The two mechanisms keep very different amounts of state, and their constraints differ accordingly. Everything in this table comes from the vLLM docs pages for the two features as of today. One number from outside vLLM gives a sense of…
You pay a router for a big model. A good answer comes back. Was it the model you paid for? Anthropic's Frontier Red Team says the answer itself can't tell you: "Judging from the model's output alone is unreliable, even…
Check every row before you pull a single byte. Most failed installs trace back to a driver or image mismatch, not to the model. You also need a shell session on a Linux machine with substantial multi-GPU infrastructure, since the…
Gemma 4 in Rust on TorchTPU: Hands On With Colab ran the same Rust engine in a Colab notebook. This rig takes it to a TPU VM of its own and keeps it there: The checkpoint is the W4A16 repack xbill9/gemma-4-E2B-it-qat…
This article provides a step by step guide to running Gemma 4 E2B's quantization ladder, BF16 down to Q2_K, on four NVIDIA GPUs with the same nine GGUF files and the same llama.cpp build, and comparing the four cards question by question.
This article provides a step by step guide to running Gemma 4 E2B with the model written in Rust and every operation executed by TorchTPU, on a Colab v5e-1 runtime: one TPU v5e chip. A Rust crate reads the checkpoint, unpacks the 4-bit weights,…
Originally published on mrsaynothing.dev — the local-LLM cluster lives there. Through WSL2, and it stops feeling like Windows fast. WSL2 is Microsoft officially admitting the best feature in Windows is Linux. It ships with the OS, installs in one line, and once…
Before the log, the short version: The rest is how I got there. I wanted a workload that would exercise TorchTPU's execution modes, its numerical defaults and its profiler, and that was small enough to iterate on quickly. Attribution fit.
Test rig: 2× H100 SXM5 80GB (NVLink 900 GB/s), Xeon Platinum 8480C, Ubuntu 24.04, CUDA 12.8, driver 570.x. Workloads: Llama-3.1-8B-Instruct (FP8 + FP16), Qwen2.5-72B-Instruct (FP8, TP=2), ShareGPT-3 truncated to 1,024 prompts, plus a synthetic 128-in/512-out uniform-length load (worst case for continuous batching variance). ISL/OSL figures denote input/output sequence…
Here is where each project stood on October 28, 2026, based on release pages and vendor documentation fetched that day. LM Studio carries a naming wrinkle too. Its site now leads with a product called Bionic, an agent for work and code…
Serving runtimes, Triton, vLLM, ONNX Runtime, and scalable endpoints.
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