Loading video...

Video Failed to Load

Go Home

This actually makes Gemma 4 26B-4A usable for a coding agent @ 72tk/s on my MacBook Pro M1 Max. This video is realtime, running completely locally.

Freerunnering's profile picture

Kyle Howells

4,153 subscribers

119,560 views • 3 months ago •via X (Twitter)

EducationScience & Technology
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Here is gemma-4-26B-A4B-it on A17 Pro chip w/8GB memory ( MacBook Neo) ~ 7 t/s running on AMX ( GPU is slower on A17) Gemma's 4 expert is x2.3 larger than Qwen See Qwen 35B below
0:41

Here is gemma-4-26B-A4B-it on A17 Pro chip w/8GB memory ( MacBook Neo) ~ 7 t/s running on AMX ( GPU is slower on A17) Gemma's 4 expert is x2.3 larger than Qwen See Qwen 35B below

Anemll

195,282 views • 5 months ago

Apple Silicon + Gemma 4 fans: this is for you. Pico AI Server now supports continuous batching with MLX-Swift. 43 tok/s on 1 stream. 26 tok/s per stream on 2 concurrent streams. That’s 52 tok/s total. a 21% throughput gain on a six-year-old MacBook Pro M1 Max!
1:41

Apple Silicon + Gemma 4 fans: this is for you. Pico AI Server now supports continuous batching with MLX-Swift. 43 tok/s on 1 stream. 26 tok/s per stream on 2 concurrent streams. That’s 52 tok/s total. a 21% throughput gain on a six-year-old MacBook Pro M1 Max!

Ronald Mannak

51,354 views • 5 months ago

This, my friends is Ubuntu running on bare metal on an M1 Pro MacBook Pro, Pro. (To my Apple Fanboys… GET REKT LOSERS!!!) 😎 mRr3b00t
0:32

This, my friends is Ubuntu running on bare metal on an M1 Pro MacBook Pro, Pro. (To my Apple Fanboys… GET REKT LOSERS!!!) 😎 mRr3b00t

denvercoder

1,132,971 views • 2 years ago

What does it take to run 3, 5, or even 10 concurrent instances of Gemma 4 locally? We've open-sourced a demo letting you run multiple models side-by-side on your hardware. Gemma 4 26B A4B easily runs 10+ concurrent requests on a MacBook Pro M4 Max at 18 tokens/sec per request.
0:48

What does it take to run 3, 5, or even 10 concurrent instances of Gemma 4 locally? We've open-sourced a demo letting you run multiple models side-by-side on your hardware. Gemma 4 26B A4B easily runs 10+ concurrent requests on a MacBook Pro M4 Max at 18 tokens/sec per request.

Google Gemma

914,611 views • 5 months ago

I’ve been using the new MacBook Pro M5 since launch and it’s a beast, especially compared to my old M1! It’s good for running the smaller AI models locally but also everything you do is just so fast. Here’s Gemma 3 QAT 12B running with MLX:
0:35

I’ve been using the new MacBook Pro M5 since launch and it’s a beast, especially compared to my old M1! It’s good for running the smaller AI models locally but also everything you do is just so fast. Here’s Gemma 3 QAT 12B running with MLX:

Adrien Grondin

101,950 views • 9 months ago

gemma 4 on my macbook pro, M4 max, 36 GB ram is crazy fast for how good it is i think i have a cool project idea for it 👀
1:08

gemma 4 on my macbook pro, M4 max, 36 GB ram is crazy fast for how good it is i think i have a cool project idea for it 👀

vogel

69,656 views • 5 months ago

The top question I got was does Halo support local LLM running on a Mac. Here's an example of the app using gemma-4-26b-a4b-qat via LM Studio running on a base M5/32GB. It's giving an average of 40 tok/s and in my brief testing is able to execute all tool calls correctly. Very usable with this app.
0:37

The top question I got was does Halo support local LLM running on a Mac. Here's an example of the app using gemma-4-26b-a4b-qat via LM Studio running on a base M5/32GB. It's giving an average of 40 tok/s and in my brief testing is able to execute all tool calls correctly. Very usable with this app.

Tanmay

11,682 views • 2 months ago

CLAUDE CODE IS NOW ACTUALLY FREE. Not a trial, not a workaround, just a full coding agent stack running with models like GLM 5.1, Gemma 4, and Elephant Alpha.
5:33

CLAUDE CODE IS NOW ACTUALLY FREE. Not a trial, not a workaround, just a full coding agent stack running with models like GLM 5.1, Gemma 4, and Elephant Alpha.

0xMarioNawfal

131,111 views • 5 months ago

Teamwork makes the dream work. Now running locally. Watch Gemma 4 26B orchestrate 10 parallel sub-agents to code an SVG art gallery in seconds. Hitting 100+ tokens/sec, imagine how you can scale this for complex tasks or local chatbots for entire teams!!
1:30

Teamwork makes the dream work. Now running locally. Watch Gemma 4 26B orchestrate 10 parallel sub-agents to code an SVG art gallery in seconds. Hitting 100+ tokens/sec, imagine how you can scale this for complex tasks or local chatbots for entire teams!!

Google Gemma

158,343 views • 3 months ago

This afternoon I picked up a new Nvidia DGX Spark computer with the goal of trying to run Gemma 4 31b (4bit) on it locally as a server. Just 1.5 hours later, it’s working! Using Open WebUI on my MacBook as the interface and it’s connecting to my DGX Spark running as a Gemma 4 server.
0:46

This afternoon I picked up a new Nvidia DGX Spark computer with the goal of trying to run Gemma 4 31b (4bit) on it locally as a server. Just 1.5 hours later, it’s working! Using Open WebUI on my MacBook as the interface and it’s connecting to my DGX Spark running as a Gemma 4 server.

Dave Lee

140,876 views • 5 months ago

🤯 You can now easily face-swap any video, all locally on your Macbook. All you need is a photo and a video. FaceFusion is an open-source face swapper/enhancer with a simple web interface. On an M1/M2 Macbook, it even makes use of Core ML!
0:27

🤯 You can now easily face-swap any video, all locally on your Macbook. All you need is a photo and a video. FaceFusion is an open-source face swapper/enhancer with a simple web interface. On an M1/M2 Macbook, it even makes use of Core ML!

Marcel Pociot 🧪

206,509 views • 3 years ago

Hermes Agent Nous Research + Gemma 4 26B Google DeepMind, fully working on local Mac 🤯: one prompt → full CLI app + tests + pytest passing tool calls firing back to back — local Claude Code vibes Gemma 4 MoE is fast AND smart, and tool calling just works — powered by Rapid-MLX which natively parses Gemma 4's tool format (no other local backend can do this yet) Two lines to try it: pip install rapid-mlx & rapid-mlx serve gemma-4-26b pip install hermes-agent && hermes @teknaborat @siaborisov #HermesAgent #Gemma4 #LocalLLM
0:21

Hermes Agent Nous Research + Gemma 4 26B Google DeepMind, fully working on local Mac 🤯: one prompt → full CLI app + tests + pytest passing tool calls firing back to back — local Claude Code vibes Gemma 4 MoE is fast AND smart, and tool calling just works — powered by Rapid-MLX which natively parses Gemma 4's tool format (no other local backend can do this yet) Two lines to try it: pip install rapid-mlx & rapid-mlx serve gemma-4-26b pip install hermes-agent && hermes @teknaborat @siaborisov #HermesAgent #Gemma4 #LocalLLM

raullen

24,988 views • 5 months ago

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop
0:36

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

atomic.chat

152,658 views • 3 months ago

You can now vibecode your own WisprFlow or Monologue alternative that runs completely locally on Apple Silicon using MLX-Audio-Swift 🔥 Check out this live transcription of Dwarkesh Patel interview with Andrej Karpathy using Qwen3-ASR-0.6B quantized to 4bit on a M3 Max. It also runs in realtime on a iPhone 15 Pro and iPad Pro M1. No cloud. No API keys.
0:21

You can now vibecode your own WisprFlow or Monologue alternative that runs completely locally on Apple Silicon using MLX-Audio-Swift 🔥 Check out this live transcription of Dwarkesh Patel interview with Andrej Karpathy using Qwen3-ASR-0.6B quantized to 4bit on a M3 Max. It also runs in realtime on a iPhone 15 Pro and iPad Pro M1. No cloud. No API keys.

Prince Canuma

60,974 views • 7 months ago

This video is at normal speed. Gemma 4 12B MLX version running locally at 50 tokens/sec. Thank you Google DeepMind team. This model feels really solid for a lot of small local tasks and everyday AI workflows.
0:50

This video is at normal speed. Gemma 4 12B MLX version running locally at 50 tokens/sec. Thank you Google DeepMind team. This model feels really solid for a lot of small local tasks and everyday AI workflows.

AshutoshShrivastava

19,851 views • 3 months ago

Running Gemma 4 12B locally at 28T/s. Facing issues with vision in LM Studio, but text performance is quite solid. Asked it to create a landing page for a tech event. Video is fast forwarded for the demo.
0:40

Running Gemma 4 12B locally at 28T/s. Facing issues with vision in LM Studio, but text performance is quite solid. Asked it to create a landing page for a tech event. Video is fast forwarded for the demo.

AshutoshShrivastava

15,697 views • 3 months ago

Let me demonstrate the true power of llama.cpp: - Running on Mac Studio M2 Ultra (3 years old) - Gemma 4 26B A4B Q8_0 (full quality) - Built-in WebUI (ships with llama.cpp) - MCP support out of the box (web-search, HF, github, etc.) - Prompt speculative decoding The result: 300t/s (realtime video)
0:59

Let me demonstrate the true power of llama.cpp: - Running on Mac Studio M2 Ultra (3 years old) - Gemma 4 26B A4B Q8_0 (full quality) - Built-in WebUI (ships with llama.cpp) - MCP support out of the box (web-search, HF, github, etc.) - Prompt speculative decoding The result: 300t/s (realtime video)

Georgi Gerganov

785,456 views • 5 months ago

Unplugging completely! No WiFi and zero notifications. A great way to get deep focus on a project. Here is a walkthrough showing how to run Gemma 4 (26B A4B) fully offline with LM Studio & OpenCode to parse PDFs, ask questions, and build sites 100% locally.
15:06

Unplugging completely! No WiFi and zero notifications. A great way to get deep focus on a project. Here is a walkthrough showing how to run Gemma 4 (26B A4B) fully offline with LM Studio & OpenCode to parse PDFs, ask questions, and build sites 100% locally.

Google Gemma

236,747 views • 5 months ago

1-Click Vibevoice-Realtime for ALL machines Finally a Realtime TTS that is ACTUALLY realtime, thanks to its tiny size! (0.5B params) This video is from my Windows machine, recorded in realtime. All you need is around 2.5GB VRAM, it even works on Macs!
1:16

1-Click Vibevoice-Realtime for ALL machines Finally a Realtime TTS that is ACTUALLY realtime, thanks to its tiny size! (0.5B params) This video is from my Windows machine, recorded in realtime. All you need is around 2.5GB VRAM, it even works on Macs!

cocktail peanut

37,149 views • 9 months ago