Ripple-65B Preview delivers strong agentic coding results as a fully Apache 2.0 open US model
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Ricursive Intelligence is releasing Ripple-65B Preview, a 65-billion-parameter open-weight language model that the company says achieves the top score among open-source U.S. models on the TerminalBench 2.1 coding benchmark at a total R&D cost of $32 million. On other benchmarks, however, Ripple falls behind the likewise newly released western open model Inkling 2 Small. The article Ripple-65B Preview delivers strong agentic coding results as a fully Apache 2.0 open US model appeared first on The Decoder.
Ricursive Intelligence is releasing Ripple-65B Preview, a 65-billion-parameter open-weight language model that the company says achieves the top score among open-source U.S. models on the TerminalBench 2.1 coding benchmark at a total R&D cost of $32 million. On other benchmarks, however, Ripple falls behind the likewise newly released western open model Inkling 2 Small.
The article Ripple-65B Preview delivers strong agentic coding results as a fully Apache 2.0 open US model appeared first on The Decoder.
Buoyant, the company behind Linkerd, has released Kinference, an open source project for building inference platforms on Kubernetes. Kinference is an inference control plane for Kubernetes and vLLM. It sits in front of a fleet of vLLM workers and decides where each request goes. It applies routing policies, token rate limits, KV-cache-aware load balancing and per-tenant metrics in one layer. For dev teams serving many open-weight models to many internal clients, it targets the layer above the model server. The project is Apache 2.0 licensed on GitHub. The current release is v0.1.0-alpha.7. TL;DR Size: v0.1.0-alpha.7, written in Rust, Apache 2.0, configured through 5 Kubernetes CRDs. Runs on: any GPU-enabled Kubernetes cluster running vLLM. Buoyant’s benchmark used 3 L40S GPUs per pool. Performance: large gains on prefix-heavy traffic versus plain Kubernetes Service routing (self-reported). Best: median TTFT fell from 6.88 s to 0.31 s (95%) on a shared-prefix workload. Worst: no median TTFT gain on prompts without shared prefixes (6.92 s vs 6.83 s). What is Kinference? Kinference is a policy-driven control plane that turns Kubernetes plus vLLM into a multi-tenant inference platform. Buoyant’s FAQ puts it simply: vLLM is the data plane and Kinference is the control plane. How does Kinference work? The system has 2 parts: a traffic gateway and an inference handler. The GitHub README states that the handler exposes a gRPC interface. It takes a request and returns the chosen endpoint plus a possibly mutated body. It pairs with a proxy such as the Buoyant Gateway. Kinference watches vLLM KV-cache events and engine metrics. It currently uses NVIDIA’s dynamo-kv-router crate to process cache events. Configuration lives in 5 CRDs: GatewayBackend, InferencePolicy, InferencePool, InferenceProvider and InferenceWorkerSet. What are inference policies? Inference policies are declarative rules for how each request is routed or rejected. Rules can match on tenant, requested model, GPU pool state and semantic tags. Example policies on the project site cover 5 patterns: Simple to cheaper: route low-complexity prompts to a smaller model, unless cache overlap makes the large model faster. Burst to cloud: skip workers with over 2 queued prompts, then fall back to providers like Bedrock. PII stays local: remove external providers when a PII tag fires, and fail closed with onEmpty: Reject. Batch on idle GPUs: send batch requests only to workers under 70% KV-cache usage. Model aliasing: publish one stable name, such as standard-model, backed by several models. Most rules end with minimize: PredictedTTFT. Kinference predicts time-to-first-token for each candidate endpoint, using cache overlap and queue length. Policies live in git and can be validated offline with the kinfctl CLI. (function(){var f=document.getElementById("kinf-frame");window.addEventListener("message",function(e){if(e.source===f.contentWindow&&e.data&&e.data.kinfHeight){f.style.height=e.data.kinfHeight+"px";}});})(); How fast is Kinference in benchmarks? Buoyant published a benchmark against plain Kubernetes load balancing. Each arm used 3 vLLM 0.31.0 pods, each on 1 L40S GPU. The model was Qwen3.8-27B-FP8. Load came from GuideLLM 0.8.1. The baseline was a ClusterIP Service, where kube-proxy picks a random pod per connection. Unloaded latency results (median TTFT): Median end-to-end latency fell 45% on shared-prefix and 32% on multiturn. On control traffic, p95 TTFT still dropped from 21.46 s to 12.33 s. Buoyant attributes this to queue-aware balancing. The capacity test measured goodput: requests per minute meeting a TTFT SLO. Peak goodput was 103.0 vs 14.0 req/min on shared-prefix, about 7.4x. On multiturn it was 82.7 vs 23.8, about 3.5x. The Service baseline never met the SLO at any tested concurrency. These numbers are self-reported. The baseline is random routing, not another cache-aware router like llm-d or Dynamo. Buoyant also notes its SLOs (8 s and 4 s) suit offline agents, not chat. How does Kinference compare with llm-d, Dynamo and Production Stack? Kinference’s distinct angle is governance: policies, aliases, provider fallback and tenant accounting. llm-d and Dynamo lead on raw serving performance, disaggregation and engine coverage. Deployment and availability Installation takes 5 steps per the getting started guide. Teams install Gateway API v1.5.1 CRDs, the Buoyant gateway Helm chart, then the Kinference chart. Kinference ships Prometheus metrics and Grafana dashboards. Metrics include TTFT, ITL, end-to-end latency and cached versus uncached prompt tokens, mostly per tenant. Key Takeaways Kinference is an Apache 2.0 control plane for vLLM on Kubernetes, from Linkerd maker Buoyant. Declarative InferencePolicy rules handle model choice, provider bursting, PII routing and batch admission. Self-reported median TTFT fell 95% on shared-prefix traffic versus kube-proxy. Peak goodput rose about 7.4x on shared-prefix and 3.5x on multiturn. It is alpha: rate limiting, classifiers and Anthropic API support are not yet shipped. Check out the GitHub repo, docs, launch post and benchmark report. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Buoyant Releases Kinference: An Open Source Inference Control Plane for Kubernetes and vLLM appeared first on MarkTechPost.
Zyphra has released ZYFA (Zyphra FlashAttention on AMD), a set of FP8 and BF16 prefill attention kernels for AMD Instinct MI355X GPUs. ZYFA is Zyphra’s FP8 attention engine for long-context prefill, and the code is open source under Apache 2.0. On real model activations, its FP8 kernels run 1.16-1.76x faster than AITER, AMD’s default kernel library. At head dimension 128, ZYFA’s FP8 error is 62-78% lower than AITER’s. TL;DR Framework: It is a kernel library. Causal forward (prefill) attention in FP8 e4m3 and BF16, head dim 128 and MLA shape (192/128). Runs on: AMD Instinct MI355X only (CDNA4, gfx950), ROCm 7.2.3, PyTorch 2.12. Drop-in flash_attn_func interface. Performance: 1.16-1.76x AITER’s FP8 throughput on real activations; BF16 roughly ties (1.01-1.02x). Best: 2,672 TFLOP/s FP8 at 32K tokens, 53% of the FP8 datasheet peak (AITER: 39%). Worst: Only 1.16x on Mistral-7B activations; FP8 clock drops to 1,628 MHz vs AITER’s 2,108 MHz. What is ZYFA? ZYFA is shipped in the form of a technical paper along with code from Zyphra. It powers the attention engine of the Zyphra Optimization Engine (ZOE), which runs Zyphra Cloud. The BF16 kernels are written in HIP. The FP8 kernels are hand-scheduled assembly emitted by a Python generator. Why does FP8 attention underperform on MI355X? FP8 matrix instructions run at 2x the BF16 peak. But FP8 only speeds up the 2 matrix products, QKᵀ and PV. The softmax between them runs on the vector unit at the same speed in both precisions. For 32 queries against 128 keys, FP8 matrix work takes 1,024 cycles. The exact FP8 softmax takes 896 vector cycles. So the kernel needs near-perfect overlap to keep matrix units busy. Zyphra’s thread traces show AITER’s matrix pipe idle for 31.7% of cycles. The MI355X does not track matrix instruction completion in hardware, so instruction order must create that overlap. How does ZYFA work? ZYFA uses 3 techniques: Lean P: one multiply-add instead of an exponential An e4m3 bit pattern, read as an integer, grows by 8 each time the value doubles. So the code of 2^x is roughly 8x plus a constant. Lean P writes each FP8 probability with 1 multiply-add and 1 integer conversion. This cuts softmax vector cycles per block from 896 to 384. Two-wave ping-pong schedule 2 waves share each SIMD. One issues matrix instructions while the other computes probabilities, then they swap. With lean P, the matrix pipe is busy 86% of steady-state cycles, up from 60% with exact P. Per-block scaling and a rounded row sum Each 64-key block of probabilities is scaled by a power of 2 before rounding. CDNA4’s scaled matrix instruction removes the factor at no cost. ZYFA also divides by the sum of the rounded probabilities, computed with 1 extra matrix instruction per block. window.addEventListener("message",function(e){if(e.data&&e.data.zyfaHeight){var f=document.getElementById("zyfa-mtp-frame");if(f&&e.source===f.contentWindow){f.style.height=e.data.zyfaHeight+"px";}}}); How fast is ZYFA? Real-activation FP8 speedups at 16K and sustained clocks are 1.16x (Mistral-7B), 1.17x (Qwen3-8B) and 1.76x (DeepSeek-V2-Lite). On Gaussian benchmarks at 32K, the head-dim-128 FP8 kernel reaches 2,672 TFLOP/s against AITER’s 1,944. At the MLA shape it reaches 2,464 against 1,237, a 1.99x ratio. On captured Mistral-7B and Qwen3-8B layers, ZYFA delivers 1.94 and 1.91 TFLOP/J. AITER delivers 1.60 and 1.56. Why are real-model gains smaller than benchmark gains? The MI355X clock depends on how many operand bits flip. AITER lets more small probabilities become zeros or subnormals. Those switch fewer bits, so its clock runs higher. On the Mistral-7B layer, ZYFA runs at 1,628 MHz while AITER runs at 2,108 MHz. ZYFA still needs fewer cycles overall. Zyphra team recommends benchmarking on real activations at sustained clocks. How accurate is ZYFA’s FP8 attention? Against fp64 attention, ZYFA’s FP8 error is 0.22x AITER’s on Mistral-7B and 0.38x on Qwen3-8B. It sits within a few percent of Composable Kernel and Triton. In full Mistral-7B, KL divergence from fp64 is 3.4×10⁻³ with ZYFA and 5.9×10⁻³ with AITER. ZYFA’s outputs were also bitwise identical across 7,200 launches. Most full-model FP8 error comes from quantizing Q, K and V. A Hadamard rotation of Q and K roughly halves that input error. On Mistral-7B it adds about 0.1% to attention time. How does ZYFA compare with other attention kernels? Cross-vendor numbers come from different inputs and clocks. Zyphra did not measure NVIDIA GPUs. Key Takeaways FP8 attention on MI355X is softmax-bound, not matrix-bound. Lean P swaps the exponential for 1 multiply-add. Real-model gains are 1.16-1.76x, smaller than benchmarks show. FP8 error drops 62-78% vs AITER at head dim 128. Prefill only: no decode, no paged KV, MI355X only. Check out the paper, the Zyphra blog post and the GitHub repo. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Zyphra ZYFA: FP8 FlashAttention for AMD MI355X, Up to 1.76x Faster appeared first on MarkTechPost.
Perplexity is launching "Teammates," always-on agents that work through ongoing projects on their own virtual machines. The feature requires the Max plan at $200 per month, well above similar offerings from OpenAI or Anthropic. The market for always-on agents is getting crowded. The article Perplexity now has its own always-on AI agent appeared first on The Decoder.
