How to Maximize Your Coding Agent Subscriptions
Get more out of your coding agent subscriptions The post How to Maximize Your Coding Agent Subscriptions appeared first on Towards Data Science.
Towards Data Science9 min read
LLM Inference
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Deploy AI Apps on CPU, Not a GPU
Full Video: https://youtu.be/6wBW0KRZi94 Join as a member to access AI Projects & Member only videos. https://www.youtube.com/channel/UCAHa1-TnXAJrkDVMHbcP1Sw/join Do you really need a GPU to build and deploy AI applications? Many beginners think that every AI application needs a GPU.
Applied with AIWhen Do You Really Need a GPU in AI? Simple Explanation.
Join as a member to access AI Projects & Member only videos. https://www.youtube.com/channel/UCAHa1-TnXAJrkDVMHbcP1Sw/join Do you really need a GPU to build and deploy AI applications? Many beginners think that every AI application needs a GPU. But that is not true.
Applied with AILossless Inference
How to make LLM serving faster without touching the model. Exact kernels, speculative decoding, lossless compression, KV reuse and scheduling, with the math and how to verify it with logit parity.
RunInfra Blog8 min readThe fastest way to serve DeepSeek V4 Flash
Historical measurements published August 2, 2026 for DeepSeek V4 Flash 0731 on four B200 GPUs. As of September 21, 2026, no current measured optimization package is published for it.
RunInfra Blog8 min read$0.09 and $290.12: What Actually Moves Your Inference Bill
Nine cents and $290.12 are both prices for one million output tokens. The gap is driven by accelerator, provider, concurrency, utilization, and reasoning token volume.
RunInfra Blog10 min readServing Kimi K3 on vLLM was hard. Here is what we measured.
No released vLLM build served Kimi K3 when we started. We resolved several distinct failures on 8x B300 and measured the result, its operating crossover, and its honest limits.
RunInfra Blog10 min readvLLM vs SGLang vs TensorRT-LLM: a serving benchmark
Self-reported Llama-3.1-8B results for three LLM inference engines. Published unique-prompt throughput and latency rows cover H100 bf16. FP8 and L40S appear only in derived cost results. The harness and raw data are not publicly available.
RunInfra Blog9 min read
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