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nd instead of serving every use case out of the box it is flexible and open enough to be fully customizable in fact jaspr_content is what currently powers the new flutter dev and dart dev documentation which contain over 3 900 pages tooling and developer experience in my opinion a framework is only as good as its tooling and this is where dart truly shines and has provided jaspr developers with a great developer experience for example flutter is known for its stateful hot reload enabling you to swap out code instantly without losing client side state but hot reload is actually a dart feature enabled by its unique compiler architecture for browser development the dartdevc compiler performs modular and incremental compilation to javascript it supports stateful hot reload and provides a seamless debugging experience by cleverly leveraging source maps you can step through native dart code right in the browser complete with breakpoints value inspection and runtime expression evaluation debugging jaspr dart code using chrome devtools for production builds dart uses the dart2js compiler to generate a heavily optimized tree shaken javascript bundle or the newer dart2wasm compiler for even better runtime performance through webassembly on the server side dart s jit compiler provides that same hot reload and debugging capabilities while its aot compiler compiles your server code to optimized platform specific native binaries for production environments jaspr builds on top of these and other capabilities for example by giving developers full stack debugging custom lints and code assists and something i call component scopes this is a neat editor feature that adds inline hints to your components showing whether they are rendered on the server the client or both when building full stack apps this makes it much easier to reason about which platform apis or libraries you can safely use in a specific file i m also working on more features to make the full stack development aspect even smoother for example a full stack hot reload where on any server side change whether updating code or for example editing a markdown file the new pre rendered html is hot reloaded into the page while keeping all client side state features like these are only possible due to jaspr s approach to combine both server and client side rendering into one framework impact and outlook last year google selected jaspr for the dart and flutter websites including dart dev flutter dev and docs flutter dev repo which is used by over a million monthly active users the sites were migrated from js and python based static site generators to jaspr and jaspr_content resulting in a unified setup with less context switching and an easier contribution experience the move to jaspr also streamlined the development of brand new interactive tutorials on dart dev learn and docs flutter dev learn for me this is not only an incredible trust in the capabilities of jaspr but also a great way to dogfood jaspr at scale it allowed me to invest more time and resources into improving jaspr with ai constantly shifting the scope of software development i believe the concept of being a strict domain expert a purely mobile or purely web developer will matter less however developers and teams will increasingly value coherent tech stacks to reduce context switching and leverage unified tooling just as react native became massively popular because it allowed web developers to reuse their skills for mobile or for companies to reuse their developers jaspr is a great option for teams working with both flutter and the web apart from using existing skills jaspr and flutter projects can also share up to 100 of their business logic models and validation code dart s type safety and high quality tooling position it well for modern web development jaspr evolved to be the missing piece a cohesive framework with modern features and a great development experience i personally see jaspr as an antithesis to the trend of ai causing everyone to converge onto the same stack especially in web development while this also has some benefits i believe there is immense value in exploring alternative ecosystems this can push boundaries surface new ideas and keep our industry vibrant if there s one takeaway from my journey it s this don t be afraid to build the tools you want to use you never know where that codebase will take you and it can be incredibly rewarding if you re a dart or flutter developer curious about building websites with the skills you already have there s never been a better time to start try out jaspr now on its online playground which is also built with jaspr or by following the jaspr quickstart learn more about flutter s migration in we rebuilt flutter s websites with dart and jaspr oh and if you re wondering where the name jaspr came from it s named after my dog jasper if you ever find yourself wandering around jaspr site and want to meet jasper keep an eye out you just might find a little easter egg tribute to him leveraging cpu memory for faster cost efficient tpu llm training friday april 10 2026 by keyur ruganathbhai ranipa qinglan xiang vrushabh sanghavi ramesh ag weilin wang intel and penporn koanantakool google host offloading with jax on intel xeon processors as large language models llms continue to scale into the hundreds of billions of parameters device memory capacity has become a big limiting factor in training as intermediate activations from every layer in the forward pass are needed in the backward pass to reduce device memory pressure these activations can be rematerialized during the backward pass trading memory for recomputation while rematerialization enables larger models to fit within limited device memory it significantly increases training time and cost intel xeon processors 5th and 6th gen with advanced matrix extensions amx enable practical host offloading of selected memory and compute intensive components in jax training workflows this approach can help teams train larger models relieve accelerator memory pressure improve end to end throughput and reduce total cost of ownership particularly on tpu based google cloud instances by publishing these results and implementation details google and intel aim to promote transparency and share practical guidance with the community this post describes how to enable activation offloading for jax on tpu platforms and outlines considerations for building scalable cost aware hybrid cpu accelerator training workflows figure 1 google cloud tpu pod commonly used in llm training host offloading traditional llm training is usually done on device accelerators alone however modern host machines have much larger memory size than accelerators 512gb or more and can offer extra compute power e g tflops in case of intel xeon scalable processor with amx capability leveraging host resources can be a great alternative to rematerialization host offloading selectively moves computation or data between host and device to optimize performance and memory usage host memory offloading keeps frequently accessed tensors on the device and spills the rest to cpu memory as an extra level of cache activation offloading transfers activations computed on device in the forward pass to the host stores them in the host memory and brings them back to the device in the backward pass for gradient computation this unlocks the ability to train larger models use bigger batch sizes and improve throughput figure 2 memory offloading during forward and backward pass in this blog post we provide a practical guide to offload activations through jax to efficiently train larger models on tpus with an intel xeon scalable processor enabling memory offloading in jax jax offers multiple strategies for offloading activations model parameters and optimizer states to the host users can use checkpoint_names to create a checkpoint for a tensor the snippet below shows how to create a checkpoint x from jax ad_checkpoint import checkpoint_name def layer_name x w w1 w2 w x checkpoint_name x x y x w1 return y w2 none users can provide checkpoint_policies to select the appropriate memory optimization strategy for intermediate values there are three strategies recomputing during backward pass default behavior storing on device offloading to host memory after forward pass and loading back during backward pass the code below moves x from device to the pinned host memory after the forward pass from jax import checkpoint_policies as cp policy cp save_and_offload_only_these_names names_which_can_be_saved no values stored on device names_which_can_be_offloaded x offload activations labeled x offload_src device move from device memory offload_dst pinned_host to pinned host memory measuring host offloading benefits on tpu v5p we examined tpu host offloading on jax on both fine tuning and training workloads all our experiments were run on google cloud platform using a single v5p 8 tpu instance with single host 4th gen intel xeon scalable processor fine tuning paligemma2 using the base paligemma2 28b model for vision language tasks we fine tuned the attention layers of the language model gemma2 27b while keeping all other parameters frozen during fine tuning we set the llm sequence length to 256 and the batch size to 256 the default checkpoint policy is nothing_saveable which does not keep any activations on device during the forward pass the activations are rematerialized during the backward pass for gradient computation while this approach reduces memory pressure on the tpu it increases compute time to apply host offloading we offload q k and v projection weights using save_and_offload_only_these_names these activations are transferred to host memory d2h during the forward pass and fetched back during the backward pass h2d so the device neither stores nor recomputes them figure 2 shows 10 reduction in training time from host offloading this translates directly into a similar reduction in tpu core hours yielding meaningful cost savings the complete fine tuning recipe is available at jax host offloading figure 3 top training time comparison between full rematerialization and host offloading bottom memory analysis with and without host offloading training llama2 13b using maxtext maxtext offers several rematerialization strategies that can be specified in the training configuration file we used the policy remat_policy qkv_proj_offloaded to offload q k and v projection weights figure 3 shows 5 reduction in per step training time compared to fully rematerializing all activations remat_policy full figure 4 maxtext llama2 13b training statistics with and without host offloading the step time was 5 faster with host offloading when to offload activations activation offloading is beneficial when the time to transfer activations across host and device is lower than the time to recompute them the timing depends on multiple factors such as pcie bandwidth model size batch size sequence length activation tensor sizes compute capabilities of the device etc an additional factor is how much the data movement can be overlapped with computation to keep the device busy figure 4 demonstrates an efficient overlap of the device to host transfer with compute during the backward pass in paligemma2 28b training figure 5 a jax trace of paligemma2 training viewed on perfetto memory offloading overlaps with compute effectively during backward pass host to device smaller model variants such as paligemma2 3b and 9b did not see benefits from host offloading because it is faster to rematerialize all tensors than to transfer them to and from the host therefore identifying the appropriate workload and offloading policy is crucial to realizing performance gain from host offloading call to action if you train on tpus and are limited by device memory consider evaluating activation offloading start by labeling candidate activations for example q k v projections and compare step time memory headroom and overall cost across representative workloads in our experiments we observed up to 10 improvement in end to end training time for larger workloads which can reduce total cost of ownership tco by shortening time to train or enabling the same workload on smaller instances acknowledgments emilio cota and karlo basioli from google and eugene zhulenev formerly at google celebrate a2april thursday april 9 2026 by patricia cruz daryl ducharme google open source happy 1st birthday to a2a join the community in celebrating the first anniversary of the a2a and its recent 1 0 release april 9th marks the official birthday and we re celebrating all month long with a2april to help you celebrate we ve used gemini to make a party hat use the template and instructions below to create your commemorative party hat assembly instructions print print this document on heavy cardstock for the best results cut carefully cut along the solid outer border of the semi circle template fold gently curve the template into a cone shape overlapping the glue tape tab underneath the opposite edge secure use double sided tape or a glue stick along the tab to hold the cone shape finish punch two small holes on opposite sides of the base and thread through an elastic string or ribbon to secure the hat to your head party hat visualization make sure to print in landscape mode ways to celebrate social media share a photo of yourself wearing your hat with the tag a2april to help generate that social media buzz blog series keep an eye out for the upcoming a2april blog series featuring quotes from the team and stories from the open source community community quotes if you re using a2a in production reach out to us via social media and share your story for the birthday post search this blog popular posts open source open doors apply now for google summer of code gemma 4 expanding the gemmaverse with apache 2 0 google cloud investing in the future of postgresql shape the future with google summer of code 2026 introducing the 185 organizations for gsoc 2026 archive 2026 22 april 7 meet the a2family a year of open collaboration celebrating the anni jaspr why web development in dart might just be a leveraging cpu memory for faster cost efficient t celebrate a2april kubernetes goes ai first unpacking the new ai con gemma 4 expanding the gemmaverse with apache 2 0 march 5 february 5 january 5 2025 47 december 6 november 5 october 3 september 5 august 5 july 5 june 3 may 5 april 1 march 1 february 3 january 5 2024 39 december 4 november 1 october 1 september 3 august 4 july 4 june 4 may 5 april 4 march 2 february 6 january 1 2023 44 december 5 november 6 october 2 september 3 august 1 july 2 june 5 may 5 april 2 march 6 february 3 january 4 2022 44 december 4 november 2 october 7 september 6 august 2 july 3 june 5 may 1 april 2 march 4 february 5 january 3 2021 55 december 3 november 7 october 4 september 7 august 5 june 2 may 2 april 6 march 6 february 8 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