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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.

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Kyle Howells

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118,769 просмотров • 29 дней назад •via X (Twitter)

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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
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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

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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!
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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!

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

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1,132,878 просмотров • 2 лет назад

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.
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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

912,570 просмотров • 2 месяцев назад

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:
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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 просмотров • 7 месяцев назад

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 👀
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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

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Semi-realtime Stable Diffusion image-to-image (Van Gogh style), Unity plugin based on Apple's Core ML port. The latency is about 2 seconds on M1 Max MacBook Pro.
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Semi-realtime Stable Diffusion image-to-image (Van Gogh style), Unity plugin based on Apple's Core ML port. The latency is about 2 seconds on M1 Max MacBook Pro.

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292,941 просмотров • 3 лет назад

gpt-oss 120b running at 50 tokens/s locally on my macbook
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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.
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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.

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11,351 просмотров • 12 дней назад

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.
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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.

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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!!
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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!!

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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.
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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.

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138,489 просмотров • 3 месяцев назад

Le nouveau MacBook Pro M5 Pro/Max a été annoncé ! → Je me le prends ou pas ? 🤔 (je suis sous MacBook Pro M1 Max)
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Le nouveau MacBook Pro M5 Pro/Max a été annoncé ! → Je me le prends ou pas ? 🤔 (je suis sous MacBook Pro M1 Max)

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Running GLM-4.7-Flash with OpenCode locally on M4 Max MacBook Pro. 4-bit model runs at 82 tok/sec. Prefill will get ~4x faster with M5 Max MacBook Pro (~28 Jan). EXO will also support disaggregating prefill and decode across devices, e.g. DGX Spark.
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Running GLM-4.7-Flash with OpenCode locally on M4 Max MacBook Pro. 4-bit model runs at 82 tok/sec. Prefill will get ~4x faster with M5 Max MacBook Pro (~28 Jan). EXO will also support disaggregating prefill and decode across devices, e.g. DGX Spark.

Alex Cheema

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🤯 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!

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HOLY SMOKES! Apple's new MacBook Neo with the A18 Pro iPhone chip can actually handle 3D video rendering in Blender! It's over TWICE as fast as the M1 MacBook Air! Full Neo vs M1 Air comparison video is LIVE on YouTube:

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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

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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
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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

151,491 просмотров • 1 месяц назад

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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.

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DeepSeek v4 PRO running via SSD streaming on my 128GB MacBook m5 max. 1.6 trillion parameters.
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