JiRackDeltaNet_27b
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
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Deep dive
Notes, sources, and the full write-up
Frequently asked
Quick answers to common questions
How much VRAM does JiRackDeltaNet_27b need?
JiRackDeltaNet_27b with 27.3B parameters needs approximately 16 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is JiRackDeltaNet_27b better than other CMSManhattan models?
JiRackDeltaNet_27b has 27.3B parameters with 262,144 context - a strong choice for general use.
What license is JiRackDeltaNet_27b under?
JiRackDeltaNet_27b is released under the mit license, making it suitable for most commercial and personal projects.
What hardware runs JiRackDeltaNet_27b well?
With 27.3B parameters, JiRackDeltaNet_27b requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for JiRackDeltaNet_27b?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~19 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can JiRackDeltaNet_27b's context window handle?
JiRackDeltaNet_27b supports a 262,144-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with JiRackDeltaNet_27b?
JiRackDeltaNet_27b competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec