Phi-3.5-mini-instruct GGUF size and VRAM requirements
Phi-3.5-mini-instruct is a 3.82 billion parameter AI model licensed under MIT, part of the Phi-3.5 family developed by Microsoft. It is designed for natural language processing tasks, including instruction-following and text generation, and is intended for research and development use. The model is available for download and modification under the MIT license terms.
Under the hood it uses 32 transformer layers, a hidden size of 3,072, 32 attention heads.
To run bartowski/Phi-3.5-mini-instruct-GGUF locally at a 4,096-token context, its quantized versions need between 3.53 GB (IQ2_M, lowest quality) and 16.54 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 Phi-3.5-mini-instruct-GGUF is about 1.5 GB, rising to roughly 48.0 GB at its full 131,072-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is Q8_0, needing about 6.08 GB. That means bartowski/Phi-3.5-mini-instruct-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for bartowski/Phi-3.5-mini-instruct-GGUF include IQ2_M, Q2_K, Q2_K_L, IQ3_XS, Q3_K_S, IQ3_M, Q3_K_M, IQ4_XS, Q3_K_L, Q3_K_XL, Q4_0_4_4, Q4_0_4_8, Q4_0_8_8, Q4_0, 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, F32. The model supports a native context length of up to 131,072 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_M | 2.76 | Low | 1.23 GB | 1.5 GB | 3.53 GB | 326.3 t/s | Fits in VRAM |
| Q2_K | 2.97 | Low | 1.32 GB | 1.5 GB | 3.62 GB | 303.3 t/s | Fits in VRAM |
| Q2_K_L | 3.17 | Low | 1.41 GB | 1.5 GB | 3.71 GB | 284.0 t/s | Fits in VRAM |
| IQ3_XS | 3.4 | Fair | 1.51 GB | 1.5 GB | 3.81 GB | 264.3 t/s | Fits in VRAM |
| Q3_K_S | 3.52 | Fair | 1.57 GB | 1.5 GB | 3.87 GB | 255.4 t/s | Fits in VRAM |
| IQ3_M | 3.88 | Fair | 1.73 GB | 1.5 GB | 4.03 GB | 231.5 t/s | Fits in VRAM |
| Q3_K_M | 4.09 | Fair | 1.82 GB | 1.5 GB | 4.12 GB | 219.6 t/s | Fits in VRAM |
| IQ4_XS | 4.31 | Good | 1.92 GB | 1.5 GB | 4.22 GB | 208.5 t/s | Fits in VRAM |
| Q3_K_L | 4.37 | Good | 1.94 GB | 1.5 GB | 4.24 GB | 205.7 t/s | Fits in VRAM |
| Q3_K_XL | 4.55 | Good | 2.02 GB | 1.5 GB | 4.32 GB | 197.6 t/s | Fits in VRAM |
| Q4_0_4_4 | 4.56 | Good | 2.03 GB | 1.5 GB | 4.33 GB | 197.4 t/s | Fits in VRAM |
| Q4_0_4_8 | 4.56 | Good | 2.03 GB | 1.5 GB | 4.33 GB | 197.4 t/s | Fits in VRAM |
| Q4_0_8_8 | 4.56 | Good | 2.03 GB | 1.5 GB | 4.33 GB | 197.4 t/s | Fits in VRAM |
| Q4_0 | 4.57 | Good | 2.03 GB | 1.5 GB | 4.33 GB | 196.8 t/s | Fits in VRAM |
| Q4_K_S | 4.58 | Good | 2.04 GB | 1.5 GB | 4.34 GB | 196.2 t/s | Fits in VRAM |
| Q4_K_M | 5.01 | Very good | 2.23 GB | 1.5 GB | 4.53 GB | 179.5 t/s | Fits in VRAM |
| Q4_K_L | 5.16 | Very good | 2.3 GB | 1.5 GB | 4.6 GB | 174.1 t/s | Fits in VRAM |
| Q5_K_S | 5.53 | Very good | 2.46 GB | 1.5 GB | 4.76 GB | 162.6 t/s | Fits in VRAM |
| Q5_K_M | 5.89 | Very good | 2.62 GB | 1.5 GB | 4.92 GB | 152.6 t/s | Fits in VRAM |
| Q5_K_L | 6.02 | Very good | 2.68 GB | 1.5 GB | 4.98 GB | 149.3 t/s | Fits in VRAM |
| Q6_K | 6.57 | Excellent | 2.92 GB | 1.5 GB | 5.22 GB | 137.0 t/s | Fits in VRAM |
| Q6_K_L | 6.67 | Excellent | 2.96 GB | 1.5 GB | 5.26 GB | 134.9 t/s | Fits in VRAM |
| Q8_0 | 8.5 | Excellent | 3.78 GB | 1.5 GB | 6.08 GB | 105.8 t/s | Fits in VRAM |
| F32 | 32.0 | Excellent | 14.24 GB | 1.5 GB | 16.54 GB | 3.5 t/s | Offload |
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/Phi-3.5-mini-instruct-GGUF?
bartowski/Phi-3.5-mini-instruct-GGUF is an instruction-tuned chat model with 3.82 billion parameters, based on the phi3 architecture. It is released under the mit license and distributed as GGUF files for local inference.
How much VRAM do you need to run bartowski/Phi-3.5-mini-instruct-GGUF?
You need about 5.26 GB of VRAM to run bartowski/Phi-3.5-mini-instruct-GGUF entirely on the GPU using the Q6_K_L quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/Phi-3.5-mini-instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run bartowski/Phi-3.5-mini-instruct-GGUF fully on the GPU using Q8_0 (about 6.08 GB).
Can I run bartowski/Phi-3.5-mini-instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/Phi-3.5-mini-instruct-GGUF fully on the GPU using Q8_0 (about 6.08 GB).
Can I run bartowski/Phi-3.5-mini-instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/Phi-3.5-mini-instruct-GGUF fully on the GPU using F32 (about 16.54 GB).
What context length does bartowski/Phi-3.5-mini-instruct-GGUF support?
bartowski/Phi-3.5-mini-instruct-GGUF supports a native context length of up to 131,072 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/Phi-3.5-mini-instruct-GGUF?
For bartowski/Phi-3.5-mini-instruct-GGUF, a strong default is Q4_K_M, which needs about 4.53 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.