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Insights

Technical deep dives, industry analysis, and perspectives on AI inference from the Infercom team.

TechnicalInferenceAPI
Stop Chasing Determinism - A Practical Guide to LLM Consistency
Temperature, top_p, top_k and repetition_penalty explained - why temperature=0 is the riskiest setting for consistency, why batch size changes your output, and what gives you reliable LLM results.September 20, 202622 min read
StrategyTechnicalSecurity
Open-Weight AI Models: Why They're a Strategic Advantage
Open-weight models like Gemma and MiniMax power your AI applications - and they give you freedoms proprietary APIs can't. What open weight means, why it reduces lock-in, and how it keeps you in control of your data.July 15, 202612 min read
TechnicalPerformanceTechnology
713 Tokens Per Second: The Architecture Behind Ultraspeed
Why dataflow architecture outperforms GPUs for LLM inference. Technical explanation of memory bottlenecks, spatial execution, and the decode problem.May 27, 202610 min read
TechnicalPerformanceInference
TTFT, Throughput and Latency: LLM Inference Speed Explained
When one provider advertises 400 tok/s and another claims sub-200ms latency, they're measuring different things. Learn which metrics matter for your workload.May 26, 20268 min read
TechnicalPerformance
Inference Speed in Agentic Coding: Why Token Throughput Matters
Coding agents resend their context as input on every turn and produce a few hundred output tokens each time. Why output speed decides how long developers wait.May 15, 20268 min read
TechnicalInferencePerformance
What 'Price Per Token' Doesn't Tell You
If you're comparing AI inference providers by token price alone, you're missing the factors that actually determine cost and performance.May 5, 202612 min read

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