# Inference engines for Qwen3.6-27B — comparison + quick recipes

This repo's main path is **vLLM** because it has the deepest support for Qwen3-Next features (vision, MTP, TurboQuant, full OpenAI API parity). But the model also runs on **llama.cpp** and **SGLang** with different trade-offs. This page compares the three; per-engine pages have setup instructions.

> 🔁 **Coming from the README's Quick start?** It already shipped you the vLLM path. Skim this comparison to see what the alternatives look like, then pick a per-engine page if you want to try one.

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## At a glance

| Engine | Status on this stack | Per-stream TPS (1× 3090) | Max ctx (1× 3090) | Vision | Tool calls | Spec-decode | OpenAI API parity |
|---|---|---|---|---|---|---|---|
| **[vLLM](VLLM.md)** ⭐ | **Validated, production-grade** (this repo) | 50-53 narr / 66-70 code | 48K default · 75K IDE-agent · **198K vision · 214K text-only** | ✅ | ✅ | ✅ MTP n=3 | ✅ Full |
| **[llama.cpp](LLAMA_CPP.md)** | Works mainline (MTP merged [PR #22673](https://github.com/ggml-org/llama.cpp/pull/22673), 2026-05-16) | 35-60 (varies by quant + KV type) | **262K** (Q4_K_M + q4_0 KV) | ✅ (via mmproj) | ⚠️ Limited (no auto-tool-choice in server) | ✅ MTP on mainline + DFlash N=5 in [Luce fork](https://github.com/Luce-Org/lucebox-hub) | ⚠️ Partial |
| **[ik_llama.cpp](IK_LLAMA.md)** *advanced quants* | **Shipped — advanced-quant track** | ~50 narr / ~58 code (ties llama.cpp at matched power; edge is **~0.5–0.8 GB leaner VRAM**) | **262K** (IQ4_KS + q4_0 KV) | ✅ (mmproj) | ✅ (template + parser) | ✅ MTP n=2 | ⚠️ Partial (llama.cpp-class) |
| **[SGLang](../../models/qwen3.6-27b/sglang/README.md)** | **Re-test pending** (May 2026). Historical block partially out-of-date — DFlash + MTP have landed natively on SGLang mainline; Marlin pad-sub-tile-n fix status unknown. See sglang README for re-test plan. | n/a (untested) | n/a | ✅ | ✅ | ✅ DFlash + MTP native upstream (untested here) | ✅ Full |

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## Pros / cons matrix

### vLLM ⭐

**Pros:**
- Deepest Qwen3-Next feature support upstream
- TurboQuant 3-bit KV cache (lets us reach 198K + vision or 214K text-only on a single 3090; 262K dual-card)
- MTP speculative decoding works out of the box
- Genesis patch ecosystem (Sandermage's tree fixes many compatibility edges)
- Full OpenAI API parity (chat, vision, tools, streaming, reasoning, structured output)
- Active development — bugs we hit get triaged within days

**Cons:**
- Heavyweight — Docker image is ~9 GB
- Longer cold-start (~2 min for compile + cudagraph capture)
- Sensitive to upstream API drift across nightly versions (we pin to a specific nightly SHA — `7a1eb8ac` = `0.20.1rc1.dev16` since 2026-05-01 — to avoid this)
- Frontier features sometimes ship with bugs we have to patch around (the whole reason this repo exists)

**When to pick:** Production / serious local work / anything that needs the full feature set.

---

### llama.cpp

**Pros:**
- Lightweight — single binary, ~50 MB
- Fastest cold-start (~30 sec)
- Lowest VRAM overhead (no inference framework taxes)
- GGUF support for many quant formats (Q4_K_M, Q5_K_S, IQ4_XS, etc.)
- Works on AMD + Intel + Apple Silicon (vLLM is NVIDIA-only)
- Active community, lots of distros / wrappers (Ollama, LM Studio, LocalAI, etc.)

**Cons:**
- Qwen3-Next family support is a moving target — needs the right binary build
- Server feature parity behind vLLM (no auto-tool-choice in upstream `server`; need wrapper)
- DFlash spec-decode requires a fork ([Luce's llama-cpp-dflash](https://github.com/Luce-Org/lucebox-hub))
- Concurrent serving is single-threaded by default (the server forks per request — sluggish under concurrent load)
- No TurboQuant equivalent → max usable context is much lower (~64K with Q4_K_M on 24 GB)

**When to pick:** Quick experiments, embedded use, non-NVIDIA hardware, when you want simplicity over feature completeness.

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### SGLang

**Pros:**
- Designed for high-throughput serving — RadixAttention prefix sharing, structured-output-aware scheduling
- Often beats vLLM by 10-30% on multi-tenant throughput when both work
- First-class OpenAI API
- Good support for batched structured output (constraint decoding)

**Cons:**
- **Last attempt (early 2026): blocked by Marlin pad-sub-tile-n bug** (same kernel-line issue as our [vllm#40361](https://github.com/vllm-project/vllm/pull/40361)). Whether SGLang has picked up the fix on current main is **unverified** — needs re-test. Bug is INT4-specific; FP16/bf16 weights work fine.
- EAGLE spec-decode was separately blocked by the DeltaNet/GDN hybrid layer not supporting KV rollback. Status on current SGLang main: also unverified, but DFlash and MTP support has landed natively (per z-lab + LMSYS) so spec-decode on Qwen3-Next is at least *intended* to work.
- TurboQuant 3-bit KV is WIP on SGLang ([Issue #21618](https://github.com/sgl-project/sglang/issues/21618)) — not yet merged.
- Smaller community than vLLM; fewer eyes on Qwen3-Next bugs.

**When to pick:** if a contributor with current SGLang main re-runs the boot test on Qwen3.6-27B-INT4 + TP=2 and it works. See the [re-test plan in the per-engine page](../../models/qwen3.6-27b/sglang/README.md). For an FP16/bf16 weight variant, it likely works today.

**Re-test plan (pending):**
1. Pull latest SGLang container/main → smoke-boot at TP=2 + AutoRound INT4 + no spec-decode + fp8/q4 KV (NOT TurboQuant — WIP upstream)
2. If boot clean: validate verify-stress 7/7
3. Add DFlash spec-decode (preferred over MTP — higher acceptance rate, z-lab maintains the SGLang integration; Qwen3.6-27B draft at [`z-lab/Qwen3.6-27B-DFlash`](https://huggingface.co/z-lab/Qwen3.6-27B-DFlash))
4. If competitive vs vLLM `dual-dflash.yml` (78-82 narr / 125-127 code TPS) → ship as a polished compose

Full plan in [models/qwen3.6-27b/sglang/README.md](../../models/qwen3.6-27b/sglang/README.md#re-test-plan).

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## How to choose

| Your priority | Pick | Why |
|---|---|---|
| **Full feature set, MTP spec-decode, OpenAI API parity** | vLLM + Lorbus AutoRound | This repo's path. 51-70 TPS depending on workload, all features, prefill-safe at 48K default. |
| **Maximum context (262K) on one 3090** | llama.cpp + UD-Q3_K_XL or Q4_K_M + q4_0 KV | Smaller quants leave 8-10 GB headroom for KV at 262K. ~35-45 TPS sustained. |
| **Leanest VRAM / best quality-per-bit GGUF on one 3090** | ik_llama + IQ4_KS + MTP | Fork-exclusive IQK imatrix quant, 262K ctx. Ties llama.cpp on TPS + quality at matched power; its edge is a ~0.5–0.8 GB leaner footprint (best when VRAM-tight). Two-stage ngram+MTP for code workloads. See [IK_LLAMA.md](IK_LLAMA.md). |
| **Best concurrent throughput on dual 3090** | vLLM TP=2 + Turbo (TQ3) | 4 streams at full 262K, ~200 TPS aggregate. See [`dual-turbo.yml`](../models/qwen3.6-27b/vllm/compose/dual/autoround-int4/turbo.yml) in this repo. |
| **Non-NVIDIA hardware (AMD / Intel / Apple)** | llama.cpp | Only engine with cross-platform support. |
| **Lightest setup, fastest cold start** | llama.cpp | Single binary, ~30s cold start. Good for embedded use, quick experiments. |
| **High-throughput multi-tenant serving** | SGLang (re-test pending — DFlash + MTP native upstream as of May 2026; Marlin INT4 fix verification needed) | RadixAttention prefix sharing wins at scale. Re-test plan in [sglang/README.md](../../models/qwen3.6-27b/sglang/README.md). |

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## Quant choice (orthogonal to engine choice)

The model itself comes in several quant formats. Engine-quant compatibility (full primer: **[QUANTIZATION.md](../QUANTIZATION.md)**):

| Quant | Disk size | Engine fit | Notes |
|---|---|---|---|
| **AutoRound int4** ([Lorbus](https://huggingface.co/Lorbus/Qwen3.6-27B-int4-AutoRound)) | ~18-19 GB | vLLM ✅ · llama.cpp ❌ · SGLang (when unblocked) | This repo's choice. W4A16, group_size=128, BF16 mtp.fc head. **Required** for vLLM's MTP spec-decode. |
| GPTQ int4 | ~16.5-17 GB | vLLM ✅ · llama.cpp ❌ · SGLang ✅ | Mature, broadly supported. Slightly smaller disk than AutoRound. |
| AWQ int4 | ~16-17 GB | vLLM ✅ · llama.cpp ❌ · SGLang ✅ | Strong baseline, compatible with Marlin kernels. |
| GGUF Q4_K_M | ~16.8 GB | llama.cpp ✅ · vLLM ⚠️ experimental · SGLang ❌ | The default GGUF mid-range quant. Strong quality, broad ecosystem (Ollama, LM Studio, etc). |
| GGUF UD-Q3_K_XL ([Unsloth](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF)) | **~14.5 GB** | llama.cpp ✅ | Smaller than 4-bit options. Quality cost is small on Qwen3.6 (quantization-friendly), buys substantial KV cache room. |
| GGUF Q3_K_M | ~13.6 GB | llama.cpp ✅ | More aggressive 3-bit; quality cost real but acceptable for many workloads. |
| **GGUF IQ4_KS (imatrix)** ⭐ ([ubergarm](https://huggingface.co/ubergarm/Qwen3.6-27B-GGUF)) | **~15.1 GB** | **[ik_llama.cpp](IK_LLAMA.md) only** · llama.cpp ❌ · vLLM ❌ | Best quality-per-bit GGUF (imatrix + kernels co-designed for IQK grids). Smaller than Q4_K_M → **262K single-card**. Fork-exclusive — see [QUANTIZATION.md](../QUANTIZATION.md). |

### AutoRound vs GPTQ vs AWQ (within vLLM)

All three are 4-bit weight-only quantization for vLLM. Differences:

| Aspect | AutoRound | GPTQ | AWQ |
|---|---|---|---|
| **Method** | Signed gradient descent jointly optimizing rounding + scaling | Layer-wise Hessian-based error minimization | Activation-aware salience scaling, then RTN |
| **Calibration set** | Small (~128-512 samples) | Larger (~1024-2048) | Small-medium |
| **Quantization time** | Minutes to ~1-2 hours for 27B | Slower for same model | Fast |
| **Accuracy at 4-bit** | Typically slightly best on hard reasoning (MMLU/GPQA/Math style) | Strong baseline; 0.5-2% behind AutoRound on average | Comparable to GPTQ; depends on tuning |
| **Ultra-low bits (3, 2)** | Strongest at <4 bit | Degrades faster below 4 bit | Middle of the pack |
| **Marlin kernel support** | ✅ (via the kernel-line fix in our [vllm#40361](https://github.com/vllm-project/vllm/pull/40361)) | ✅ (mature) | ✅ |
| **Ecosystem** | Newer, growing fast (Intel-maintained) | Most mature, broadest tool support | Strong vLLM/SGLang support |

**Why we picked AutoRound for this repo:** Lorbus's AutoRound quant ships `mtp.fc.weight` as BF16 (preserved at higher precision), which lets vLLM's `Qwen3_5MTP` loader actually load the head and run multi-token prediction at high acceptance rates (~80% per-position-1, AL ~3.5). GPTQ-quantized variants of the MTP head silently fail to load → 0% draft acceptance. So AutoRound isn't just "slightly better quality" here — it's the only path to working MTP spec-decode in vLLM today.

If MTP isn't a priority for your workload, GPTQ or AWQ are equally valid.

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## Per-engine pages

- **[VLLM.md](VLLM.md)** — current setup (what this repo ships). Brief recap + tuning levers.
- **[LLAMA_CPP.md](LLAMA_CPP.md)** — quick GGUF recipe, vision via mmproj, Luce DFlash fork pointer for spec-decode, gotchas around server feature parity.
- **[SGLANG.md](SGLANG.md)** — current blocked state, what would unblock, when to revisit. TBD recipe placeholder until either Marlin pad lands upstream or DeltaNet rollback lands.
- **[IK_LLAMA.md](IK_LLAMA.md)** ⭐ — the advanced-quant engine: fork-exclusive IQK imatrix quants (`IQ4_KS`), 262K single-card, MTP + two-stage ngram+MTP, `-khad` / `-vhad` / `--merge-qkv`, `--parallel-tool-calls`. Pairs with [QUANTIZATION.md](../QUANTIZATION.md).

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## See also

- [docs/INTERNALS.md](../INTERNALS.md) — why this repo picked vLLM specifically (the 9-probe forensics + upstream tracker)
- [docs/SINGLE_CARD.md](../SINGLE_CARD.md) and [docs/DUAL_CARD.md](../DUAL_CARD.md) — workload-specific configs by hardware count
- [LEARNINGS.md (parent stack)](https://github.com/noonghunna/qwen36-27b-single-3090/blob/master/docs/INTERNALS.md) — why vLLM, why these patches
