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A fast and efficient 27B model optimized for CPU inference. Built on a Qwen3.8-style DeltaNet architecture (hybrid attention + SSM), with an updated tokenizer that includes Routing, Media, Vision, Sound, Tool call, and Robotics tags. Ready-to-run GGUF quantizations, and native Ollama support with reasoning disabled by default for fast, direct responses.
<think> blocks).cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning — https://ollama.com/cmsmanhattanollama run cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning --think=false| Tag | Quant | Size | Approx. RAM | Description |
|---|---|---|---|---|
cmsmanhattan/jirack-deltanet-27b-cpu:latest |
Full | ~55 GB | ~56–64 GB | Full precision reference |
cmsmanhattan/jirack_deltanet_27b-cpu-q4:latest |
Q4_K_M | ~16.8 GB | ~18–24 GB | Recommended balance |
cmsmanhattan/jirack-deltanet-27b-cpu-q3:latest |
Q3_K_M | ~13.9 GB | ~15–20 GB | Good quality / size trade-off |
cmsmanhattan/jirack-deltanet-27b-cpu-q2:latest |
Q2_K | ~11.2 GB | ~12–17 GB | Maximum compression |
ollama pull cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning
ollama run cmsmanhattan/JiRackDeltaNet_27b-q4-reasoning "What is the capital of France?"
Q8/int8
docker run -d \
--name jirack_deltanet_27b \
-p 7869:7869 \
--cpus=16 \
-e THREADS=16 \
-e THREADS_BATCH=16 \
--restart unless-stopped \
cmsmanhattan/jirack_deltanet_27b-cpu-q8:latest
Q6
docker run -d \
--name jirack_deltanet_27b \
-p 7869:7869 \
--cpus=16 \
-e THREADS=16 \
-e THREADS_BATCH=16 \
--restart unless-stopped \
cmsmanhattan/jirack_deltanet_27b-cpu-q6:latest
Default CPU (Q4/int4 recommended)
docker run -d \
--name jirack_deltanet_27b \
-p 7869:7869 \
--restart unless-stopped \
cmsmanhattan/jirack_deltanet_27b-cpu-q4:latest
Q3
docker run -d \
--name jirack_deltanet_27b \
-p 7869:7869 \
--cpus=16 \
-e THREADS=16 \
-e THREADS_BATCH=16 \
--restart unless-stopped \
cmsmanhattan/jirack_deltanet_27b-cpu-q3:latest
Q2 (lowest memory)
docker run -d \
--name jirack_deltanet_27b \
-p 7869:7869 \
--cpus=16 \
-e THREADS=16 \
-e THREADS_BATCH=16 \
--restart unless-stopped \
cmsmanhattan/jirack_deltanet_27b-cpu-q2:latest
Full precision
docker run -d \
--name jirack_deltanet_27b \
-p 7869:7869 \
--cpus=16 \
-e THREADS=16 \
-e THREADS_BATCH=16 \
--restart unless-stopped \
cmsmanhattan/jirack-deltanet-27b-cpu:latest
Multi CPU
docker run -d \
--name jirack_deltanet_27b \
-p 7869:7869 \
--cpus=16 \
-e THREADS=16 \
-e THREADS_BATCH=16 \
--restart unless-stopped \
--memory=32g \
--cpus=8 \
cmsmanhattan/jirack-deltanet-27b-q4:latest
services:
jirack:
image: cmsmanhattan/jirack_deltanet_27b-cpu-q4:latest
container_name: jirack_deltanet_27b
ports:
- "7869:7869"
volumes:
- .:/app
- ./web:/app/web
environment:
- MAX_TOKENS=2048
- TEMPERATURE=0.7
- TOP_P=0.8
- TOP_K=20
- DEFAULT_STREAM=False
- INTRA_THREADS=4
- USE_ENV_ALLOCATOR=1
- THREADS=16
- THREADS_BATCH=16
deploy:
resources:
limits:
memory: 32g
Once the container is running, open your browser and navigate to:
http://localhost:7869
This opens the JiRack UI — a clean web interface.
The listening port can be easily modified directly from the Settings panel within the JiRack UI.
Model weights are released under the MIT License — free to use, modify, and distribute for any purpose, including commercial. No royalties, no per-user fees, no subscription.
The Docker image with UI and the pre-built Ollama quantizations are separate paid products. If you prefer to build your own secure deployment — take the weights, assemble your own stack, and you're done.
The JiRack DeltaNet 27B model for Docker and Ollama is provided under a commercial license ($12 per user per year).
All JiRack UI clients are provided under a commercial license.
However, the UI clients can be used for free when running together with the official JiRack Docker containers, as long as they are not redistributed separately.
For commercial licensing, cluster deployment, or enterprise use of JiRack models, please contact us.
| Use Case | CPU | RAM | Recommended Quant | Expected Speed | Recommendation |
|---|---|---|---|---|---|
| Recommended | Ryzen 9 / Intel i9 / Xeon | 24–32 GB | Q4_K_M | Good interactive | Best choice |
| High Performance | High-core server CPU | 48 GB+ | Full / Q4 | Excellent | Excellent |
| Low Memory | Modern 12+ core CPU | 16–24 GB | Q3_K_M or Q2_K | Usable | Acceptable |
| Edge / Minimal | Strong workstation CPU | 16 GB | Q2_K | Acceptable | Budget option |
Even though the quantized 27B models are relatively compact for their size, we recommend the following for best experience:
Reasons for extra headroom:
Minimum recommended (Q4): 18 GB system RAM Ideal: 24–32 GB system RAM
I added the default model in full precision. This serves as the base for quantization, allowing us to find the optimal balance between model size and performance.
qwen35 in GGUF metadata)<think> blocks default to closed; native support on OllamaJiRack DeltaNet 27B is built on the Qwen3.5/Qwen3.8-style DeltaNet architecture. The table below reproduces the published base-model benchmark results from Qwen/Qwen3.8-27B for reference — these reflect the upstream base model's capabilities, not JiRack-specific fine-tuning or quantization results.
| Qwen3.8-27B | Qwen3.6-27B | Qwen3.7-Plus | Muse Glimmer-30B | Opus4.6 Max | |
|---|---|---|---|---|---|
| Coding | |||||
| Agentic terminal coding — Terminal Bench 2.1 (Terminus) | 73.0 | 63.4 | 64.0 | 51.7 | 78.2 |
| Agentic coding — SWE-bench Pro | 61.7 | 53.5 | 57.6 | 51.2 | 53.4 |
| Repo-level code generation — NL2Repo-Bench | 42.3 | 36.2 | 41.1 | -- | 47.6 |
| Agentic coding — DeepSWE 1.1 | 42.2 | 13.3 | 14.2 | -- | -- |
| Software engineering — QwenSWEBench | 79.0 | 49.3 | 59.2 | -- | 63.8 |
| Agent | |||||
| Long-horizon office work — CoWorkBench | 70.7 | 61.0 | 65.1 | -- | 68.2 |
| Professional job tasks — JobBench | 33.4 | 21.8 | 27.6 | -- | -- |
| Frontier agentic tasks — Agents' Last Exam (Pass@1/Score) | 20.4 / 42.9 | 10.6 / 27.3 | 13.2 / 33.6 | -- | -- |
| General | |||||
| Instruction following — IFBench | 79.5 | 69.1 | 79.1 | 77.0 | 62.5 |
| Scientific reasoning — GPQA Diamond | 89.2 | 87.8 | 90.3 | 83.5 | 91.3 |
| Multidisciplinary reasoning — HLE | 30.8 | 24.0 | 34.7 | 22.0 | 40.0 |
| Competitive coding — LiveCodeBench v6 | 90.3 | 83.9 | 89.6 | -- | 88.8 |
| Qwen3.8-27B | Qwen3.6-27B | Qwen3.7-Plus | Muse Glimmer-30B | Opus4.6 Max | |
|---|---|---|---|---|---|
| Agentic Multimodal Intelligence | |||||
| Computer use — OSWorld-Verified | 84.3 | 63.9 | 73.3 | 65.9 | 72.7 |
| Browser use — WebArena-Verified | 64.8 | 48.8 | 55.3 | -- | -- |
| Mobile use — AndroidWorld | 81.9 | 70.3 | 81.0 | -- | 62.0 |
| Application recreation — RecreationBench | 47.1 | 29.8 | 30.2 | -- | -- |
| Multimodal tool use — ClawEval-MM (Pass@3/Avg) | 57.4 / 56.9 | 42.6 / 50.4 | 57.4 / 60.1 | -- | 52.5 / 54.7 |
| Multimodal SWE — SWE-MM | 38.6 | 25.7 | 30.0 | -- | 27.1 |
| Visual web development — Vision2Web | 62.9 | 45.0 | 42.1 | -- | -- |
| General Multimodal Intelligence | |||||
| Visual math — MathVision (w/o CI / w/ CI) | 90.0 / 94.6 | 85.1 / -- | 90.3 / -- | -- | 65.5 / -- |
| General visual reasoning — BabyVision (w/o CI / w/ CI) | 65.7 / 85.6 | 28.9 / -- | 64.7 / 70.4 | -- | 12.6 / -- |
| Scientific chart analysis — CharXiv (RQ) (w/o CI / w/ CI) | 83.7 / 90.2 | 78.4 / -- | 85.8 / 85.9 | 78.8 | 66.0 / -- |
| Document intelligence — OmniDocBench 1.5 | 91.1 | 89.4 | 91.4 | 75.8 | 86.6 |
| Real-world perception — RealWorldQA | 85.9 | 84.1 | 86.9 | -- | 73.9 |
| Embodied intelligence — ERQA | 65.5 | 62.5 | 69.8 | -- | 40.8 |
Source: Qwen/Qwen3.8-27B model card. Best result in each row is bolded. Empty cells (--) indicate results not yet available. See the source card for full evaluation methodology and footnotes.
For joint venture opportunities, hardware integration, or licensing inquiries:
MIT License