Faster LLMs. Fewer GPUs. Your servers.

Where the speed comes from

• QuantisationQuantisationStoring model weights with fewer bits (e.g. 8 or 4 instead of 32), cutting memory and speeding up inference with little loss in accuracy.FP8 / INT8 / INT4INT44-bit integer weights: one-eighth the memory of standard 32-bit (FP32) weights. weights cut memory and raise throughput.
• Continuous batchingBatchingProcessing many requests together on the same hardware. Continuous batching lets new requests join mid-flight, so GPUs stay busy.Requests join in-flight to keep GPUs fully busy.
• Paged KV-cacheKV-cacheA memory of what the model has already read, so it does not recompute it for every new word. Paging it lets more users share one GPU. & prefix cachingMore concurrent users per GPU; shared prompts reused.
• Speculative decodingSpeculative decodingA small fast model drafts several words ahead; the large model checks them in one step. Same output, lower latency.Draft-and-verify generation for lower latency.
• Tensor & pipeline parallelismLarge models split efficiently across GPUs.
• Autoscaling & monitoringThroughput, latency and cost tracked live.