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@@ -1,6 +1,6 @@
 # club-3090
 
-**Recipes for serving LLMs locally on RTX 3090s.** Multi-engine (vLLM, llama.cpp, ik_llama, SGLang), multi-model, model-agnostic by design.
+**Recipes for serving LLMs locally on RTX 3090s.** Multi-engine (vLLM, llama.cpp, ik_llama), multi-model, model-agnostic by design.
 
 If you have one or two RTX 3090s and want to run modern LLMs at home, in a homelab, or as a dev backend — this repo collects the working configs, patches, and benchmarks.
 
@@ -65,14 +65,14 @@ bash scripts/update.sh
   - 🏎 **vLLM dual** = max throughput. Up to **127 TPS code** (DFlash) or **4 concurrent streams @ 262K** (turbo). Full feature stack (vision · tools · MTP · streaming).
   - 🛡 **llama.cpp single** = max robustness. Full **200K context** on one 3090 (max-safe — fills cleanly with margin; see [CLIFFS](docs/CLIFFS.md)). Stress-tested clean: no prefill cliffs, 25K-token tool returns work, 91K needle ladder passes. **~51 / 60 TPS** (Q4_K_M + MTP) — slower than vLLM dual but doesn't crash on real-world tool-using agents.
 - **Validated docker compose configs** for both routes — drop-in OpenAI-compatible API on `localhost:8020`
-- **Multi-engine**: vLLM (full features), llama.cpp (max ctx + robustness), ik_llama (best GGUF quants), SGLang (currently blocked, watch list)
+- **Multi-engine**: vLLM (full features), llama.cpp (max ctx + robustness), ik_llama (best GGUF quants). _(SGLang was evaluated — currently blocked on Ampere; see [`docs/engines/SGLANG.md`](docs/engines/SGLANG.md).)_
 - **Model-agnostic**: today ships curated configs for Qwen3.6-27B and friends; structure scales as we add models
 - **Universal `pull`** (v0.8.0; extended in v0.8.2) — evaluate any safetensors HF repo, get an honest one-line fit verdict (`--recommend`), and when a pull hard-blocks, send the redacted diagnostic back in one consented step (`--submit-last`). Broader arch coverage each release. See [`docs/PULL.md`](docs/PULL.md)
 
 **New to local AI itself?** → [`docs/LOCAL_AI_PRIMER.md`](docs/LOCAL_AI_PRIMER.md) — plain-English: how hardware / engines / model sizes / quants fit together.
 **New here?** → [`docs/GETTING_STARTED.md`](docs/GETTING_STARTED.md) — 5-minute clone-to-curl path.
 **Already running, want to compare engines?** → [docs/engines/](docs/engines/)
-**Picking an engine** (vLLM / llama.cpp / SGLang / ik_llama)? → [docs/INFERENCE_ENGINES.md](docs/INFERENCE_ENGINES.md)
+**Picking an engine** (vLLM / llama.cpp / ik_llama)? → [docs/INFERENCE_ENGINES.md](docs/INFERENCE_ENGINES.md)
 **Confused by quant names** (Q4_K_M vs IQ4_KS vs AWQ)? → [docs/QUANTIZATION.md](docs/QUANTIZATION.md)
 **Hardware questions** (4090, NVLink, power caps)? → [docs/HARDWARE.md](docs/HARDWARE.md)
 **Don't know what TPS / KV / MTP mean?** → [docs/GLOSSARY.md](docs/GLOSSARY.md)
@@ -97,10 +97,10 @@ Each hardware page lists every supported model with the working composes for tha
 
 | Model | Status | Card counts | Engines | Highlights |
 |---|---|---|---|---|
-| **[Qwen3.6-27B](models/qwen3.6-27b/)** | Production-ready ⭐ | 1× / 2× 3090 | vLLM ✅ · llama.cpp ✅ · ik_llama ✅ · SGLang ❌ blocked | Vision · tools · MTP n=3 · up to 262K ctx · vLLM dual = 89/127 TPS · llama.cpp single = 200K max-safe, no prefill cliffs · ik_llama IQ4_KS = ~60/69 TPS (fastest single-card) |
-| **[Gemma 4 31B](models/gemma-4-31b/)** | Production-ready (dual-card only on Ampere 24 GB) | 2× 3090 only ¹ | vLLM ✅ · llama.cpp ❌ · SGLang ❌ | Vision · tools · MTP n=3 (Google official drafter) **OR** DFlash n=7 (z-lab drafter) · up to 262K ctx via INT8 PTH KV (PR [#40391](https://github.com/vllm-project/vllm/pull/40391) vendored) · MTP dual = 106/141 TPS at 32K, 95/126 at 262K · DFlash dual = 105/177 TPS at 32K (code-optimal) |
-| **[Qwen3.6 35B-A3B](models/qwen3.6-35b-a3b/)** ⭐ NEW v0.7.3 | Preview (production-track blocked on Genesis v7.73.x) | 2× 3090 | vLLM ✅ (preview) · SGLang ❌ · llama.cpp ❌ | **MoE (256 experts × 8 active, ~3 B active params)** · vision · tools · upstream native loader via [vLLM PR #42521](https://github.com/vllm-project/vllm/pull/42521) · preview dual = **182/177 TPS at 16K** (no MTP, no TQ3, no Genesis) |
-| **[Gemma 4 26B-A4B](models/gemma-4-26b-a4b/)** ⭐ NEW v0.7.3 | Production via AWQ (Intel AutoRound INT4 blocked on Ampere) | 2× 3090 | vLLM ✅ (AWQ overlay) · SGLang ❌ · llama.cpp ❌ | **MoE (128 experts × 8 active, ~4 B active params)** · vision · tools · AWQ dual = **139/139 TPS at 32K**, CV 0.2% / 0.0% |
+| **[Qwen3.6-27B](models/qwen3.6-27b/)** | Production-ready ⭐ | 1× / 2× 3090 | vLLM ✅ · llama.cpp ✅ · ik_llama ✅ | Vision · tools · MTP n=3 · up to 262K ctx · vLLM dual = 89/127 TPS · llama.cpp single = 200K max-safe, no prefill cliffs · ik_llama IQ4_KS = ~60/69 TPS (fastest single-card) |
+| **[Gemma 4 31B](models/gemma-4-31b/)** | Production-ready (dual-card only on Ampere 24 GB) | 2× 3090 only ¹ | vLLM ✅ · llama.cpp ❌ | Vision · tools · MTP n=3 (Google official drafter) **OR** DFlash n=7 (z-lab drafter) · up to 262K ctx via INT8 PTH KV (PR [#40391](https://github.com/vllm-project/vllm/pull/40391) vendored) · MTP dual = 106/141 TPS at 32K, 95/126 at 262K · DFlash dual = 105/177 TPS at 32K (code-optimal) |
+| **[Qwen3.6 35B-A3B](models/qwen3.6-35b-a3b/)** ⭐ NEW v0.7.3 | Preview (production-track blocked on Genesis v7.73.x) | 2× 3090 | vLLM ✅ (preview) · llama.cpp ❌ | **MoE (256 experts × 8 active, ~3 B active params)** · vision · tools · upstream native loader via [vLLM PR #42521](https://github.com/vllm-project/vllm/pull/42521) · preview dual = **182/177 TPS at 16K** (no MTP, no TQ3, no Genesis) |
+| **[Gemma 4 26B-A4B](models/gemma-4-26b-a4b/)** ⭐ NEW v0.7.3 | Production via AWQ (Intel AutoRound INT4 blocked on Ampere) | 2× 3090 | vLLM ✅ (AWQ overlay) · llama.cpp ❌ | **MoE (128 experts × 8 active, ~4 B active params)** · vision · tools · AWQ dual = **139/139 TPS at 32K**, CV 0.2% / 0.0% |
 
 ¹ Single-card boot OOMs on Ampere 24 GB regardless of KV format. Single-card Gemma 4 is feasible on 32 GB+ GPUs (validated on RTX 5090 32 GB by [@apnar](https://github.com/noonghunna/club-3090/discussions/67#discussioncomment-16832042)).