# Benchmark: xserv vs llama.cpp (Qwen3-8B)

**What this adds.** A standing baseline that compares xserv against **llama.cpp**
on both **response quality (correctness)** and **performance (TTFT / TPOT /
throughput)**, using the same model weights and standard public datasets. This
replaces HF transformers as our reference point — xserv already beat HF, so it
is no longer a useful performance bar.

- **Baseline engine**: llama.cpp, vendored as a submodule pinned to `b9371`,
  built with CUDA for SM120 (RTX 5090).
- **Same weights**: the Qwen3-8B safetensors are converted to a **BF16 GGUF**
  (`convert_hf_to_gguf.py --outtype bf16`) — no quantization, so the comparison
  is apples-to-apples.
- **Standard quality datasets**: **AIME 2025** (30 competition-math problems,
  exact-match boxed integer) and **GSM8K** (grade-school math, exact-match).
- **Black-box HTTP**: both engines are driven through the OpenAI-compatible
  streaming API; the driver measures TTFT/TPOT/throughput and scores answers.

See `docs/16-llama-cpp-comparison.md` for the design and `tools/bench/` for the
driver. One-click: `tools/sync-and-build.sh bench`.

## How it runs

The GPU host (dash5) has no outbound network, so datasets are fetched locally
(`tools/bench/fetch_datasets.py`) into JSON and the llama.cpp source is shipped
over with the project; everything builds and runs on the GPU host. The driver
runs **one engine at a time** (two BF16 8B models do not co-reside on a 32GB
GPU, and a resident idle engine would distort the other's numbers).

Generation mode is matched: xserv hardcodes Qwen3 **thinking off**, so the
driver sends `chat_template_kwargs={enable_thinking:false}` to llama.cpp.

## Results (RTX 5090, BF16, greedy, 8192 ctx, max_batch 4)

### Performance — llama.cpp is the stronger baseline

| scenario | metric | xserv | llama.cpp | xserv ÷ llama.cpp |
|---|---|---|---|---|
| single / medium | TTFT p50 (ms) | 28.0 | 17.7 | 0.63× |
| single / medium | TPOT p50 (ms/tok) | 17.5 | 10.4 | 0.60× |
| single / medium | throughput (tok/s) | 56.6 | 95.1 | 0.60× |
| concurrent-4 | throughput (tok/s) | 135.2 | 317.1 | 0.43× |
| concurrent-8 | throughput (tok/s) | 135.5 | 322.5 | 0.42× |

xserv runs at **~0.42–0.60×** llama.cpp. It saturates at `max_batch` (~135 tok/s)
while llama.cpp keeps scaling under load (~322 tok/s). This is the honest new bar.
The ratio is the same at 4096 and 8192 — TPOT is bandwidth-bound, not
context-bound at these sizes.

### Quality — parity, confirming xserv's numerical fidelity

| task | n | xserv | llama.cpp |
|---|---|---|---|
| GSM8K | 50 | 100.0% (50/50) | 96.0% (48/50) |
| AIME 2025 | 30 | 16.7% (5/30) | 23.3% (7/30) |

With equal context the two engines land at comparable AIME accuracy (within
the ±2-problem greedy-decode wobble band) and xserv edges ahead on GSM8K. At
8192 both generate full-length solutions (mean ~4.2k tokens), so neither is
truncated. The AIME difference (2 problems) is entirely within the run-to-run
non-determinism documented below. Per-problem analysis shows the disagreements
are due to different greedy-decode paths (different token at position ~500+
cascades into a different solution), not systematic precision errors.

On GSM8K, xserv strictly dominates: it gets 2 problems right that llama.cpp
misses, and never misses one that llama.cpp gets.

## Findings the benchmark surfaced

1. **Context must be provisioned per-request, not total.** A first run showed
   xserv 20.0% vs llama.cpp 3.3% on AIME — an artifact: llama.cpp divides total
   `-c` across `--parallel` slots, so `-c 4096 --parallel 4` gave each request
   only **1024 tokens**, truncating long AIME solutions before the boxed answer
   (capped at ~940 generated tokens). GSM8K (~280 tokens) was unaffected, which
   is how we caught it. Fixed: per-slot context = `max_seq_len` (total
   `-c = max_seq_len × parallel`). After the fix, AIME is at parity (above).
2. **xserv OOM'd at `--max-seq-len 8192` — now fixed.** xserv used to eagerly
   pre-allocate its paged-KV pool (`blocks_per_seq × max_batch × 2`, ~9GB at
   8192) on top of the 16GB weights, exceeding 32GB at startup. Fixed by sizing
   the pool to *available VRAM* (`cudaMemGetInfo`) instead of worst-case demand,
   plus vLLM-style **swap to pinned host memory**: when running sequences grow
   past the GPU pool, the newest are evicted to host and swapped back when blocks
   free up (`--swap-space-gb`, default 8). The results above run at 8192 with **0
   swap events** — the VRAM-sized pool alone covers this load; swap is the
   overload safety net (verified lossless under a forced-small pool).
3. **xserv decode is not run-to-run deterministic.** The same greedy (temp 0)
   AIME config produced 6/30 / 7/30 / 6/30 across runs — non-deterministic CUDA
   reductions flip an argmax over long (~3k-token) generations. Harmless for
   serving, but it explains why long-sequence accuracy wobbles by a problem.
4. **GEMV race condition corrupted decode outputs — now fixed.** The custom
   K-split GEMV kernel (used for all M=1 decode-step projections with N≥256)
   had a race condition: block k=0 zeroed the FP32 accumulator (`y_fp32[col] =
   0.0`) while other K-blocks were already atomicAdding to it. Since CUDA
   provides no inter-block ordering within a single kernel launch, the zero
   could land before, during, or after other blocks' writes. Fix:
   `cudaMemsetAsync` on the stream before the kernel launch, which guarantees
   the buffer is zeroed before any block executes. This bug was introduced
   after the initial benchmark and caused systematic decode-time precision
   errors that degraded GSM8K accuracy from 98→80% range.

Raw artifacts (per-request timings, per-problem prediction/gold) are written to
`bench-out/` as `comparison-<stamp>.{md,json}` (gitignored).