News & guides
Grouped-Query Attention: Why Long Context Got Affordable
GQA shrinks the KV cache by sharing keys and values across query heads. What it costs in quality, and why it is on the spec sheet of nearly every model.
· 9 min read
Inference vs Training Cost: Which One Is Actually Your Problem
Training is a one-off capital cost you never pay. Inference is a recurring cost you pay per request. How the two differ and why the distinction shapes pricing.
· 9 min read
Instruction Tuning: How a Text Predictor Becomes an Assistant
A pretrained model continues text; it does not answer questions. Instruction tuning is the small, cheap stage that turns one into the other.
· 8 min readKV Cache Explained: The Memory Behind Long Context
The KV cache is why generation is fast and why long context is expensive in memory rather than compute. What it stores and what it costs you.
· 8 min read
Layer Normalisation: The Part That Keeps Training Stable
Why deep transformers need normalisation, what pre-norm changed, and why RMSNorm won. The connection to quantisation and low-precision serving.
· 8 min read
Load Balancing in MoE: Stopping Experts Going Idle
An MoE model only delivers its promised capacity if traffic spreads across experts. Collapse during training and skew during serving are both real risks.
· 9 min read
Logits and Softmax: What a Model Really Outputs
A model does not emit words, it emits a score for every token in its vocabulary. What softmax does to those scores and why probabilities are not confidence.
· 8 min readLong Context Degradation: How Quality Falls Before the Limit
Accuracy does not hold flat and then fall off a cliff at the context limit. It declines gradually, and the decline starts far earlier than most teams assume.
· 9 min read
max_tokens and Truncation: Why Your Output Stops Mid-Sentence
A response that ends mid-word is almost never a model failure. It is a cap you set, or a default you never set. How truncation actually happens.
· 8 min read
Model Parallelism: How Huge Models Are Split to Run
Tensor, pipeline and expert parallelism split a model across devices in different ways. Each moves a different cost onto the network, and that decides latency.
· 9 min readModel Size vs Capability: Why Bigger Stopped Meaning Better
Parameter counts once tracked capability closely and no longer do. What size still predicts, what it never predicted, and what to check instead.
· 9 min readMulti-Head Latent Attention Explained
MLA compresses the KV cache into a small latent vector instead of sharing heads. Why it trades compute for memory, and which models bet on it.
· 9 min readReady to try it?
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