Million-token context serving is becoming a critical requirement for industry LLM systems for real deployments. However, practical 1M-context serving for open-source models has remained heavily CUDA-centric, largely because the required long-context runtime features are not fully available on AMD ROCm. With AMD GPUs now reported at around 19% market share, this gap matters. We address it by enabling 1M-token Qwen3 serving on AMD GPUs with ROCm. We present , a runtime stack that enables million-token serving for the widely used open-source Qwen3 family on AMD ROCm. achieves this by intercepting vLLM’s attention routing at runtime and redirecting queries, keys, and values to JIT-compiled Triton Dual Chunk Attention (DCA) kernels, without forking the serving engine. We then harden the surrounding runtime to make full 1M-token execution practical: DCA-aware paged metadata, tiled single-request chunked prefill, MoE token chunking that reduces per-rank transient memory from approximately 91.6 GiB to 10.6 GiB, and FP8 GEMMs with hardened hipBLASLt kernel selection. We evaluate across multiple million-token context tasks and show consistent serving performance and exact retrieval correctness. To our knowledge, this is the first reported end-to-end Qwen3 million-token serving path on AMD ROCm integrated with vLLM.

Long-context and RAG workloads expose production bottlenecks that raw tokens/s and offline accuracy do not capture, including prefill cost, KV-cache pressure, prefix reuse, routing, and latency SLOs. We introduce OpenDC-Infer-LC, a hardware- and backend-neutral benchmark for evaluating complete data-center LLM serving stacks under long-context and retrieved-context workloads. The benchmark ranks systems by SLO-qualified, quality-preserving goodput: valid output-token throughput at the highest load satisfying p95 TTFT, p95 TPOT, p95 end-to-end latency, and success-rate targets. Because RAG and LongBench quality depends on model ability, retrieval difficulty, and task difficulty, OpenDC-Infer-LC uses a relative quality guardrail against a low-load reference run for the same model and workload, while reporting absolute quality separately. Pilot results on AMD MI355X and NVIDIA B200 show that deployable capacity depends strongly on backend scheduling, cache reuse, context length, routing, quantization, and runtime maturity.

LLM assistants deployed outside the United States often inherit US-centred interactional defaults. We introduce an Australian-facing assistant, and test whether local voice is better learned during fine-tuning or imposed with an inference-time prompt. We define Australian Voice as pragmatic alignment, directness, modesty, warmth, register control, and task fidelity, rather than slang or spelling substitution. We build a governed dataset from user research, human rewrites, contrastive negatives, reviewed synthetic examples, and replay data. In a blind evaluation by Australian English users (356 question-level ratings over 89 prompts), the raw 50% voice/replay mix scores best on all five dimensions (4.24/5; 102 best-pick wins). Prompted variants underperform every matched fine-tuned model.