Yashsmith shah @Yashsmith_dev

Just an engineer trying to turn curiosity into code | 8x hackathon winner incl NVIDIA AI hack | Tech club chair | currently in the GenAI & startup rabbit hole San Francisco, CA Joined November 2021

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Yashsmith shah @Yashsmith_dev

a month ago

@sama how will they pay for the increasing model prices ? will it be free ? 😁

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Yashsmith shah @Yashsmith_dev

a month ago

@ShubhamInTech love the concept !!! if you want to power this with open source models and skip the cold start problems, check out @InferX any model, sub second wake times, zero infra stress

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@AnhPhuNguyen1 if you dont want to manage infra and don't want cold starts (most important for your product) you can always look at @InferXai , sub second cold starts , any model integration you ask for

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Yashsmith shah @Yashsmith_dev

a month ago

@im_roy_lee we at @InferXai have the infra to serve models who hold those insane contexts with sub second cold starts. exactly for your use case at InferX

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Yashsmith shah @Yashsmith_dev

a month ago

@Techmeme @dinabass throwing $ 615m at silicon proves how deep the pain point is. the next massive leap will be redefining the inference architecture itself. we at @InferXai are already on it.

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Yashsmith shah @Yashsmith_dev

a month ago

@ycombinator @sdianahu building new silicon for a serving and runtime issue is overkill inference is not a chip problem, it is a system problem agents need smarter scheduling and state management at the infra level to fix that 30-40% utilization gap that is exactly what we are solving at @InferXai

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Yashsmith shah @Yashsmith_dev

a month ago

@arankomatsuzaki 100%. building custom silicon for a serving runtime issue is wild. inference is a system problem, not a chip problem. that is exactly the layer we are building at @InferXai

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Yashsmith shah @Yashsmith_dev

a month ago

@0xSero @badlogicgames metrics and logs are crucial, but the bugs definitely aren't. @InferXai handles the serverless GPU inference smoothly so you can skip the setup headaches. might be a solid alternative for your workflows

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Yashsmith shah @Yashsmith_dev

a month ago

@TTT_Shogun spot on. if you're looking to power that OS with open-source models on serverless GPUs, check out @InferXai an execution layer needs zero cold starts to feel native, and we have that covered. DM me if you need any help

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Yashsmith shah @Yashsmith_dev

a month ago

@AradhyeAgarwal hey , I work at @InferXai and they have already been doing serverless inference for agentic workflows , you can build on top of it , DM me if you want to partner with us

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Yashsmith shah @Yashsmith_dev

2 months ago

@PMV_InferX @huggingface interesting!

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Yashsmith shah @Yashsmith_dev

2 months ago

@TheAhmadOsman U can always get more on @InferXai 2 h100s 👀

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Yashsmith shah @Yashsmith_dev

2 months ago

@AlexEngineerAI gwen distilled by Opus 4.6 inferenced by @InferXai truly is the best cost to quality ratio out there also got 30$ worth of free credits

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Yashsmith shah @Yashsmith_dev

2 months ago

@thdxr he math is right, but it ignores utilization. Step 2 falls apart if your GPUs sit idle during 2 minute cold starts. You need instant routing to make the economics work. This is exactly why the sub-second H100 spin ups on @InferXai actually matter for the bottom line.

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Yashsmith shah @Yashsmith_dev

2 months ago

Had a great and informative meet with @MrTalkStock ! never thought a person with more than 250k followers across all social media would be so nice to hop on a call to guide and help always grateful to all the mentors

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Yashsmith shah @Yashsmith_dev

2 months ago

@HarshithLucky3 its really great , u can test it out on @InferXai , they give around 30$ worth of free credits and 2 h100 gpu's to test it out yourself and integrate into your application

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Harsh Chandramania @HChandramania

2 months ago

@Spotify @Spotifyindia a solution to this would be really helpful. Would appreciate if any of my followers here could repost this. P.S. had to post it as images because X won't just let me type the whole thing :(

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Yashsmith shah @Yashsmith_dev

2 months ago

@outsource_ Fitting weights in 16GB is cool, but agent loops require huge context windows that will instantly OOM that setup. The quantization hit is not worth it. I just use this exact Qwen variant on @InferXai One click for the uncompromised version

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Yashsmith shah @Yashsmith_dev

2 months ago

@XunWallace The math does not work. A 4090 has 24GB VRAM, but a 70B model needs ~40GB You will still bottleneck offloading to Mac RAM This is why I use @InferXai now Sub-second cold starts on dual H100s feel completely local but actually hold the model