Gpu

Got a Lunar Lake laptop and went looking for Intel’s Arc Control app on Linux? It doesn’t exist. Intel only ships Arc Control for Windows. Linux users get a community tool instead: LACT , the Linux GPU Configuration and Monitoring Tool. It covers temperature, power limits, clock speeds, and voltage through a proper GUI. For live performance data, intel_gpu_top and nvtop handle the rest from the terminal.

Below: driver setup, LACT installation, CLI monitoring tools, power tuning, and the most common things that go wrong on a fresh install.

Linux 7.0 makes Rust a permanent part of the kernel development model. Kernel builds now use stable Rust releases anchored to the Debian stable toolchain. Drivers like NVIDIA’s Nova and Android’s ashmem already run on millions of devices. This policy change lets developers use a language that eliminates memory-safety bugs at compile time.

Why the Kernel Needed Rust in the First Place

Bringing Rust into the kernel wasn’t about ideology. About two-thirds of kernel security bugs come from memory issues like buffer overflows and use-after-free errors. These are the expected costs of writing software in C. Manual memory management gives control but lacks guardrails. One mistake can lead to a major exploit or a system crash.

For most local AI workloads in 2026, the RTX 5080 with 16 GB of GDDR7 is the better buy. It delivers 40-60 tokens per second on quantized 7B-13B parameter models at roughly half the price of the RTX 5090. The RTX 5090’s 32 GB of GDDR7 only justifies the premium if you regularly run 30B+ parameter models or full-precision fine-tuning jobs that cannot fit in 16 GB of VRAM. If either of those describes you, the 5090 earns its keep. If not, you are paying $1,000 extra for headroom you will not use.

For local AI image upscaling in 2026, Real-ESRGAN is the best free pick. It is fast and solid for most jobs. Topaz Photo AI gives the best overall quality with smart noise reduction and face recovery, but costs $199/year. SUPIR (Scaling Up to Excellence) makes the most detailed and lifelike output on badly degraded images. It needs 12+ GB of VRAM and runs 10-50x slower than the rest. The right pick depends on your workload: Real-ESRGAN for batch jobs and pipelines, Topaz for pro photo work, and SUPIR for one-off hero shots where time is not a factor.

Gemma 4 shipped on April 2, 2026 with four model variants under the Apache 2.0 license. The 31B dense model ranks third on the Arena AI text leaderboard with a score of 1452. The 26B MoE model scores 1441 while firing only 3.8B of its 26B total parameters per forward pass. So what design choices make this possible? Three of them break from the standard transformer recipe: Per-Layer Embeddings (PLE), Shared KV Cache, and Dual RoPE. Each one shifts the math for inference cost, memory use, and fine-tuning. The rest of this post covers those three, plus the Mixture-of-Experts layer and the multimodal encoders.

The short answer is no, the Gemma 4 26B MoE model will not fit entirely in 8 GB of VRAM at standard Q4_K_M quantization - the weights alone require roughly 16-18 GB. But with the right approach, you can run it on budget hardware and get usable interactive performance. The three practical strategies are aggressive quantization (IQ3_XS brings weights under 10 GB), GPU-CPU layer offloading (split 15-20 of 30 layers to GPU, rest on system RAM), and multi-GPU setups (two cheap 8 GB cards via tensor parallelism). Each involves different trade-offs between quality, speed, and hardware requirements.