Qwen3.6-27B-Text-NVFP4-MTP-GGUF

Qwen3.6-27B-Text-NVFP4-MTP (GGUF)

NVFP4-quantized, text-only GGUF build of Qwen3.6-27B for llama.cpp on NVIDIA DGX Spark (GB10, SM121).

Role in the .init Stack

This is the primary coding and reasoning model for the .init AI engineering platform. It's exposed as the .INIT/Pro alias in LiteLLM and is the default model for day-to-day AI-augmented software engineering on DGX Spark.

Why This Model for DGX Spark

• ~14 GB GGUF file → ~58 GB in GPU memory, leaving 50%+ of 128 GB free
• ~40 tok/s with MTP speculative decoding — fast enough for interactive coding
• NVFP4 quantization — NVIDIA's format works on SM121 without server-class TMEM hardware
• llama.cpp only — no vLLM dependency (DGX Spark lacks the Tensor Memory and driver version vLLM requires)

Optimal Settings for the .init Stack

These are the settings used in docker-compose.interface.yml:

llama-server \
  -m /models/model.gguf \
  -a qwen3.6_27b \
  --jinja --chat-template-file /workspace/chat_template.jinja \
  --reasoning on --reasoning-format deepseek --reasoning-budget 8192 \
  --min-p 0.05 \
  --spec-type draft-mtp,ngram-mod --spec-draft-n-max 2 \
  --spec-draft-p-min 0.88 --spec-draft-ngl 99 \
  -ctk f16 -ctv f16 -ngl all -fa on -sm none -fit off \
  -c 131072 -b 2048 -ub 512 \
  --parallel 1 --cont-batching --cache-prompt --swa-full \
  -t 8 -tb 8 --mlock \
  --port 8080 --host 0.0.0.0 --metrics --timeout 120

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Why This Exists

• Inspired by sakamakismile/Qwen3.6-27B-Text-NVFP4-MTP — NVFP4 quantization + MTP restoration recipe
• Goal: build a GGUF-compatible NVFP4 image for use with llama.cpp on DGX Spark

What Changed

• Vision tower stripped (text-only)
• Quantized to NVFP4 via nvidia-modelopt (group_size=16)
• MTP head preserved in BF16 for speculative decoding
• Calibrated on neuralmagic/calibration (20 samples)

Why llama.cpp + ModelOpt NVFP4 (and not vLLM)

The vLLM Problem on DGX Spark

Deploying NVFP4 via vLLM on DGX Spark (SM120/SM121) hits multiple blockers:

1. Missing TMEM hardware — DGX Spark's edge-tier Blackwell chip lacks the 256 KB Tensor Memory (TMEM) found in datacenter SM100. NVFP4's block-packed layout cannot take the hardware-accelerated fast path, wasting ~7 GB VRAM and losing ~16% throughput vs. AWQ/FP8.
2. Unoptimized kernels — vLLM's NVFP4 kernels for SM120 fall back to slow software paths, underperforming established 4-bit formats.
3. MTP shape mismatch — Qwen3.6's speculative decoding head fails to inherit NVFP4's quantization layout in vLLM, causing batch initialization errors or 0% acceptance rates.
4. Illegal instruction crashes — vLLM may invoke server-class Cutlass/FlashInfer backends incompatible with Spark's edge silicon.
5. Driver lock — vLLM's latest NVFP4 support requires driver 595.58+, but DGX Spark ships with 580.x. Forcing an upgrade can break the unified memory fabric.

Why This Stack Works

Demo

<a href="https://www.youtube.com/watch?v=Nsyi_n7PpuQ" target="_blank"><img src="https://img.youtube.com/vi/Nsyi_n7PpuQ/maxresdefault.jpg" alt="Watch the video" width="100%"></a>

Click the thumbnail above to play the demo recording on YouTube.

Real-time capture: VS Code Chat + llama.cpp interface on a single DGX Spark node.

Performance

40 tok/s achieved when:

• Single DGX Spark node (GB10, SM121, 128 GB unified memory)
• Speculative decoding enabled (--spec-type draft-mtp,ngram-mod, --spec-draft-n-max 2)
• Model fully resident on GPU (-ngl all, --mlock)
• Short-to-medium context (< 8K tokens in prompt)
• 8 threads, flash attention on, F16 KV cache

Optimal Settings for Long Context

llama-server \
  -m qwen3.6-27b-text-nvfp4-mtp.gguf \
  -a qwen3.6_27b \
  --jinja \
  --chat-template-file /workspace/chat_template.jinja \
  --reasoning on \
  --reasoning-format deepseek \
  --reasoning-budget 8192 \
  --min-p 0.05 \
  --spec-type draft-mtp,ngram-mod \
  --spec-draft-n-max 2 \
  --spec-draft-p-min 0.88 \
  --spec-draft-ngl 99 \
  -ctk f16 -ctv f16 \
  -ngl all \
  -fa on \
  -sm none \
  -fit off \
  -c 131072 \
  -b 2048 \
  -ub 512 \
  --parallel 1 \
  --cont-batching \
  --cache-prompt \
  --swa-full \
  -t 8 -tb 8 \
  --mlock \
  --port 8080 \
  --host 0.0.0.0 \
  --metrics \
  --timeout 120

Key Settings Explained

When to Use Long Context Settings

• Codebase analysis: scanning entire repositories (50K–128K tokens)
• Document reasoning: legal/technical documents with cross-reference needs
• Extended conversations: multi-turn sessions accumulating context
• Not needed for: chat, quick Q&A, or prompts < 8K tokens (use simpler settings for max throughput)

Usage

# Quick start
llama-server -m qwen3.6-27b-text-nvfp4-mtp.gguf --port 8080

License

Apache 2.0