- Add ChatModel enum dispatching between Qwen3 and GptOss based on
config.is_moe(), following the TP engine pattern.
- Add --tp N flag for tensor-parallel inference (required for 39GB
gpt-oss-20b which doesn't fit on a single 32GB GPU).
- Add gpt-oss harmony chat template with channel/message format.
- Replace hardcoded is_stop_token() with tokenizer.is_eos() for
multi-model EOS support.
- Restore gpt-oss hardcoded prompt template in server api.rs, lost
during the Jinja template refactor.
- Fix GEMV race condition: the K-split kernel zeroed the FP32
accumulator inside the kernel (block k=0) while other blocks
atomicAdd'd concurrently. Pre-zero with cudaMemsetAsync instead.
- Update benchmark docs with post-fix results.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
AIME 2025 + GSM8K at TP=1/2/4. Quality on par across engines/TP. Opposite
perf scaling: xserv TPOT improves with TP (21->17->15ms) while llama.cpp
row-split regresses over PCIe (10->19->20ms), crossing over so xserv is faster
at TP=4. Includes the clean same-path bench-tp scaling (58/76/86 tok/s).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Re-ran the full comparison at --max-seq-len 8192 now that xserv handles it:
- OOM finding resolved — pool sized to available VRAM + vLLM-style host swap;
8192 runs with 0 swap events (swap is the overload safety net).
- Quality at parity with equal context: AIME 20.0% vs 20.0%, GSM8K 98% vs 96%.
- Speed unchanged relative to llama.cpp (~0.42-0.60x); TPOT is bandwidth-bound.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Record what the new baseline adds (llama.cpp pinned b9371, same BF16 weights,
AIME 2025 + GSM8K) and the measured results: performance (xserv ~0.45-0.61x
llama.cpp throughput) and quality parity (GSM8K 94% vs 96%, AIME 23.3% vs 20%
after the context fix), plus the findings the bench surfaced.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
