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i14 migrates its custom architecture to AWS Trainium2
i14 migrated its custom architecture to AWS Trainium2. According to information released by AWS Machine Learning on October 21, 2026, i14 collaborated with AWS to train and deploy the Gated Linear Attention-based Laplace 3B model on AWS Trainium2 using TorchNeuron Native. Training throughput on a single trn2.48xlarge is approximately 120,000 tokens/s; the model's recurrent state is fixed at 29.5 MB per sequence and does not grow with context length; inference reaches 11,544 output tokens/s at 1,280 concurrent sequences. The team also reworked Muon's all_to_all communication, reducing per-card communication volume by 24x and increasing training throughput by 1.63x. There are currently no subsequent developments.
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View story:i14 migrates its custom architecture to AWS Trainium2
AWS presents: Securing AI agent identities with Keyfactor EJBCA and AWS services
On October 21, 2026, AWS presented a solution for securing AI agent identities using Keyfactor EJBCA combined with AWS services. According to an article published by AWS Machine Learning, AWS and Keyfactor jointly released a reference architecture that uses Keyfactor EJBCA's X.509 PKI to establish verifiable, revocable cryptographic identities for AI agents, filling the gap left by MCP's lack of a built-in authentication standard. The solution combines Amazon Bedrock, the AWS Strands Agents SDK, AWS Secrets Manager, and Amazon RDS, and covers three trust boundaries—user authentication, agent-to-agent communication, and MCP tool invocation—through four layers of mechanisms: signed request envelopes, user-signed delegation assertions, RFC 8693 audience-restricted tokens, and real-time OCSP revocation checks. Currently, the solution is presented publicly only as a reference architecture, and there are no public facts regarding subsequent implementation progress.
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View story:AWS presents using Keyfactor EJBCA and AWS services to secure AI agent identities
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i14 migrates its custom architecture to AWS Trainium2
i14 migrated its custom architecture to AWS Trainium2. According to information released by AWS Machine Learning on October 21, 2026, i14 collaborated with AWS to train and deploy the Gated Linear Attention-based Laplace 3B model on AWS Trainium2 using TorchNeuron Native. Training throughput on a single trn2.48xlarge is approximately 120,000 tokens/s; the model's recurrent state is fixed at 29.5 MB per sequence and does not grow with context length; inference reaches 11,544 output tokens/s at 1,280 concurrent sequences. The team also reworked Muon's all_to_all communication, reducing per-card communication volume by 24x and increasing training throughput by 1.63x. There are currently no subsequent developments.
i14 migrated its custom architecture to AWS Trainium2. According to information released by AWS Machine Learning on October 21, 2026, i14 collaborated with AWS to train and deploy the Gated Linear Attention-based Laplace 3B model on AWS Trainium2 using TorchNeuron Native. Training throughput on a single trn2.48xlarge is approximately 120,000 tokens/s; the model's recurrent state is fixed at 29.5 MB per sequence and does not grow with context length; inference reaches 11,544 output tokens/s at 1,
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AWS presents using Keyfactor EJBCA and AWS services to secure AI agent identities
On October 21, 2026, AWS presented a solution for securing AI agent identities using Keyfactor EJBCA combined with AWS services. According to an article published by AWS Machine Learning, AWS and Keyfactor jointly released a reference architecture that uses Keyfactor EJBCA's X.509 PKI to establish verifiable, revocable cryptographic identities for AI agents, filling the gap left by MCP's lack of a built-in authentication standard. The solution combines Amazon Bedrock, the AWS Strands Agents SDK, AWS Secrets Manager, and Amazon RDS, and covers three trust boundaries—user authentication, agent-to-agent communication, and MCP tool invocation—through four layers of mechanisms: signed request envelopes, user-signed delegation assertions, RFC 8693 audience-restricted tokens, and real-time OCSP revocation checks. Currently, the solution is presented publicly only as a reference architecture, and there are no public facts regarding subsequent implementation progress.
On October 21, 2026, AWS presented a solution for securing AI agent identities using Keyfactor EJBCA combined with AWS services. According to an article published by AWS Machine Learning, AWS and Keyfactor jointly released a reference architecture that uses Keyfactor EJBCA's X.509 PKI to establish verifiable, revocable cryptographic identities for AI agents, filling the gap left by MCP's lack of a built-in authentication standard. The solution combines Amazon Bedrock, the AWS Strands Agents SDK,
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AWS introduces vLLM bidirectional KV transfer to reduce multi-turn TTFT
On October 20, 2026, AWS introduced a new bidirectional KV cache transfer mechanism in vLLM: in subsequent turns, the decode node sends the already-computed KV cache back to the prefill node, avoiding recomputation of the conversation history. On a 1P1D deployment on Amazon EC2 P5en, this mechanism improves TTFT for Qwen3-32B by up to 1.6x and for zai-org/GLM-5-FP8 by up to 3.3x. The capability was initially validated on AWS Trainium clusters and contributed to vLLM as an accelerator-agnostic feature that requires coordination with a KV-cache-aware router for cross-turn reuse. There are currently no further developments.
On October 20, 2026, AWS introduced a new bidirectional KV cache transfer mechanism in vLLM: in subsequent turns, the decode node sends the already-computed KV cache back to the prefill node, avoiding recomputation of the conversation history. On a 1P1D deployment on Amazon EC2 P5en, this mechanism improves TTFT for Qwen3-32B by up to 1.6x and for zai-org/GLM-5-FP8 by up to 3.3x. The capability was initially validated on AWS Trainium clusters and contributed to vLLM as an accelerator-agnostic fe
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Deepgram and AWS port Nova-3 to Trainium2
Deepgram and AWS collaborated to port the Nova-3 speech-to-text model to AWS Trainium2. According to information published by AWS Machine Learning on October 20, 2026, a single trn2.3xlarge instance can run 8 model replicas, processing 11%–70% more audio per dollar in offline batch processing scenarios than the previous solution. The team used TorchNeuron to complete the PyTorch port and tuned it through NKILib fused kernels and hand-written NKI kernels, among which the FFN kernel latency was reduced by 2.0x, depthwise conv1d was sped up by 1.5x, and the conv2d kernel unlocked batch sizes above 32. There are currently no subsequent development facts.
Deepgram and AWS collaborated to port the Nova-3 speech-to-text model to AWS Trainium2. According to information published by AWS Machine Learning on October 20, 2026, a single trn2.3xlarge instance can run 8 model replicas, processing 11%–70% more audio per dollar in offline batch processing scenarios than the previous solution. The team used TorchNeuron to complete the PyTorch port and tuned it through NKILib fused kernels and hand-written NKI kernels, among which the FFN kernel latency was re
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AWS introduces vLLM bidirectional KV transfer to reduce multi-turn TTFT
On October 20, 2026, AWS introduced a new bidirectional KV cache transfer mechanism in vLLM: in subsequent turns, the decode node sends the already-computed KV cache back to the prefill node, avoiding recomputation of the conversation history. On a 1P1D deployment on Amazon EC2 P5en, this mechanism improves TTFT for Qwen3-32B by up to 1.6x and for zai-org/GLM-5-FP8 by up to 3.3x. The capability was initially validated on AWS Trainium clusters and contributed to vLLM as an accelerator-agnostic feature that requires coordination with a KV-cache-aware router for cross-turn reuse. There are currently no further developments.
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View story:AWS introduces vLLM bidirectional KV transfer to reduce multi-turn TTFT
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Deepgram and AWS port Nova-3 to Trainium2
Deepgram and AWS collaborated to port the Nova-3 speech-to-text model to AWS Trainium2. According to information published by AWS Machine Learning on October 20, 2026, a single trn2.3xlarge instance can run 8 model replicas, processing 11%–70% more audio per dollar in offline batch processing scenarios than the previous solution. The team used TorchNeuron to complete the PyTorch port and tuned it through NKILib fused kernels and hand-written NKI kernels, among which the FFN kernel latency was reduced by 2.0x, depthwise conv1d was sped up by 1.5x, and the conv2d kernel unlocked batch sizes above 32. There are currently no subsequent development facts.
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