Short definitions for the terms used across these docs. Each links to the page that covers it properly.
| Term | Meaning |
|---|---|
| Ember | OtherU's own inference engine — a narrow C/C++ runtime for DeepSeek V4 Flash on AMD Strix Halo, not a general GGUF loader. See Ember. |
| ds4 | The upstream reference architecture (antirez/ds4) that Ember reimplements. Ember is a separate codebase, not a fork. |
| Hermes Agent | The agent layer: channel routing (Telegram, Signal, WhatsApp), tool dispatch, memory and scheduling. Ember provides inference; Hermes decides what to do. See Connect Hermes Agent. |
| Hindsight | The persistent memory system from Vectorize that gives Hermes long-term recall. See Hindsight Memory. |
| Memory bank | A Hindsight container for memories; the API is scoped per bank (/v1/default/banks/{bank_id}/...). |
| DSpark | The small draft model used for speculative decoding — it proposes tokens that the main model verifies, raising decode throughput. |
| Speculative decoding | Running a small drafter ahead of the main model and accepting its tokens when they match, so several tokens can be produced per full forward pass. |
| Acceptance rate | The fraction of drafted tokens the main model accepts. Workload-dependent — high on predictable text, lower on free-form sampling. |
| Abliteration | A targeted edit that removes a model's refusal direction while leaving general capability intact. See Quantization Pipeline. |
| Quantization | Storing model weights at reduced precision to fit in memory. OtherU ships DeepSeek-V4-Flash at 2.58 bits per weight. |
| bpw | Bits per weight — the average precision of the quantized model. |
| imatrix | An importance matrix collected from sample prompts, used to decide which weights tolerate the most aggressive quantization. |
| affine fp2 | OtherU's 2-bit format, Q2_0_ROCMFP2, where value = code · scale − offset. |
| ROCmFPx | The family of custom low-precision tensor formats Ember understands. Mainline llama.cpp, LM Studio and Ollama do not implement them. |
| MoE | Mixture of Experts — only a subset of "expert" weights runs per token, so a large model can serve at a smaller active cost. |
| KV cache | Cached attention state for tokens already processed, so a continued conversation need not re-process the whole prompt. Ember can page it to disk. |
| Prefill | Processing the prompt before any output token is produced. Measured in tokens per second. |
| Decode | Generating output tokens one at a time after prefill. Usually the slower phase. |
| gfx1151 | The GPU architecture identifier for the AMD Radeon 8060S iGPU in Strix Halo — what Ember's kernels target. |
| Strix Halo | The AMD Ryzen AI MAX+ platform with a large unified-memory iGPU, used as the reference hardware. See Hardware. |
| JetKVM | The hardware KVM device that lets the agent see and control a physical machine over WebRTC capture and USB HID, with no software installed on the target. |
| GGUF | The single-file model format Ember loads. |