DeepSeek's More Advanced V4 AI Model Could Run on Huawei Chips - Republic World
DeepSeek's More Advanced V4 AI Model Could Run on Huawei Chips Republic World
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Is Turboquant really a game changer?
I am currently utilizing qwen3.5 and Gemma 4 model. Realized Gemma 4 requires 2x ram for same context length. As far as I understand, what turbo quant gives is quantizing kv cache into about 4 bit and minimize the loses But Q8 still not lose the context that much so isn't kv cache ram for qwen 3.5 q8 and Gemma 4 truboquant is the same? Is turboquant also applicable in qwen's cache architecture? because as far as I know they didn't tested it in qwen3.5 style kv cache in their paper. Just curious, I started to learn local LLM recently submitted by /u/Interesting-Print366 [link] [comments]
Running Gemma 4 e4b (9.6GB RAM req) on RPi 5 8GB! Stable 2.8GHz Overclock & Custom Cooling
Finally got the Gemma 4 (E4B) model running on my Raspberry Pi 5 (8GB). Since the model requires about 9.6GB of RAM, I had to get creative with memory management. The Setup: Raspberry Pi OS. Lexar SSD (Essential for fast Swap). Memory Management: Combined ZRAM and RAM Swap to bridge the gap. It's a bit slow, but it works stably! Overclock: Pushed to 2.8GHz (arm_freq=2800) to help with the heavy lifting. Thermal Success: Using a custom DIY "stacked fan" cooling rig. Even under 100% load during long generations, temps stay solid between 50°C and 55°C. It's not the fastest Al rig, but seeing a Pi 5 handle a model larger than its physical RAM is amazing! submitted by /u/AncientWin9492 [link] [comments]
We absolutely need Qwen3.6-397B-A17B to be open source
The benchmarks may not show it but it's a substantial improvement over 3.5 for real world tasks. This model is performing better than GLM-5.1 and Kimi-k2.5 for me, and the biggest area of improvement has been reliability. It feels as reliable as claude in getting shit done end to end and not mess up half way and waste hours. This is the first OS model that has actually felt like I can compare it to Claude Sonnet. We have been comparing OS models with claude sonnet and opus left and right months now, they do show that they are close in benchmarks but fall apart in the real world, the models that are claimed to be close to opus haven't even been able to achieve Sonnet level quality in my real world usage. This is the first model I can confidently say very closely matches Sonnet. And before s
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More in Models
Is Turboquant really a game changer?
I am currently utilizing qwen3.5 and Gemma 4 model. Realized Gemma 4 requires 2x ram for same context length. As far as I understand, what turbo quant gives is quantizing kv cache into about 4 bit and minimize the loses But Q8 still not lose the context that much so isn't kv cache ram for qwen 3.5 q8 and Gemma 4 truboquant is the same? Is turboquant also applicable in qwen's cache architecture? because as far as I know they didn't tested it in qwen3.5 style kv cache in their paper. Just curious, I started to learn local LLM recently submitted by /u/Interesting-Print366 [link] [comments]
Claude Code replacement
I'm looking to build a local setup for coding since using Claude Code has been kind of poor experience last 2 weeks. I'm pondering between 2 or 4 V100 (32GB) and 2 or 4 MI50 (32GB) GPUs to support this. I understand V100 should be snappier to respond but MI50 is newer. What would be best way to go here? submitted by /u/NoTruth6718 [link] [comments]
Found how to toggle reasoning mode for Gemma in LM-Studio!
I’ve figured out how to trigger the reasoning process by adding "/think" to the system prompt. Heads up: the thought tags have an unusual pipe ( | ) placement, which is why many LLM fail to parse the reasoning section correctly. So Start String is : " thought" And End String is " " Here is the Jinja template: https://pastebin.com/MGmD8UiC Tested and working with the 26B and 31B versions. submitted by /u/Adventurous-Paper566 [link] [comments]
Running Gemma 4 e4b (9.6GB RAM req) on RPi 5 8GB! Stable 2.8GHz Overclock & Custom Cooling
Finally got the Gemma 4 (E4B) model running on my Raspberry Pi 5 (8GB). Since the model requires about 9.6GB of RAM, I had to get creative with memory management. The Setup: Raspberry Pi OS. Lexar SSD (Essential for fast Swap). Memory Management: Combined ZRAM and RAM Swap to bridge the gap. It's a bit slow, but it works stably! Overclock: Pushed to 2.8GHz (arm_freq=2800) to help with the heavy lifting. Thermal Success: Using a custom DIY "stacked fan" cooling rig. Even under 100% load during long generations, temps stay solid between 50°C and 55°C. It's not the fastest Al rig, but seeing a Pi 5 handle a model larger than its physical RAM is amazing! submitted by /u/AncientWin9492 [link] [comments]
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