These gains come on top of already-optimized baselines in kernels that were considered "done" by their authors. The improvements are the direct result of CompileIQ discovering compiler configurations that the default heuristics would never select.

Most auto-tuning tools optimize for a single metric, typically runtime. CompileIQ goes further, supporting multi-objective optimization, simultaneously exploring trade-offs across competing objectives like runtime, compile time, and power consumption.

In attention inference kernels, GEMMs in the linear layers of FFN/MLP blocks plus the Q, K, V, and output projections account for approximately 70% of total FLOPs. Scaled dot-product attention, fused and flash attention variants account for another 25%. Together, these two kernel families represent more than 90% of end-to-end inference compute.

NVIDIA GPU compilers apply the same default heuristics (register allocation strategies, instruction scheduling decisions, loop unrolling thresholds, etc.) to every kernel they compile. These heuristics are engineered to produce good results across a vast range of workloads. But "good across the board" and "optimal for your workload" are two very different things.

increase the ability of our trained Graph Neural Network (GNN) model to find feasible solutions for the problem from 14% to over 88%

Even with these improvements, Responses API overhead was too large relative to the speed of the model—that is, use

We approached this through caching, eliminating unnecessary network hops, improving our safety stack to quickly flag issues, and—most importantly—building a way to create a persistent connection to the Responses API, instead of having to make a series of synchronous API calls.

In the past, running LLM inference on GPUs was the slowest part of the agentic loop, so API service overhead was easy to hide.

The variance is also worth noting: baseline+FA TG has ±19 t/s of noise, while optimized+FA has ±0.59 t/s on x86. The fusions eliminate intermediate writes that pollute the cache, making the hot paths more predictable.

Coding agents working from code alone generate shallow hypotheses. Adding a research phase — arxiv papers, competing forks, other backends — produced 5 kernel fusions that made llama.cpp CPU inference 15% faster.

The variance is also worth noting: baseline+FA TG has ±19 t/s of noise, while optimized+FA has ±0.59 t/s on x86.

GLM-5.1 pushes this frontier further, delivering 3.6× speedup and continuing to make progress well into the run. While its rate of improvement also slows over time, it sustains useful optimization for substantially longer than GLM-5.

Artificial Analysis has also positioned Gemini 3.1 Flash TTS within its 'most attractive quadrant' for its ideal blend of high-quality speech generation and low cost.

Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak chip specifications.

why not allow block forwarding without capturing: foo(&) foo(1, 2, &)

It's typically taken for granted that better performance must require higher complexity. But I've often had the experience that making some component of a system faster allows the system as a whole to be simpler

The latest SQLite 3.8.7 alpha version is 50% faster than the 3.7.17 release from 16 months ago. [...] This is 50% faster at the low-level grunt work of moving bits on and off disk and search b-trees. We have achieved this by incorporating hundreds of micro-optimizations. Each micro-optimization might improve the performance by as little as 0.05%. If we get one that improves performance by 0.25%, that is considered a huge win. Each of these optimizations is unmeasurable on a real-world system (we have to use cachegrind to get repeatable run-times) but if you do enough of them, they add up.

Optimization in this case is nothing crazy, just something I neglected while designing the framework.

If a UTF8-encoded Ruby string contains unicode characters, then indexing into that string becomes O(N). This can lead to very bad performance in string_end_with_semicolon?, as it would have to scan through the whole buffer for every single file. This commit fixes it to use UTF32 if there are any non-ascii characters in the files.

What is the point of avoiding the semicolon in concat_javascript_sources

   (intermediate value)(...) is not a function
       at something.source.js:1

   ({
     // other.js
   })()
   (function() {
     // something.js
   })();

And no need to walk backwards through all these strings which is surprisingly inefficient in Ruby.

Since the common problem with concatenating JavaScript files is the lack of semicolons, automatically adding one (that, like Sam said, will then be removed by the minifier if it's unnecessary) seems on the surface to be a perfectly fine speed optimization.

reducing it down to one call significantly speeds up the operation.

I feel like the walk in string_end_with_semicolon? is unnecessarily expensive when having an extra semicolon doesn't invalidate any JavaScript syntax.

The template language's restrictions compared to JavaScript/JSX-built views are part of Svelte's performance story. It's able to optimize things ahead of time that are impossible with dynamic code because of the constraints. Here's a couple tweets from the author about that

It's fast. The Dart VM is highly optimized, and getting faster all the time (for the latest performance numbers, see perf.md). It's much faster than Ruby, and close to par with C++.

In the vast majority of cases there’s nothing wrong about wasted renders. They take so little resources that it is simply undetectable for a human eye. In fact, comparing each component’s props to its previous props shallowly (I’m not even talking about deeply) can be more resource extensive then simply re-rendering the entire subtree.

In some frameworks you may see recommendations to avoid inline event handlers for performance reasons, particularly inside loops. That advice doesn't apply to Svelte — the compiler will always do the right thing, whichever form you choose.