feat(sglang): stage vLLM-vs-SGLang bench stack on ana-ml2

SGLang 0.5.13 confirmed to support our formats on Blackwell sm_120
(compressed-tensors NVFP4 W4A4, fp8, modelopt_fp4, petit_nvfp4, fp4_e2m1 KV),
so the bench can be a real NVFP4 head-to-head. Parameterized compose (model/
quant/GPU via .env) + a common streaming load generator (bench.py: agg tok/s,
TTFT p50/p99, TPOT) so both engines are driven identically on an exclusive GPU.
Bench-oriented; promote to a real stack only if SGLang wins. Launch deferred
until the NVFP4 eval frees a GPU.

stacks/sglang/.env.example

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+# SGLang bench tunables. Copy to .env on ana-ml2 before deploying.
+#
+# Set SGLANG_MODEL + SGLANG_QUANT to match whatever vLLM config we're benching
+# against (the format that wins Brokkr's eval is the production-relevant target).
+
+SGLANG_VERSION=latest
+SGLANG_PORT=30000
+
+# EXCLUSIVE GPU for the bench (no co-tenants → fair numbers). Default 0; ensure
+# the eval endpoints + any llama-swap hot-load are torn down / off this GPU first.
+SGLANG_GPU_ID=0
+
+# ── Model under test ── point at the SAME checkpoint vLLM serves ──────────────
+# NVFP4 W4A4 (the Blackwell-relevant format):
+SGLANG_MODEL=/aimodels/nvfp4/granite-4.1-8b-NVFP4-W4A4-test
+SGLANG_QUANT=compressed-tensors
+SGLANG_SERVED_NAME=granite-4.1-8b-nvfp4
+#
+# FP8 alternative (engine comparison on the current prod format):
+#   SGLANG_MODEL=ibm-granite/granite-4.1-8b-fp8
+#   SGLANG_QUANT=fp8
+#   SGLANG_SERVED_NAME=granite-4.1-8b
+
+# KV cache dtype — match vLLM's fp8 for apples-to-apples (or fp4_e2m1 to test
+# SGLang's FP4 KV, but then vLLM isn't comparable):
+SGLANG_KV_DTYPE=fp8_e4m3
+
+# Context length — match the vLLM endpoints (64k for the heavy-substrate bench):
+SGLANG_CTX_LEN=65536
+
+# Static memory fraction (SGLang's gpu-memory-utilization analog). On an
+# exclusive 96GB card this can be high (0.85-0.90).
+SGLANG_MEM_FRACTION=0.85