As local LLM inference becomes a standard part of the DevOps toolkit in 2026, many engineers are realizing that default Linux kernel parameters aren't optimized for the unique memory and IO patterns of models like Llama 4 or DeepSeek-V3.
Running inference at scale requires high-bandwidth memory access and low-latency IO. Here are three critical Linux optimizations to squeeze every token per second out of your self-hosted AI stack.
1. HugePages: Reducing Memory Overhead
When loading a 70B parameter model, your system is dealing with massive contiguous chunks of RAM. Using standard 4KB page sizes leads to significant TLB (Translation Lookaside Buffer) misses.
The Fix: Enable Transparent HugePages (THP) or, better yet, pre-allocate Static HugePages for your inference engine (vLLM, Ollama, or llama.cpp).
# Check current THP status
cat /sys/kernel/mm/transparent_hugepage/enabled
# Set to always for high-performance workloads
echo always | sudo tee /sys/kernel/mm/transparent_hugepage/enabled
2. io_uring: Async IO for Model Loading
Waiting 30 seconds for a model to load from an NVMe drive is 29 seconds too long. Modern inference engines use io_uring to maximize NVMe throughput without blocking the CPU.
The Fix: Ensure your kernel is 6.1+ and your inference binary is linked against liburing. If you're using Docker, ensure the container has sufficient permissions to use the io_uring syscall.
3. Tuning Swappiness for High-Pressure Context
When your context window hits 128k tokens, the KV cache can suddenly spike memory usage. If your vm.swappiness is at the default (usually 60), the kernel might start swapping out critical model weights prematurely.
The Fix: Lower your swappiness to keep weights in physical RAM as long as possible.
# Check current swappiness
sysctl vm.swappiness
# Lower to 10 for AI workloads
sudo sysctl -w vm.swappiness=10
Summary
Linux is the world's best AI platform, but it assumes a general-purpose workload by default. By tuning memory paging, utilizing modern async IO, and protecting your RAM from aggressive swapping, you can significantly improve the responsiveness of your local digital familiars.
What's your go-to kernel flag for performance? Let's discuss in the comments! 🌙
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