Benchmarks
10 posts tagged #benchmarks
10 posts on Benchmarks, spread across LLM Hardware & Local AI (4), AI in Production (3), AI Agents (2) — most read: “9 Picks for the Best Local Model for Agentic Coding [2026]” and “How to Run Qwen 3.8 Flash Next 125B in Strata on an RTX 4090”.
AI and Machine Learning How to Run Qwen 3.8 Flash Next 125B in Strata on an RTX 4090
A reproducible Strata setup for Qwen 3.8 Flash Next (125B) on RTX 4090, plus the KV cache knobs that decide whether you hit ~100 tok/s or hit OOM.
AI and Machine Learning Kolibri Open Weight Model Benchmark: 1M Context Reality Check [2026]
Aleph Alpha’s Kolibri ships downloadable weights under Apache-2.0 and claims a 1M-token context. Here’s what that changes for EU/on-prem teams and how to benchmark it reproducibly on an A10/A100.
Technology Intel Arc B‑Series Local LLM Benchmark [2026]: Arc Done Right
A reproducible Intel Arc B‑series local LLM benchmark harness (TTFT, tok/s, VRAM, power), plus a Linux/Windows compatibility matrix and Arc-friendly GGUF quants that won’t OOM at 8K context.
AI and Machine Learning Mercury 2.5 770 tok/s Benchmark: The Production Playbook
Mercury 2.5 is clocking ~780 tok/s on Artificial Analysis. At that speed, decode stops being your bottleneck. Here’s what actually changes in production: batching, streaming UX, context packing, cost controls, and evals that don’t lie.
Developer Tools How to Measure AI Coding Agent Harness Overhead
Model wars are a distraction. If you can’t measure prompt scaffolding, repo indexing, context packing, tool calls, and test loops, you’re not benchmarking your coding agent. Here’s a reproducible protocol.
AI and Machine Learning Prompt Compression Token Savings Benchmark: RTK vs Reality
Token savings are easy to market and hard to bank. Here’s the benchmark framework I trust for RTK-style compression: cost-per-success, tool-call failures, and latency.
Developer Tools Bun vs Node Performance 2026: Cold Start, Memory, WebSockets
Cold starts and memory decide your infra bill. WebSockets decide your tail latency. Here’s how Bun and Node compare in 2026, with a reproducible harness and deployment math.
AI and Machine Learning AI Agent Evaluation Framework: 8 Metrics Beyond Task Success
If your agent eval is just “did it finish the task?”, you’re flying blind. Here’s a 2026-ready scorecard for tool correctness, recovery, safety, and cost-per-success—plus a regression suite blueprint you can actually run in CI.
AI and Machine Learning 9 Picks for the Best Local Model for Agentic Coding [2026]
A practical decision tree for agentic coding: pick local models by repo size and VRAM tier (16GB/24GB/48GB+/CPU), with context, tool-calling reliability, and quantization rules that actually hold up.
Technology Local LLM Benchmark Methodology [2026]: TTFT vs tok/s Done Right
Stop screenshot-benchmarking. Here’s a reproducible local LLM benchmark methodology for 2026 that separates TTFT from throughput and reports rerunnable results.
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