@the_steve_tiktok: ##қазақшамем #рекомендации

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Friday 09 October 2026 17:01:57 GMT

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Злой декстер :

Рахмет тепкілеп кетті

2026-10-10 06:39:08

926

𝐧𝐮𝐫𝐦𝐚𝐱𝐱 :

рақмет пышақтап кетті

2026-10-10 07:17:14

47

Romamyn🙋 :

мынаны аитсам шапалақ жеп қаламау

2026-10-09 18:35:19

581

айдока♊️ :

неге бул ректе емес😭😭✌🏻✌🏻

2026-10-09 17:15:13

60

Om1rbekov.🌐 :

менің рекомендациям

2026-10-09 19:15:01

230

𝐓𝐚𝐡𝐢𝐫𝐣𝐚𝐧𝐨𝐯🫀 :

А бұл фразама

2026-10-10 16:51:36

2

𝕄𝕒𝕘𝕫𝕙𝕒𝕟 :

жеп қаламызау сол қоя салаиық

2026-10-09 18:09:56

18

Dimaaas⚡ :

строис калай

2026-10-10 10:55:02

1

Shahknaz 🤍 :

Жегізем деп жеп қаламго

2026-10-10 13:58:56

6

⚰️♱₊•⁺.⋆𝑺𝒂𝒌𝒖⋆.⁺•✮🧟‍♀️ :

Рахмет мени урып, помойкага тастап жерди

2026-10-10 07:27:12

13

user06600909515 :

осы сөздерді қолданатын достарым бар👇

2026-10-10 08:31:42

1

azhar👾 :

Рахмет досыма барып айтып келейің

2026-10-10 10:49:52

3

𝓐𝓲𝓴𝓱𝓪𝓷𝔂𝓶. :

рахмет өлтырып кетты.

2026-10-10 08:41:39

1

ilkha 🦈 :

2012-2013 СТС кеты

2026-10-10 09:35:29

1

JustNobody :

2026-10-10 05:01:26

2

@ @balau :

Айтсам жеп калмайм ба

2026-10-10 09:32:59

1

broken. :

йоо, бро бұл мені суықтатты🥶🥶

2026-10-09 19:10:09

1

Muzaa  :

2026-10-10 09:12:56

1

￴ ￴￴￴￴ ￴￴￴￴￴ ￴￴ ￴￴￴￴￴￴￴￴￴ ￴ ￴ :

Енді қолданатын болам

2026-10-09 17:04:52

4

𝐷𝑖𝑛 :

2026-10-09 18:23:36

1

▪️ :

менде ослай сойлим😏😏😎😎😉😉😉😉

2026-10-10 10:39:17

3

ali_mine⛏️🟩🟫 :

кыттайларга шамалы комекко

2026-10-09 17:24:52

1

Savi :

қай жақа баратсын десе -арғы дүниеге баратырмын деп айтам мен.

2026-10-10 14:20:01

0

beksultan17 :

срочно шинам

2026-10-10 20:47:16

0

. :

2026-10-09 18:01:54

0

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Running powerful AI locally is becoming far more practical—but choosing the wrong model can leave you with slow responses, memory errors, or hardware that simply cannot cope. Gemma 4 is Google’s open-weight multimodal model family, available in E2B, E4B, 12B Unified, 26B A4B and 31B variants, covering everything from mobile and edge deployments to consumer GPUs and high-end workstations. The family supports text and image understanding, while E2B, E4B and 12B also support audio input. This carousel breaks down what each model is designed for, the difference between active and total parameters, practical RAM and VRAM considerations, multimodal capabilities, quantisation options and the local deployment workflow. The goal is not to download the biggest model available—it is to choose the smallest model that delivers the quality your workload actually needs. Quantised GGUF versions can make local deployment more accessible by reducing memory requirements, and tools such as Ollama and llama.cpp allow supported Gemma models to run directly on laptops and desktops, including smaller CPU-only setups. Performance will still depend on your exact checkpoint, quantisation level, context length, runtime and hardware configuration, so treat hardware figures as starting estimates rather than guaranteed requirements. Save this guide before building your local AI setup, share it with someone experimenting with open models, and comment with your RAM, GPU and VRAM—which Gemma 4 version would you run? #techserks #gemma4 #localai #artificialintelligence #opensourceai

Running powerful AI locally is becoming far more practical—but choosing the wrong model can leave you with slow responses, memory errors, or hardware that simply cannot cope. Gemma 4 is Google’s open-weight multimodal model family, available in E2B, E4B, 12B Unified, 26B A4B and 31B variants, covering everything from mobile and edge deployments to consumer GPUs and high-end workstations. The family supports text and image understanding, while E2B, E4B and 12B also support audio input. This carousel breaks down what each model is designed for, the difference between active and total parameters, practical RAM and VRAM considerations, multimodal capabilities, quantisation options and the local deployment workflow. The goal is not to download the biggest model available—it is to choose the smallest model that delivers the quality your workload actually needs. Quantised GGUF versions can make local deployment more accessible by reducing memory requirements, and tools such as Ollama and llama.cpp allow supported Gemma models to run directly on laptops and desktops, including smaller CPU-only setups. Performance will still depend on your exact checkpoint, quantisation level, context length, runtime and hardware configuration, so treat hardware figures as starting estimates rather than guaranteed requirements. Save this guide before building your local AI setup, share it with someone experimenting with open models, and comment with your RAM, GPU and VRAM—which Gemma 4 version would you run? #techserks #gemma4 #localai #artificialintelligence #opensourceai

“هداج🤩 #HMA#فولو #أضفتم #القريات تيك_توك #عشّاق @هداج 🇸🇦

“هداج🤩 #HMA#فولو #أضفتم #القريات تيك_توك #عشّاق @هداج 🇸🇦

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