vh
3132a16ca0
Every fv-ml1 link on the Homepage dashboard was broken. Measured against the
live dashboard API before the fix: 16 entries pointing at the dead 10.250.50.54
and zero at the live 10.251.50.54, covering gen, M.O.G.-SEC, Scriberr, Embed,
Rerank, Reward, Coder, Dockge and six dormant seats.
The miss was structural, not careless. fv-ml1-rename-sweep.sh works from an
allowlist assembled from files that mention the HOST, and a homepage.href label
mentions only an IP -- so every stack whose sole stale reference was a label
fell outside it. The allowlist now covers those 24 files, and records how to
derive the list next time (grep the old address, subtract history) rather than
enumerating from memory.
History is still untouched, and the exclusions are now written down with the
reason each one keeps the old address: recorded benchmark results, whose
base_url is part of a measurement's provenance; the one LiteLLM comment
preserving a retired hand-test endpoint; and the cutover runbooks, where the old
address is the subject matter.
Two bugs found while applying it, both fixed here:
- deploy-stack.sh rejected any stack name containing a dot, so qwen3.5-122b,
qwopus3.5-122b and mistral-medium-3.5 could not be deployed by the script at
all. The check exists to stop path traversal, which means rejecting ".." and
"/" -- not every dot. Traversal is now rejected explicitly and tested.
- stacks/scriberr/.env.example allowed CORS only from the dead IP and from
scriberr.ana.internal, which no longer resolves; the box is at the fv site
and DNS already carries scriberr.fv.internal. The live .env had both stale
origins, i.e. an allowlist with nothing reachable in it.
Host side, applied separately: canonical pushed for the 16 stacks whose only
difference from the host was this renumber, and an in-place address-only fix for
the nine whose host copy has genuinely drifted or has no canonical copy, so that
drift survives for a deliberate reconciliation instead of being clobbered. Every
compose.yaml on fv-ml1 now reads 10.251.50.54. The labels themselves only take
effect at container creation, so the running containers still need recreating.
2.6 KiB
sglang — vLLM-vs-SGLang bench on fv-ml1
Stood up to benchmark SGLang against vLLM on the same model + hardware, to see whether SGLang's throughput/latency wins justify it as a serving option (or a replacement) for the granite path on the Blackwells.
Bench-oriented, not a permanent service (yet). If SGLang wins decisively → promote to a real stack + add a gateway entry. Otherwise tear it down after.
Capability (checked 2026-06-13)
SGLang 0.5.13 (torch 2.11+cu130) supports our formats on Blackwell sm_120:
compressed-tensors (the llm-compressor NVFP4 W4A4 output), fp8,
modelopt_fp4, petit_nvfp4, mxfp4, and fp4_e2m1 KV. So the bench can be a
real NVFP4 head-to-head, not just FP8.
The one rule for a fair bench
Exclusive GPU, same everything. Both engines must run on a card with NO
co-tenants (no eval endpoints, no llama-swap hot-load), same model, same context
length, same prompt profile, same concurrency sweep, driven by the SAME load
generator (bench.py) — not each engine's self-flattering built-in benchmark.
The contention that skewed the earlier vLLM throughput probe is exactly what to
avoid here.
Run
# 1. On fv-ml1, after the eval frees a GPU: cp .env.example .env, set
# SGLANG_MODEL / SGLANG_QUANT to match the vLLM config under test, and
# SGLANG_GPU_ID to an EXCLUSIVE card.
scripts/deploy-stack.sh fv-ml1 sglang
# (or docker compose up -d on the host)
# 2. Bench SGLang:
python3 stacks/sglang/bench.py --url http://10.251.50.54:30000/v1 \
--model granite-4.1-8b-nvfp4 --concurrency 1 10 50 100 200 --in-tokens 2048 --out-tokens 256
# 3. Stop SGLang, bring up vLLM on the SAME GPU + model, bench identically:
python3 stacks/sglang/bench.py --url http://10.251.50.54:8006/v1 \
--model granite-4.1-8b-nvfp4 --concurrency 1 10 50 100 200 --in-tokens 2048 --out-tokens 256
# 4. Repeat the sweep at --in-tokens 30000 (the prefill-heavy agent-memory
# regime, where the engines can diverge sharply).
Metrics (bench.py reports)
- agg_tok/s — aggregate output throughput at concurrency N (the headline)
- ttft_p50 / p99 — time-to-first-token (prefill latency; matters most at high in-tokens)
- tpot_ms — time-per-output-token (decode latency; the per-stream UX number)
mem-fraction-static is SGLang's gpu-memory-utilization analog; set it high
(0.85–0.90) on an exclusive 96 GB card.
Bench target
Bench whichever format wins Brokkr's quality eval (the production-relevant one): 8B-NVFP4-W4A4 if that's the path, else FP8. Benching a format we won't ship is academic. Optionally run both formats to see if the engine ranking flips.