# llm-d 1.0 Advances Open, Cost-Efficient AI Inference for Production Scale

- Link: https://www.thailand-business-news.com/pr-news/llm-d-1-0-advances-open-cost-efficient-ai-inference-for-production-scale
- Published: 2026-10-20T19:00:00+07:00
- Author: PR Newswire

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 ◦ _llm-d 1.0 consolidates a hardened core for distributed inference validated for
   day 2 production deployments that helps organizations improve AI serving efficiency
   and scale enterprise workloads._
 ◦ _llm-d is broadly adopted and trusted by frontier model labs, hyperscalers and
   neoclouds, hardware accelerators, and major enterprises, with recent demonstrations
   showing up to five to 10 times cost reductions for context-heavy agentic workloads
   ^(1)_ _and three times greater output throughput.^(2)_

SAN JOSE, Calif., Oct. 20, 2026 /PRNewswire/ — Today, llm-d project contributors
announced the release of llm-d 1.0, a major milestone for the open source project
designed to help organizations deploy and scale large language model inference more
efficiently across modern infrastructure. Founded [in 2025](https://llm-d.ai/blog/llm-d-press-release)
by Red Hat, IBM (NYSE: [IBM](http://www.ibm.com/investor)), Google Cloud, CoreWeave
and NVIDIA, and now governed by the Cloud Native Computing Foundation (CNCF), llm-
d plays a key role in the open foundation for enterprises deploying AI at scale 
in production.

As organizations race to put AI into production, many are discovering that running
AI at scale can be as challenging and expensive as building the models themselves.
Organizations need a way to deliver responsive experiences for users, support agentic
workloads, and maximize utilization of increasingly scarce compute resources. llm-
d 1.0 is built to address these challenges through intelligent distributed inference
capabilities to facilitate improvements in performance, increases in throughput 
and help organizations gain greater control over the economics of AI serving.

**Turning Innovation into Production Reality**

With the rise of agentic AI, new types of workloads are placing a different kind
of stress on infrastructure systems. Agentic workloads continually access and reuse
vast amounts of context while supporting parallel agents that can generate unpredictable
bursts of activity. Managing that context often consumes more resources than generating
the final output itself, making latency, throughput, memory efficiency and infrastructure
costs critical factors in realizing AI ROI. llm-d already delivers hardware heterogeneity
and advanced inference capabilities out of the box for complex infrastructure environments,
as well as prefix-cache and load-aware routing, KV cache offloading, prefill/decode
disaggregation, wide expert parallelism and batch and async inference — across multiple
model servers, including vLLM and SGLang.

llm-d 1.0 is engineered to bring that performance to the realities of Day 2 operations
and the ongoing work of running inference in production: serving multiple tenants,
balancing competing priorities, providing with zero downtime features and supporting
emerging agentic workloads, all within one suite of co-designed components that 
compose into a platform. New capabilities in 1.0 include:

 ◦ **Multi-tenant quality of service** — Priority and fairness-aware flow control
   helps latency-sensitive requests, and bursty agent workloads share a fleet without
   starving one another.
 ◦ **Zero-downtime operations** — Router high availability with hot standby and 
   rolling updates keep traffic flowing through upgrades and planned maintenance.
   Management of prefill/decode replicas as one logical unit, with coordinated rollouts
   and lockstep upgrades—making disaggregated serving practical in production.
 ◦ **A stable foundation to build on** — A v1 configuration API and a hardened request
   path, validated across clouds and accelerators, give adopters a release they 
   can standardize on.
 ◦ **Agent-aware serving** — Session-aware routing keeps multi-turn conversations
   close to their cached context, while turn-priority scheduling and native Responses
   API state support help agents reuse context across turns and reduce recomputation.

The result is infrastructure that can support diverse AI workloads, ranging from
latency-sensitive user interactions to large-scale autonomous agent workflows, without
forcing organizations to choose between responsiveness and efficiency.

**Growing Ecosystem Momentum**

llm-d is already being used across a growing ecosystem of model developers, cloud
providers, infrastructure vendors and enterprise users. Today, the project includes
more than 500 contributors from over 25 [organizations](https://github.com/llm-d/llm-d/blob/main/ADOPTERS.md)
collaborating on the future of distributed AI serving and helping build common foundations
across the AI infrastructure stack.

Core users including organizations running large-scale distributed inference, frontier
model developers optimizing inference stacks and cloud providers serving multi-tenant
AI endpoints have all seen measurable benefit from deploying llm-d.

In one [recent use case](https://www.ibm.com/downloads/documents/us-en/17f55bd44d44b0e3),
open frontier models and llm-d enabled IBM Research to explore quantum error correction
hardware at 11.5 times lower inference cost than an all-proprietary configuration.
^(3) Researchers reported a prefix-cache hit rate of approximately 98% and a blended
serving cost of about $0.11 per million tokens.

In a separate recent [demonstration](https://research.ibm.com/blog/running-open-models-on-h100-gpus-with-llmd)
using benchmark workloads, researchers showed that llm-d could efficiently serve
large mixture-of-experts models for agentic workloads. Researchers used llm-d to
deploy GLM 5.2, an approximately 753-billion-parameter open-weight model, on  widely
deployed NVIDIA GPUs, demonstrating how enterprises can use existing GPU fleets 
to serve frontier-class AI at scale. On a workload with hundreds of agentic sessions
running in parallel, the deployment delivered over 6.6 million output tokens per
minute at peak on agentic benchmarks, serving up to 3,000 concurrent coding agents
with zero preemptions. At current cloud rental rates, this deployment led to costs
that would have been 5 to 10 times less per token than equivalent commercial API
pricing, with the largest savings on the input-heavy traffic patterns typical of
agentic workloads, highlighting how llm-d can save resources and costs for enterprises
deploying AI in production at scale.^(4)

Through its CNCF governance model and broad industry participation, llm-d aims to
accelerate innovation while providing organizations with an open alternative for
managing increasingly complex AI infrastructure. Looking to the future, the community
will continue adapting to new hardware and models as they emerge, evolving the software
stack to achieve peak performance across new architectures. To learn more, read 
the announcement blogs from IBM and Red Hat. 

**Supporting Quotes**

**_Chris Aniszczyk, CTO, CNCF_**

"AI inference is quickly becoming a core cloud native workload. llm-d 1.0 helps 
organizations meet this shift at scale by bringing together open source inference
capabilities with the requirements that matter in scaling production to then meet
the demands of agentic workloads. It’s an important step toward making high performance
open source inference more operationally mature and accessible across the cloud 
native ecosystem."

**_Brian Stevens, senior vice president and AI chief technology officer, Red Hat_**

"As organizations move beyond AI experimentation and into full-scale production,
the bottleneck shifts from generating answers to managing the economics, operational
reliability, and extreme infrastructure demands of complex agentic workloads. Open
source collaboration is essential to solving these day-two operational challenges
at scale. With llm-d 1.0, the community is delivering a standardized, hardened core
for distributed inference to empower enterprises to optimize hardware performance,
address operational costs, and build high-density AI architectures on an open foundation."

_**Priya Nagpurkar, vice president, AI native systems, IBM Research**_

"At IBM, we see firsthand that the challenge of enterprise AI is no longer just 
building powerful models but running them efficiently and reliably at scale. llm-
d 1.0 represents an important milestone in creating an open foundation for distributed
inference to help organizations get more value from their AI infrastructure and 
more control on the AI capabilities they depend on. As agentic workloads become 
more prevalent, open technologies like llm-d will be critical to improving performance,
lowering costs, and accelerating innovation across the AI ecosystem."

**_Mark Lohmeyer, VP & GM, AI & Computing Infrastructure, Google Cloud_**

"Agentic AI is unlocking tremendous value for enterprises and consumers across a
broad range of use-cases. However, as these workloads scale rapidly in production,
they place unique demands on the infrastructure and require new approaches to deliver
the performance, reliability, and cost-effectiveness our customers need. The release
of llm-d 1.0 is an important milestone for the industry to address these needs. 
As a founding contributor to llm-d, Google Cloud is integrating llm-d 1.0 into Google
Kubernetes Engine, and enabling it across our Google TPU and NVIDIA GPU portfolio,
enabling customers to scale multi-tenant environments, optimize their inference 
performance, and dramatically lower their overall serving costs on a hardened, open-
source foundation."

**_Shadi Saba, vice president_****_ of engineering, AI/ML at CoreWeave_**

"Every team running inference at scale hits the same problems: context that often
costs more to manage than the output, tenants competing for the same fleet, and 
upgrades that can’t interrupt traffic. llm-d 1.0 gives the community a hardened,
open base to solve them once instead of separately. CoreWeave helped found the project,
and customers can run llm-d on CoreWeave Kubernetes Service alongside NVIDIA Dynamo,
choosing the serving stack that fits their workload."

**_Ujval Kapasi, vice president, Engineering AI Frameworks, NVIDIA_**

"The next wave of agentic AI demands inference that delivers performance and efficiency
at scale. Bringing llm-d to NVIDIA Vera Rubin will help enterprises build and scale
advanced AI applications on the next generation of NVIDIA accelerated infrastructure.
Through our collaboration with the llm-d community and contributions from open source
NVIDIA Dynamo, we’re helping enterprises get more from their AI infrastructure investments."

**_Phil Guido, executive vice president and chief commercial officer at AMD_**

"With llm-d v1.0, we are advancing open, high-performance distributed inference 
on AMD Instinct™ GPUs. Agentic workloads require infrastructure that delivers responsiveness
and efficiency. Working with the llm-d community, we’re delivering the latest inference
innovation on an open foundation, giving developers greater flexibility and choice
to deploy AI at scale."

**_Acyr Locatelli, VP, Modeling, Cohere_**

"At Cohere, we face the dual challenge of training frontier language models while
serving them efficiently at enterprise scale. llm-d helps us optimize for both, 
whether maximizing throughput for large-scale reinforcement learning workloads or
delivering the responsiveness users expect in production. Its flexible, open-source
architecture aligns closely with our commitment to sovereign AI, giving organizations
greater control over how AI is deployed, operated, and scaled." 

**_Salman Paracha, SVP AI Engineering, DigitalOcean_**

"DigitalOcean is excited about the llm-d 1.0 release and relies on llm-d as foundational
distributed inference software for DigitalOcean Serverless Inference and Managed
Agents, serving trillions of tokens. Its Kubernetes-native design and integration
with the Kubernetes Gateway API Inference Extensions provide robust orchestration
and endpoint picking, while production-proven paths for dynamic flow control, inference-
aware load balancing, and prefill/decode disaggregation help us operate reliably
at scale. We collaborate closely with the llm-d community, contributing upstream
features and feedback, and look forward to the continued development of llm-d."

**_Vladislav Tankov, Director of AI, JetBrains_**

"JetBrains has been working with llm-d since its early releases, and it is great
to see the project reach 1.0. llm-d now runs in production behind our new Junie 
Lite plan and serves a significant share of its traffic. Our traffic ranges from
long agentic coding sessions to short requests, and predicted latency-based scheduling
was straightforward to enable and keeps latency consistent across that mix. Junie
Lite brings our AI coding agent to developers at no cost, and llm-d helps make that
possible by letting us serve more requests per GPU at lower cost and with faster
responses. We have enjoyed working with the llm-d team and congratulate them on 
this release."

**_Mathis Felardos, Lucas Fernandes-Martins and Nikhil Thomas (Mistral, Inference
Engineers and llm-d contributors)_**

"Serving frontier models across interactive applications and high-volume APIs means
handling steep traffic spikes without letting bursty workloads degrade latency for
priority customers. With llm-d’s prefix-cache-aware routing, flow control and disaggregated
serving support we can enforce strict priority bands across our commercial tiers,
automatically holding or shedding lower-priority requests when pools saturate so
paid traffic meets our latency SLOs. Mistral is a proud supporter of open-source
software, and we will continue to contribute back to llm-d and DisaggregatedSet 
projects."

**_Nishchaie Khanna, Sr Director of Engineering, AI Platform, Roblox_**

"At Roblox, serving large language models efficiently across a mix of cloud and 
on-premises GPUs is one of our core infrastructure challenges. llm-d gives us a 
Kubernetes-native foundation for disaggregated serving and cache-aware routing on
the hardware we already run, without tying us to a single inference engine. We’re
excited to build on llm-d 1.0 and to contribute back to the community."

**About IBM**

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise.
We help clients in more than 175 countries capitalize on insights from their data,
streamline business processes, reduce costs and gain the competitive edge in their
industries. Thousands of governments and corporate entities in critical infrastructure
areas such as financial services, telecommunications and healthcare rely on IBM’s
hybrid cloud platform and Red Hat OpenShift to affect their digital transformations
quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum
computing, industry-specific cloud solutions and consulting deliver open and flexible
options to our clients. All of this is backed by IBM’s long-standing commitment 
to trust, transparency, responsibility, inclusivity and service. Visit [www.ibm.com](http://www.ibm.com/)
for more information.

**About Red Hat**

[Red Hat](https://www.redhat.com/en/home-page) is the open hybrid cloud technology
leader, delivering a trusted, consistent and comprehensive foundation for transformative
IT innovation and AI applications. Its portfolio of cloud, developer, AI, Linux,
automation and application platform technologies enables any application, anywhere—
from the datacenter to the edge. As the world’s leading provider of enterprise open
source software solutions, Red Hat invests in open ecosystems and communities to
solve tomorrow’s IT challenges. Collaborating with partners and customers, Red Hat
helps them build, connect, automate, secure and manage their IT environments, supported
by consulting services and [award-winning](https://www.redhat.com/en/home-page) 
training and certification offerings.

**Media contacts:

Ashley Peterson
IBM Communications[ashley.peterson@ibm.com](https://www.thailand-business-news.com/pr-news/ashley.peterson@ibm.com)

Marian Pierson
Red Hat Communications[mpierson@redhat.com](https://www.thailand-business-news.com/pr-news/mpierson@redhat.com)

^(1) [How llm-d makes the most of the hardware you already have](https://research.ibm.com/blog/running-open-models-on-h100-gpus-with-llmd)

^(2) [Production-Grade LLM Inference at Scale with KServe, llm-d, and vLLM](https://llm-d.ai/blog/production-grade-llm-inference-at-scale-kserve-llm-d-vllm)
^(3) [Highly Cost-Efficient Agent Factories for High-Level Synthesis: Design Space Exploration for Quantum Error Correction Chip with Open Frontier Models and Distributed Inference at Scale](https://www.ibm.com/downloads/documents/us-en/17f55bd44d44b0e3)
^(4 )[How llm-d makes the most of the hardware you already have ](https://research.ibm.com/blog/running-open-models-on-h100-gpus-with-llmd)

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