DeepSeek V4.1-Flash uses asymmetric MoE activation and a smaller KV cache to target lower inference cost

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GitHub Copilot Metrics Add OpenCode and Other Clients: Requests Are Not Prompts

GitHub added totals_by_client to the Copilot usage metrics API on October 23, 2026. Enterprise and organization reports can now show model traffic from OpenCode, Claude Code, Codex, Zed, Copilot CLI, GitHub apps, cloud agents, code review, VS Code, JetBrains, and other clients. This article helps administrators build reliable one-day and 28-day reports without confusing model calls with user prompts. It explains request, session, token, model, and selection-mode fields; separates client traffic from agent-app jobs; and adds checks for unknown identifiers, empty arrays, permissions, and coverage. The result is a governance trend report, not an exact invoice or individual productivity score.

GitHub Copilot Usage API Names Custom Skills, Agents, and MCP Servers: How to Read the Report

GitHub updated the Copilot usage metrics API on October 22, 2026, so customer-defined skills, agents, MCP servers, slash commands, and plugins can appear by configured name instead of being grouped entirely under other or custom. Each totals_by_* list now includes as many as 25 entries per record, up from five. This article helps administrators turn the change into a reliable adoption report. It covers required Copilot CLI and SDK versions, a naming review before reports are shared, one-day and 28-day comparisons, and a reconciliation check against known pilots. It also explains why historical days, path-like names, outdated clients, and blocked telemetry can still undercount activity. The metrics are useful for trends and enablement, not exact billing.

GitHub Copilot Model Policy API Is Generally Available Across Four Control Levels

GitHub has made GET and PATCH endpoints for Copilot model policies generally available at four levels: enterprise, enterprise team, agent application, and organization. This is an administrative API release, distinct from the earlier global model policy interface. The article gives technical administrators a cautious workflow: read current state, save a baseline, identify the controlling level, review the intended delta, apply one minimal update, and read the result back. No live organization, credential, or production mutation is used in the example. The approach helps teams automate inventory and consistency checks without turning an API into an unreviewed bulk-change mechanism, especially where enterprise policy delegates control to lower levels.

AWS scales MoE reinforcement learning with EKS, EFA, and DeepEP

AWS published an architecture for large-scale mixture-of-experts reinforcement learning on September 25. It separates rollout generation, reward-model inference, policy training, experience buffers, and checkpoints across Amazon EKS node groups, while Elastic Fabric Adapter and DeepEP handle communication and Amazon S3 provides durable storage. AWS reports that, on the same 48 P5en instances, its DeepEP-over-EFA configuration increased aggregate rollout throughput by 40 percent in an internal workload and scaled to roughly one thousand accelerators. The result is tied to a specific sparse model, software versions, instance topology, and training mix, so teams should reproduce the baseline before treating the number as a general performance promise.

AWS HyperPod Inference Gateway routes requests with live GPU signals

AWS announced SageMaker HyperPod Inference Gateway Tier 1 on September 18. The EKS managed add-on uses a Body-Based Router to read the model field from OpenAI-compatible requests and an Endpoint Picker that scores KV-cache use, queue depth, LoRA residency, prefix-cache hits, and running requests. AWS reports tail-latency gains across several matched-replica scenarios and summarizes up to an 82% first-token reduction, while a uniform steady workload remained comparable to round robin. Teams should repeat the comparison on their own hardware, models, and traffic. A production trial should hold replicas and requests constant, include failure and 429 behavior, and preserve the prior routing configuration for immediate rollback.

A durable Dense versus MoE model-selection checklist beyond total parameters

NVIDIA's September 15 technical article explains the central Dense versus Mixture-of-Experts tradeoff. Dense models activate all parameters for every token; MoE models keep all experts in memory but route each token through a subset, separating memory cost from compute cost. Nemotron 3.5 Lightning is cited as a 30B-total model with 3B active parameters per token. MoE can raise throughput at similar total size, while latency advantages narrow at high concurrency, fine-tuning can create router imbalance, and quantization affects routing and projection layers differently. This checklist turns those factors into a repeatable selection task using a fixed request set, target hardware, quality thresholds, concurrency steps, and cost per accepted result.

Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient.