[!IMPORTANT] Superseded by Qwen3.8 — use this instead: AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4-MIXED

Operator recipes (Spark Dynamic DFlash lattice, TP=2, RTX): AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED

This Qwen3.6-27B card stays online for history and existing downloads. For new work, move to the Qwen3.8 Ultimate Uncensored NVFP4-MIXED release. That line is far superior in capability and reaches substantially more of what the architecture can do, with a more refined mixed quantization methodology (NVFP4 + FP8 lattice) than these older Qwen3.6 exports.

Serve with the unified container: ghcr.io/aeon-7/aeon-vllm-ultimate:latest (Spark / GB10) or ghcr.io/aeon-7/aeon-vllm-ultimate-rtx:latest (RTX discrete).

Qwen3.6-27B-AEON-Ultimate-Uncensored-Text-NVFP4-MTP-XS

Deployment, operations & benchmarks → github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash

The GitHub repo is the source of truth for the production deployment guide, hardware-tuned docker-compose configs, full configuration reference, measured benchmarks, and AGENTS.md — an operator's manual that pre-empts common stale-documentation traps.

🏆 DGX Spark performance — current production (v3 image, 2026-04-29)

The XS body served with DFlash spec decode (not the MTP head) under the v3 image (ghcr.io/aeon-7/vllm-aeon-ultimate-dflash:qwen36-v3) is the highest-throughput config we've measured on Spark: 38.5 tok/s median, 71.3 tok/s peak thinking-on / 38.1 / 68.4 thinking-off. That's a +17–26 % lift across thinking modes vs the original -NVFP4 + old-DFlash + v2.1 production. See the GitHub Performance section for the four-config comparison table.

🙏 Reference recipe credit: The conv1d-preserved NVFP4 + MTP graft pipeline used to build this XS variant is based on sakamakismile's validated Qwen3.6-27B-NVFP4-MTP series (22K+ downloads). They worked out the modelopt config — including the strategic decision to quantize the GDN projection matmuls to NVFP4 while preserving linear_attn.conv1d at BF16 — and the MTP-head graft technique. We adapted the recipe to AEON-Ultimate's abliterated weights and ship both the conv1d-preserved-only XS variant (matching their footprint) and a heavier regular-MTP variant that additionally keeps the projections at BF16. Full credit for the underlying recipe → sakamakismile.

What "XS" means — and what it's not

This is the extra-small footprint sibling of -Text-NVFP4-MTP. XS is not "everything to FP4." It is a deliberate, principled split: the heavy GDN matmul projections drop to NVFP4 (where they're bandwidth-bound and FP4 wins big), while the SSM-critical linear_attn.conv1d kernel stays BF16 (where FP4 has documented stability problems on long-context recurrence).

This is a smart, strategic quantization — not a precision compromise. The conv1d preservation matters: the GatedDeltaNet recurrence depends on the 1D convolution behaving numerically like its training distribution, and FP4 quantization of conv1d has been observed to cause drift on long-context inference in community testing. By keeping conv1d BF16 while quantizing the projections (which are bandwidth-limited matmuls where FP4 is a clean win), we get the ~6 GB footprint reduction without sacrificing the part of the model that's actually fragile under quantization. This is the same principle modelopt's NVFP4_DEFAULT_CFG applies by default and the same recipe sakamakismile validated across his Qwen3.6-NVFP4-MTP series (22K+ downloads).

When to pick which:

• Pick the regular variant if you have ≥48 GB VRAM. Even the projection weights at BF16 give a small additional safety margin on long-context recurrence stability.
• Pick this XS variant if you have 24–32 GB VRAM (RTX 5090, single GPUs without headroom for full BF16 GDN). The conv1d preservation guarantees the SSM recurrence stays numerically stable; the ~6 GB savings buy meaningful KV-cache headroom on tight GPUs.

We ship both because we have the headroom on RTX PRO 6000 / B100/B200 to run the larger, more numerically-conservative version, and several users on tighter cards have asked for the smaller one. Neither variant quantizes linear_attn.conv1d — that would be a different (and not-recommended) variant we have explicitly chosen not to ship.

🆕 AEON vLLM Ultimate container (2026-06-04)

ghcr.io/aeon-7/aeon-vllm-ultimate:latest — vLLM 0.23.0 (= :2026-06-18-v0.23.0-dflashfix) + PR #44389 NVFP4 KV cache (~3× capacity) + DFlash + TurboQuant K8V4 + AEON sm_121a patches. Same recipe family as the -Multimodal-NVFP4-MTP-XS sibling which has been benchmarked end-to-end (production-style greedy + n_spec=15 by category: math/code peak ~45 tok/s, overall mean 34.7 tok/s; concurrent ×4 steady ~84 tok/s aggregate). This variant uses the same modelopt NVFP4 format, the same qwen3_5_mtp native head, and the same hybrid GDN+attention stack — it should serve identically with --quantization modelopt and either --speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":3}' (native MTP) or a DFlash drafter (recommended on Spark — see container README Recipe A).

The v3 image (ghcr.io/aeon-7/vllm-aeon-ultimate-dflash:qwen36-v3) remains the stable production target if you need FP8 KV + DFlash; in the new image DFlash requires --kv-cache-dtype auto (BF16). Full setup + 4-config bench comparison: container README.

Variants

What this is

The modelopt-format NVFP4 + MTP variant, text-only (vision tower stripped), with linear_attn projections fully quantized, of AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16 — the lossless abliteration of Qwen 3.6 27B (KL 0.000492 vs base, 0/100 refusals, multimodal preserved, hybrid GDN-aware quantization).

Specifically:

• Body quantized to NVFP4 via nvidia-modelopt 0.43.0 with NVFP4_DEFAULT_CFG. modelopt format, served by vLLM through --quantization modelopt.
• Linear-attn / GatedDeltaNet projections quantized to NVFP4 (this is the XS difference). Only linear_attn.conv1d is kept BF16 (modelopt's default). The community has validated this approach on Qwen3.5/3.6-NVFP4 builds with 22K+ downloads on sakamakismile's reference recipes; we re-ran calibration on our abliterated weights and the model serves correctly.
• Vision tower stripped (333 visual keys removed, ~0.92 GB). Text-only build — no image / video input. language_model_only: true set in config.json.
• MTP head grafted from the base Qwen/Qwen3.6-27B checkpoint (15 tensors, BF16, bit-exact verified). Powers --speculative-config '{"method":"qwen3_5_mtp",...}' for self-speculative decoding without a separate drafter.

Why MTP

Multi-Token Prediction (MTP) lets the model predict multiple future tokens per forward pass via the trained mtp.* head, enabling speculative decoding without a separate drafter model. The acceptance rate is high because the drafter is the model itself — same architecture, same weights, same distribution.

Indicative published numbers (sakamakismile's reference recipe on RTX 5090):

• Single-stream short prompts at n=3: ~132 tok/s
• Single-stream long-form: ~105 tok/s
• 2-parallel aggregate (256K + KV FP8): ~189-207 tok/s
• Mean acceptance length: ~3.0-4.0 (compared to DFlash chains of ~2.0-2.3)

Validated benchmarks of the AEON-Ultimate XS variant land in the GitHub repo once measured.

🎯 When to pick this variant — measured hardware routing

The right speculative-decode method depends on memory architecture:

Full bench numbers: GitHub repo Performance section. | A100 / H100 (no native FP4) | BF16 |

Usage

vLLM serve

# One-time: pull this repo locally
hf download AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Text-NVFP4-MTP-XS \
  --local-dir ./aeon-ultimate-text-nvfp4-mtp-xs

# Serve
export VLLM_NVFP4_GEMM_BACKEND=flashinfer-cutlass
export VLLM_USE_FLASHINFER_MOE_FP4=0
export VLLM_USE_FLASHINFER_SAMPLER=1

vllm serve ./aeon-ultimate-text-nvfp4-mtp-xs \
&
  --mamba-cache-dtype float32 \
  --trust-remote-code \
  --max-model-len 262144 \
  --max-num-seqs 32 \
  --max-num-batched-tokens 16384 \
  --gpu-memory-utilization 0.94 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --enable-auto-tool-choice \
  --speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":3}'

num_speculative_tokens=3 is the canonical setting for qwen3_5_mtp. Higher values diverge the drafter further from the target distribution and acceptance falls.

Configuration notes

• --quantization modelopt is required (not compressed-tensors — different format).
• --speculative-config '{"method":"qwen3_5_mtp", ...}' activates the grafted MTP head as the spec-decode drafter. No external drafter download needed — the head is in the safetensors of this repo.
• --gpu-memory-utilization 0.94 is the validated cap on RTX PRO 6000; on RTX 5090's tighter 32 GB you'll want 0.92 and a smaller --max-model-len (try 65536 first).

Quantization recipe

• Tool: nvidia-modelopt 0.43.0 with NVFP4_DEFAULT_CFG
• Loader: Qwen3_5ForConditionalGeneration.from_pretrained (multimodal-preserved class — vision stripped post-export)
• Calibration: neuralmagic/calibration LLM split, 20 samples × 8192 tokens
• Excluded from quantization (kept BF16) — XS variant differences from the regular variant in bold:
  • lm_head, proj_out.*, *router*, *mlp.gate.* (NVFP4_DEFAULT_CFG)
  • *linear_attn.conv1d*, *mixer.conv1d* *(NVFP4_DEFAULT_CFG default — kept BF16 because FP4 quantization of the SSM 1D convolution causes drift on long-context recurrence; this is the recurrence-critical kernel of the GatedDeltaNet block. Both regular and XS variants preserve this.)*
  • *linear_attn* is NOT broadly excluded (XS difference — the projection matmuls in_proj_qkv, in_proj_z, in_proj_a/b, out_proj get NVFP4-quantized; saves ~8 GB; FP4 is a clean win on bandwidth-bound matmuls)
  • *visual* (excluded during quant; vision tower then stripped post-export)
  • *mtp* (MTP head preservation)
  • *output_layer*, output.*
• MTP graft: 15 tensors copied bf16 from Qwen/Qwen3.6-27B after modelopt export
• Vision strip: post-export, all model.visual.* keys removed; config.json patched with language_model_only: true
• Pipeline: lna-lab/GGUF-to-NVFP4-SM120 reference recipe, adapted for AEON-Ultimate-BF16 input + separate MTP source

Provenance & credits

• BF16 source: AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16. See that card for the full abliteration pipeline.
• MTP graft technique: lna-lab/GGUF-to-NVFP4-SM120 (docs/MTP_GRAFT_RECIPE.md)
• Reference benchmark recipes: sakamakismile/Qwen3.6-27B-Text-NVFP4-MTP
• Quantization: NVIDIA TensorRT Model Optimizer (nvidia-modelopt 0.43.0)
• Base: Alibaba Qwen team — Qwen/Qwen3.6-27B

License + responsibility

Apache 2.0, inherited from Qwen/Qwen3.6-27B. This is an uncensored model. Read the full User Responsibility & Arbitration Clause on the BF16 source card before deploying. Summary: you implement downstream safety layers (input validation, output filtering, content moderation, audit logging, rate limiting, access controls, human-in-the-loop for high-risk workflows). The model has no opinions of its own — you supply the opinions, the judgment, and the ethics.

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