The weights are unchanged from those upstream releases. This repository adds a reproducible llama.cpp launch profile and measured RTX 5090 results at a 200,704-token configured context window.

DSpark support is experimental. These results use llama.cpp PR #26275, commit f806441edb5006fdacb07df42445f337476dd169.

Files

File	Purpose	Size
`Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf`	verifier/target model	21.17 GB
`Qwen3.6-35B-A3B-DSPARK.gguf`	DSpark draft model; not standalone	1.04 GB
`launch-dspark.sh`	tested 200K launch profile	—
`prime-agent-models.json`	Prime Agent custom-provider template	—
`benchmark-results.json`	machine-readable measurements	—

RTX 5090 generation benchmark

Measured locally on 2026-08-07. Each result is an end-to-end wall-clock OpenAI Chat Completions request generating 512 tokens. Values are three independent runs after loading the full 200,704-token context configuration.

Configuration	Runs (tok/s)	Mean	Best	VRAM	Relative to baseline
No draft	270.79, 276.17, 279.65	275.54 tok/s	279.65	23,266 MiB	1.000x
DSpark, max draft 3	306.16, 312.70, 318.27	312.38 tok/s	318.27	27,944 MiB	1.134x
DSpark, max draft 5	233.74, 256.70, 260.33	250.26 tok/s	260.33	28,070 MiB	0.908x
DSpark, max draft 7	207.63, 224.25, 226.90	219.59 tok/s	226.90	28,196 MiB	0.797x

--spec-draft-n-max 3 is the clear choice on this target. In the 3 × 512-token coding run, 337 of 520 drafted tokens were accepted (64.81%). A separate short generation accepted 41 of 45 drafted tokens (91.11%). Acceptance depends on the prompt and output distribution.

Test machine

Component	Value
GPU	NVIDIA GeForce RTX 5090, 32,607 MiB
Driver / power limit	595.84 / 575 W
OS / kernel	Ubuntu 26.04 LTS / Linux 7.0.0-28-generic x86_64
Target quantization	Q4_K_M, 21.17 GB
Draft	BF16 GGUF, 1.04 GB
Configured context	200,704 tokens
KV cache	Q8_0 K and V for both target and draft
Batch / microbatch	2,048 / 512
Parallel slots	1
Flash attention	enabled

These are single-stream decode measurements, not prompt-processing throughput or multi-user aggregate throughput. The first request can include warm-up overhead, which is intentionally retained in the mean.

Quality benchmarks

DSpark is target-verifying speculative decoding: accepted draft tokens are verified by the target, so it accelerates inference without substituting the draft model's predictions for the target's decisions.

The upstream Aggressive model card does not publish LiveCodeBench v6 or SWE-bench Verified scores, and those suites were not rerun for this packaging work. No quality score is claimed here. Refer to the HauhauCS model card for the target's reported properties and safety behavior.

Run

Build the experimental branch with CUDA enabled:

bash

git clone --branch dspark-speculators https://github.com/wjinxu/llama.cpp.git
cmake -S llama.cpp -B llama.cpp/build \
  -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_CUDA_ARCHITECTURES=120a
cmake --build llama.cpp/build --config Release -j --target llama-server

Then, from this repository directory:

LLAMA_SERVER=/path/to/llama.cpp/build/bin/llama-server ./launch-dspark.sh

OpenAI-compatible API:

bash

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "qwen3.6-35b-a3b-aggressive-dspark",
    "messages": [{"role": "user", "content": "Write a Python LRU cache."}],
    "temperature": 0,
    "max_tokens": 512
  }'

For Prime Agent, copy prime-agent-models.json to ~/.prime/agent/models.json, change YOUR_SERVER_IP, then select provider qwen36-dspark and model qwen3.6-35b-a3b-aggressive-dspark.

Attribution and limitations

Target and quantization: HauhauCS
Base model: Qwen/Qwen3.6-35B-A3B
Draft conversion: williamliao/Qwen3.6-35B-A3B-DSPARK-GGUF
Draft lineage: Koopah/Qwen3.6-35B-A3B-NVFP4-DSPARK
DSpark research/runtime: DeepSpec and llama.cpp PR #26275

This target is explicitly uncensored. Review the upstream model card and use it responsibly. This bundle is text-only because it does not include the optional vision projector. See THIRD_PARTY_NOTICES.md and LICENSE.

Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF

Qwen3.6 35B A3B Aggressive Q4_K_M + DSpark GGUF

Files

RTX 5090 generation benchmark

Test machine

Quality benchmarks

Run

Attribution and limitations

Danny-Dasilva/
Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF