gemma-2-27b-it GGUF size and VRAM requirements
gemma-2-27b-it is a large language model from the Gemma family developed by Google, featuring 27.23 billion parameters. It is designed for instruction-following tasks and is licensed under the Gemma terms, allowing use in applications requiring advanced natural language understanding and generation. The model is derived from the Hugging Face repository and optimized for deployment via tools like llama.cpp.
Under the hood it uses 46 transformer layers, a hidden size of 4,608, 32 attention heads. It uses grouped-query attention (32 query heads sharing 16 key/value heads), which already trims KV-cache memory compared with full multi-head attention.
To run bartowski/gemma-2-27b-it-GGUF locally at a 4,096-token context, its quantized versions need between 10.24 GB (IQ2_XS, lowest quality) and 103.85 GB (F32, highest quality) of memory, weights plus KV cache and a system margin included. Context length drives that memory directly: at 4,096 tokens the KV cache for gemma-2-27b-it-GGUF is about 1.62 GB, rising to roughly 3.23 GB at its full 8,192-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is Q6_K_L, needing about 23.49 GB. That means bartowski/gemma-2-27b-it-GGUF fits entirely in the VRAM of a 12 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for bartowski/gemma-2-27b-it-GGUF include IQ2_XS, IQ2_S, IQ2_M, Q2_K, Q2_K_L, IQ3_XXS, IQ3_XS, Q3_K_S, IQ3_M, Q3_K_M, Q3_K_L, Q3_K_XL, IQ4_XS, Q4_K_S, Q4_K_M, Q4_K_L, Q5_K_S, Q5_K_M, Q5_K_L, Q6_K, Q6_K_L, Q8_0, Q8_0_L, F32. The model supports a native context length of up to 8,192 tokens; a longer context grows the KV cache and the memory needed.
Recommended Use Cases & Local Setup
Ideal for: General knowledge, document summarization, email drafting, language translation, and multi-turn conversational chat.
Simple one-line CLI installation running silently in the background
Polished desktop client with one-click model downloads and GPU offloading
Open-source privacy-focused desktop assistant
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ2_XS | 2.47 | Very low | 7.82 GB | 1.62 GB | 10.24 GB | 6.4 t/s | Offload |
| IQ2_S | 2.54 | Very low | 8.06 GB | 1.62 GB | 10.48 GB | 6.2 t/s | Offload |
| IQ2_M | 2.76 | Low | 8.75 GB | 1.62 GB | 11.17 GB | 5.7 t/s | Offload |
| Q2_K | 3.07 | Low | 9.73 GB | 1.62 GB | 12.15 GB | 5.1 t/s | Offload |
| Q2_K_L | 3.15 | Low | 10.0 GB | 1.62 GB | 12.42 GB | 5.0 t/s | Offload |
| IQ3_XXS | 3.16 | Low | 10.01 GB | 1.62 GB | 12.43 GB | 5.0 t/s | Offload |
| IQ3_XS | 3.39 | Fair | 10.76 GB | 1.62 GB | 13.17 GB | 4.6 t/s | Offload |
| Q3_K_S | 3.58 | Fair | 11.33 GB | 1.62 GB | 13.75 GB | 4.4 t/s | Offload |
| IQ3_M | 3.66 | Fair | 11.6 GB | 1.62 GB | 14.02 GB | 4.3 t/s | Offload |
| Q3_K_M | 3.94 | Fair | 12.5 GB | 1.62 GB | 14.92 GB | 4.0 t/s | Offload |
| Q3_K_L | 4.27 | Good | 13.52 GB | 1.62 GB | 15.94 GB | 3.7 t/s | Offload |
| Q3_K_XL | 4.35 | Good | 13.79 GB | 1.62 GB | 16.21 GB | 3.6 t/s | Offload |
| IQ4_XS | 4.35 | Good | 13.8 GB | 1.62 GB | 16.21 GB | 3.6 t/s | Offload |
| Q4_K_S | 4.62 | Good | 14.66 GB | 1.62 GB | 17.08 GB | 3.4 t/s | Offload |
| Q4_K_M | 4.89 | Good | 15.5 GB | 1.62 GB | 17.92 GB | 3.2 t/s | Offload |
| Q4_K_L | 4.97 | Good | 15.77 GB | 1.62 GB | 18.19 GB | 3.2 t/s | Offload |
| Q5_K_S | 5.55 | Very good | 17.59 GB | 1.62 GB | 20.0 GB | 2.8 t/s | Offload |
| Q5_K_M | 5.7 | Very good | 18.08 GB | 1.62 GB | 20.49 GB | 2.8 t/s | Offload |
| Q5_K_L | 5.79 | Very good | 18.34 GB | 1.62 GB | 20.76 GB | 2.7 t/s | Offload |
| Q6_K | 6.57 | Excellent | 20.81 GB | 1.62 GB | 23.23 GB | 2.4 t/s | Offload |
| Q6_K_L | 6.65 | Excellent | 21.08 GB | 1.62 GB | 23.49 GB | 2.4 t/s | Offload |
| Q8_0 | 8.5 | Excellent | 26.95 GB | 1.62 GB | 29.37 GB | — | Insufficient |
| Q8_0_L | 8.83 | Excellent | 27.98 GB | 1.62 GB | 30.4 GB | — | Insufficient |
| F32 | 32.0 | Excellent | 101.43 GB | 1.62 GB | 103.85 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
What kind of model is bartowski/gemma-2-27b-it-GGUF?
bartowski/gemma-2-27b-it-GGUF is an instruction-tuned chat model with 27.23 billion parameters, based on the gemma2 architecture. It is released under the gemma license and distributed as GGUF files for local inference.
How much VRAM do you need to run bartowski/gemma-2-27b-it-GGUF?
You need about 11.17 GB of VRAM to run bartowski/gemma-2-27b-it-GGUF entirely on the GPU using the IQ2_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/gemma-2-27b-it-GGUF on an 8 GB GPU?
Partially. bartowski/gemma-2-27b-it-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q6_K_L), which runs but is slower.
Can I run bartowski/gemma-2-27b-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/gemma-2-27b-it-GGUF fully on the GPU using Q3_K_L (about 15.94 GB).
Can I run bartowski/gemma-2-27b-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/gemma-2-27b-it-GGUF fully on the GPU using Q6_K_L (about 23.49 GB).
What context length does bartowski/gemma-2-27b-it-GGUF support?
bartowski/gemma-2-27b-it-GGUF supports a native context length of up to 8,192 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for bartowski/gemma-2-27b-it-GGUF?
For bartowski/gemma-2-27b-it-GGUF, a strong default is Q4_K_M, which needs about 17.92 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.