Redis launched LangCache, a managed semantic caching service that matches prompts by meaning and returns stored LLM responses without making a new model call on a cache hit. LangCache reports up to 90% lower LLM API costs and cache-hit responses up to 15x faster, with a demo showing 0.37 seconds vs 2.232 seconds for a paraphrased query. Applications can replace per-request LLM calls with a REST two-step loop (search before calling, store after misses), changing latency and spending by shifting cost mainly to avoided output tokens while requiring tuned thresholds and TTL/monitoring to prevent incorrect matches.
KVector, a University of Birmingham spinout, raised funding to commercialise super-efficient antenna technology aimed at steering signals without thousands of individually controlled components. The company secured £3.8m, alongside an Innovate UK grant of £950,000, to support recruitment and development of radar and communications products with defence-sector partners. The raise speeds up product development and expands KVector’s team while it continues operating in stealth around specific partners and applications.
Together’s kernels team gained access to the NVIDIA Vera Rubin NVL72 platform and updated ThunderKittens to run NVFP4 and FP8 GEMMs on it by adding Rubin-specific ISA and memory/pipeline features. The NVFP4 GEMM kernels improve from about 42.1% of the roofline to over 22 PFLOPS. These changes increase attainable performance by doubling the K step, expanding tensor memory and shared memory use, and adjusting tiling and commit/collector behaviors so tensor cores stay fed.
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