Mistral AI: The European Open-Source Challenger Mistral AI is a Paris-based AI lab founded in 2023 by former Meta and Google DeepMind researchers. They have established themselves as the leading European AI company and one of the most important open-source model publishers globally. Their models are known for strong performance relative to parameter count, Apache … Read more
Who Is DeepSeek and Why Do They Matter? DeepSeek is a Chinese AI research lab founded in 2023 as a subsidiary of High-Flyer, a quantitative hedge fund. In a little over a year, they released a series of models that genuinely surprised the AI research community — not because they were incremental improvements, but because … Read more
How to Use This Guide Choosing an LLM in 2026 is genuinely hard because the right model depends on your use case, budget, deployment constraints, and quality requirements. A model excellent for code generation may be mediocre for creative writing. A frontier model that tops benchmarks may cost 20x more than a smaller model that … Read more
AWS EC2 GPU Instance Families for LLMs AWS offers several GPU instance families suited to LLM workloads, each targeting a different use case and budget point. Understanding the differences prevents paying for capabilities you do not need or under-provisioning for your actual workload. p3 instances use NVIDIA V100 GPUs (16 GB VRAM each). The p3.2xlarge … Read more
When Do You Need Multiple GPUs? A single GPU handles most LLM workloads up to 34B parameters at Q4. You need multiple GPUs when: the model weights do not fit in a single GPU’s VRAM, you need higher throughput than one GPU delivers for concurrent users, or your training job would take impractically long on … Read more
What the RTX 5090 Brings to LLM Inference The NVIDIA RTX 5090, released in early 2025, is the current flagship consumer GPU based on the Blackwell architecture. For LLM inference specifically, two specifications matter most: it ships with 32 GB of GDDR7 memory — double the 5090’s predecessor, the RTX 4090 — and delivers memory … Read more
Why GPU Cloud Costs Vary So Much GPU cloud infrastructure is sold through dramatically different business models, and understanding them is key to finding the right cost-performance fit. The major hyperscalers (AWS, GCP, Azure) charge premium prices for the ecosystem value they provide — managed services, compliance certifications, SLAs, and deep integration with the rest … Read more
The Three Components of LLM Latency LLM inference latency has three distinct components that require different optimisation strategies. Time to first token (TTFT) is the delay between sending a request and receiving the first token of the response — dominated by prefill time, the cost of processing all input tokens. Time per output token (TPOT) … Read more
Why the H100 vs A100 Decision Matters The NVIDIA A100 has been the workhorse GPU for LLM training and inference since 2020. The H100, released in 2022 and widely available by 2023, represents the next generation — faster, more memory-efficient, and with new hardware features specifically designed for transformer workloads. For teams provisioning GPU infrastructure … Read more
Why Apple Silicon Changed Local LLM Inference Before Apple Silicon, running large language models locally meant either accepting painfully slow CPU inference or buying expensive NVIDIA GPU hardware. Apple’s M-series chips changed this with unified memory architecture — CPU, GPU, and Neural Engine share a single high-bandwidth memory pool. A Mac Studio with 128 GB … Read more
