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I've been wanting to run open weight models lately to give them a shot with OpenCode. However, I get the impression that models like Qwen and Kimi k3 are impossible to run locally? I have a RTX 5090 and 64 GB of RAM but the models seem to be much larger than that. What's the route to start using these models? Bedrock?


Bedrock seems to have stopped adding new open-weights models, and mostly only has Anthropic and OpenAI stuff now. You can get Qwen 3.8 directly from Alibaba: https://www.qwencloud.com (proprietary variant) or from DigitalOcean (this variant, probably also from others soon).

On your 5090 you could easily run a smaller model like Qwen 3.6 27B: https://huggingface.co/collections/Qwen/qwen36 or Gemma 4 etc., or as mentioned there's a Qwen 3.8 27B coming out in a few days.


What does the number before the B signify?


Its number of parameters. The 'B' is billions. If you have bf16 weights each parameter would be 16 bits.


OpenRouter is (roughly) a single proxy between you + many different models + providers. it works with opencode (+ many other products), and is relatively convenient for trying out a bunch of models.

for example, they already have qwen3.8-max

https://openrouter.ai/discover?model=qwen/qwen3.8-max

note that they add some fee ontop of things (maybe 10% of spend?). it isn't htat big of a deal for general experimentation, but if you end up wanting to use a single model in a higher-volume way, it likely makes sense to cut them out of your stack.


You could easily run any of their 30B-or-less models which is what most people are waiting for.

Apparently the ~30B variant will be released on Friday?


Define "easily". My laptop (2023) was configured with 16gb.

Getting to the point where I was able to run a 30B model required $500 in memory.


I think he is specificlly talking to the person with "RTX 5090 and 64 GB of RAM"

Not the greater "you"


5090 is plenty for the Q4_K_M quantized version of 3.6 27B with reduced context size.

I run it on a 3090(24GB) and 64k context using GGUF format and llama-cpp. Double 3090 gives you 128k, quad 3090 gets you to full context - 256k.


Or you can run quantized context, there's some degradation but it fits in a lot less.


Fireworks or OpenCode Go




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