTX 3090 – the best one; unfortunately it doesn't support the latest FP8 and FP4, but it’s still very powerful.
- Tesla V100 – performance is almost like the RTX 3090, just much older.
- Tesla P100 – old, and doesn't have tensor cores, but still can handle small models.
- Radeon MI50 – old, similar to the P100, but uses ROCm instead of CUDA, which is actually a pretty good experience to setup.
- GTX 1080 Ti – mostly useless, no FP16 support.
- GTX 1660 – first generation of the Turing architecture, but mostly useless.
llmlaba
It really brings back memories of the good old days when we used to flex our custom PC specs on enthusiast forums 20 years ago! That inspired me to fill out my own profile and share it here.
And this is my first set of GPUs that I am using to learn MLOps:
- RTX 3090 – the best one; unfortunately it doesn't support the latest FP8 and FP4, but it’s still very powerful.
- Tesla V100 – performance is almost like the RTX 3090, just much older.
- Tesla P100 – old, and doesn't have tensor cores, but still can handle small models.
- Radeon MI50 – old, similar to the P100, but uses ROCm instead of CUDA, which is actually a pretty good experience to setup.
- GTX 1080 Ti – mostly useless, no FP16 support.
- GTX 1660 – first generation of the Turing architecture, but mostly useless.
- 4 replies
reacted to
their
post with 🤗
6 days ago
Post
3276
My home lab for AI models - llmlaba v1
After I began learning MLOps I realized that I needed some kind of home lab, there are a lot of GPUs that I need to learn how to set up and test.
So I spent some time to do a researching which platform I could buy or build.
My requirements ware:
- Limited budget
- Power supply 1 kW or higher
- Few PCIe slots to be able to install more than one gpu
- Zero maintenance cost, I don't want spend a lot of time or money to maintain lab hardware, except for the GPUs
I chose the Intel Mac Pro 7.1:
- Prices on eBay acceptable
- Excelent cooling
- 1.4 kW power supply
- 7 PCIe slots
- Zero maintenance: I don't need to do anything with the Mac Pro hardware; it just works
- Classic UEFI boot loader
It requires a bit of OS preparation:
1. Install Ubuntu 24.04 (it works with the general PC ISO image)
2. Set up T2 drivers
3. Install t2fanrd to manually manage fans (/etc/t2fand.conf) https://wiki.t2linux.org/guides/fan/
4. Fix PCIe BAR: add pci=realloc to GRUB_CMDLINE_LINUX_DEFAULT so the Linux kernel will properly initializes server GPUs without Graphics Output Protocol
5. Install NVIDIA GPU driver:
And it works!
I was able to run server-grade Nvidia Tesla P100 (required DIY air duct), and consumer Nvidia Titan X, Titan V, GTX 1080 cards on the old Mac Pro 7.1 - even three in parallel.
llmlaba
After I began learning MLOps I realized that I needed some kind of home lab, there are a lot of GPUs that I need to learn how to set up and test.
So I spent some time to do a researching which platform I could buy or build.
My requirements ware:
- Limited budget
- Power supply 1 kW or higher
- Few PCIe slots to be able to install more than one gpu
- Zero maintenance cost, I don't want spend a lot of time or money to maintain lab hardware, except for the GPUs
I chose the Intel Mac Pro 7.1:
- Prices on eBay acceptable
- Excelent cooling
- 1.4 kW power supply
- 7 PCIe slots
- Zero maintenance: I don't need to do anything with the Mac Pro hardware; it just works
- Classic UEFI boot loader
It requires a bit of OS preparation:
1. Install Ubuntu 24.04 (it works with the general PC ISO image)
2. Set up T2 drivers
sudo apt install -y dkms linux-headers-$(uname -r) applesmc-t2 apple-bce lm-sensors3. Install t2fanrd to manually manage fans (/etc/t2fand.conf) https://wiki.t2linux.org/guides/fan/
4. Fix PCIe BAR: add pci=realloc to GRUB_CMDLINE_LINUX_DEFAULT so the Linux kernel will properly initializes server GPUs without Graphics Output Protocol
5. Install NVIDIA GPU driver:
sudo apt install nvidia-driver-570And it works!
I was able to run server-grade Nvidia Tesla P100 (required DIY air duct), and consumer Nvidia Titan X, Titan V, GTX 1080 cards on the old Mac Pro 7.1 - even three in parallel.
- 3 replies
posted
an
update
9 days ago
Post
3276
My home lab for AI models - llmlaba v1
After I began learning MLOps I realized that I needed some kind of home lab, there are a lot of GPUs that I need to learn how to set up and test.
So I spent some time to do a researching which platform I could buy or build.
My requirements ware:
- Limited budget
- Power supply 1 kW or higher
- Few PCIe slots to be able to install more than one gpu
- Zero maintenance cost, I don't want spend a lot of time or money to maintain lab hardware, except for the GPUs
I chose the Intel Mac Pro 7.1:
- Prices on eBay acceptable
- Excelent cooling
- 1.4 kW power supply
- 7 PCIe slots
- Zero maintenance: I don't need to do anything with the Mac Pro hardware; it just works
- Classic UEFI boot loader
It requires a bit of OS preparation:
1. Install Ubuntu 24.04 (it works with the general PC ISO image)
2. Set up T2 drivers
3. Install t2fanrd to manually manage fans (/etc/t2fand.conf) https://wiki.t2linux.org/guides/fan/
4. Fix PCIe BAR: add pci=realloc to GRUB_CMDLINE_LINUX_DEFAULT so the Linux kernel will properly initializes server GPUs without Graphics Output Protocol
5. Install NVIDIA GPU driver:
And it works!
I was able to run server-grade Nvidia Tesla P100 (required DIY air duct), and consumer Nvidia Titan X, Titan V, GTX 1080 cards on the old Mac Pro 7.1 - even three in parallel.
llmlaba
After I began learning MLOps I realized that I needed some kind of home lab, there are a lot of GPUs that I need to learn how to set up and test.
So I spent some time to do a researching which platform I could buy or build.
My requirements ware:
- Limited budget
- Power supply 1 kW or higher
- Few PCIe slots to be able to install more than one gpu
- Zero maintenance cost, I don't want spend a lot of time or money to maintain lab hardware, except for the GPUs
I chose the Intel Mac Pro 7.1:
- Prices on eBay acceptable
- Excelent cooling
- 1.4 kW power supply
- 7 PCIe slots
- Zero maintenance: I don't need to do anything with the Mac Pro hardware; it just works
- Classic UEFI boot loader
It requires a bit of OS preparation:
1. Install Ubuntu 24.04 (it works with the general PC ISO image)
2. Set up T2 drivers
sudo apt install -y dkms linux-headers-$(uname -r) applesmc-t2 apple-bce lm-sensors3. Install t2fanrd to manually manage fans (/etc/t2fand.conf) https://wiki.t2linux.org/guides/fan/
4. Fix PCIe BAR: add pci=realloc to GRUB_CMDLINE_LINUX_DEFAULT so the Linux kernel will properly initializes server GPUs without Graphics Output Protocol
5. Install NVIDIA GPU driver:
sudo apt install nvidia-driver-570And it works!
I was able to run server-grade Nvidia Tesla P100 (required DIY air duct), and consumer Nvidia Titan X, Titan V, GTX 1080 cards on the old Mac Pro 7.1 - even three in parallel.
-
-
- 3 replies
posted
an
update
27 days ago
Post
807
I created list of models based on permissive license (apache2, mit, openrail) and raw fp16 weights.
LLM:
- Mistral 7b v1
- Falcon 7b
- GLM4 9b
- Olmo3 7b
- Yi 9b
- Qwen3 8b
- Internlm3 8B
- PHI4
Multimodal LLM:
- Pixtral 12b
- Qwen3-VL-8B-Instruct
Picture generation:
- Stable Diffusion 1.5
- Stable Diffusion 2.0
- Stable Diffusion XL
Video generation:
- WAN 2.1 VACE Diffusers
TTS:
- SUNO Bark
This can be very useful for those who are just starting their AI LLM journey in PyTorch, like me.
Suggestions in the comments are welcome.
LLM:
- Mistral 7b v1
- Falcon 7b
- GLM4 9b
- Olmo3 7b
- Yi 9b
- Qwen3 8b
- Internlm3 8B
- PHI4
Multimodal LLM:
- Pixtral 12b
- Qwen3-VL-8B-Instruct
Picture generation:
- Stable Diffusion 1.5
- Stable Diffusion 2.0
- Stable Diffusion XL
Video generation:
- WAN 2.1 VACE Diffusers
TTS:
- SUNO Bark
This can be very useful for those who are just starting their AI LLM journey in PyTorch, like me.
Suggestions in the comments are welcome.
reacted to
their
post with 🚀
about 1 month ago
Post
1522
Hi Everyone!
I am new in LLM AI, and I think it is very cool.
Thanks HF authors that Hugging Face exist. It is great place to start learning AI.
I am new in LLM AI, and I think it is very cool.
Thanks HF authors that Hugging Face exist. It is great place to start learning AI.
- 2 replies