Instructions to use nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF

SGLang

How to use nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/nilayparikh/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.

Property	Value
`.INIT/` alias	`.INIT/Pro`
Docker profile	`llama-qwen-3-6-27b`
Host port	`8000`
Use case	Coding, reasoning, chat — the daily driver
Context	128K tokens (codebase analysis, long documents)

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

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:

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.
Unoptimized kernels — vLLM's NVFP4 kernels for SM120 fall back to slow software paths, underperforming established 4-bit formats.
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.
Illegal instruction crashes — vLLM may invoke server-class Cutlass/FlashInfer backends incompatible with Spark's edge silicon.
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

Component	Why
llama.cpp	No TMEM dependency — GGUF loads weights directly into GPU memory without layout transformations that require server-class hardware
ModelOpt NVFP4	NVIDIA's own quantizer produces compact weights (~14 GB for 27B) with native BF16 MTP preservation
MTP + n-gram	Dual speculative decoding path achieves ~40 tok/s on DGX Spark without vLLM's MTP bugs
~45% memory	Model uses ~58 GB of 128 GB — leaving 50%+ free for additional models alongside

Demo

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

Condition	Throughput	Notes
DGX Spark, short prompts	~40 tok/s	MTP n=2 + ngram speculative decoding, model fully on GPU
DGX Spark, long context (128K)	~25–35 tok/s	KV cache grows with context

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

Flag	Value	Why
`-c`	`131072`	128K context window for long documents
`--spec-type draft-mtp,ngram-mod`	MTP + n-gram hybrid	Dual speculative path for higher acceptance rate
`--spec-draft-n-max 2`	2 draft tokens	Matches MTP head depth
`--spec-draft-p-min 0.88`	88% acceptance threshold	Balanced speculation, fallback to n-gram
`--reasoning-budget 8192`	8192 tokens	Extended reasoning budget for complex tasks
`-ngl all`	All layers on GPU	No CPU offloading — DGX Spark has 128 GB
`-fa on`	Flash attention	O(n) memory for long context
`-ctk f16` / `-ctv f16`	F16 KV cache	Precision-critical for long context
`-b 2048` / `-ub 512`	Prefill 2048, decode 512	Balanced batch sizing for throughput
`--parallel 1`	1 concurrent sequence	Single sequence avoids memory pressure
`--cont-batching`	Continuous batching	Better GPU utilization under load
`--swa-full`	Full sliding window attention	Better long-range attention quality
`--mlock`	Lock in RAM	Prevents eviction during long generations

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

Downloads last month: 3,787

GGUF

Hardware compatibility

4-bit

Model tree for nilayparikh/Qwen3.6-27B-Text-NVFP4-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(419)

this model