microsoft · text

Phi-3.5-mini-instruct

microsoft/Phi-3.5-mini-instruct

Phi-3.5-mini-instruct at Q4_K_M is exactly 2,393,232,384 bytes (2.23 GiB / 2.39 GB) — an effective 5.011 bits per weight, not the nominal 4. Its KV cache at 32K is 12.00 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
3.8B
Architecture
phi3
32 layers
Context
131,072
native (config.json)
License
mit

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
IQ1_S0.78 GiB841,609,9521.762—MaziyarPanahi
IQ1_M0.85 GiB917,107,4241.920—MaziyarPanahi
IQ2_XS1.07 GiB1,153,037,0242.414—MaziyarPanahi
IQ2_M1.23 GiB1,316,395,2962.756—bartowski
Q2_K1.32 GiB1,416,204,5122.965—MaziyarPanahi
Q2_K1.32 GiB1,416,204,5762.965—bartowski
Q2_K_L1.41 GiB1,512,396,5763.166—bartowski
IQ3_XS1.51 GiB1,625,175,7763.402—MaziyarPanahi
IQ3_XS1.51 GiB1,625,175,8403.402—bartowski
Q3_K_S1.57 GiB1,681,798,8803.521—MaziyarPanahi
Q3_K_S1.57 GiB1,681,798,9443.521—bartowski
IQ3_M1.73 GiB1,855,600,4163.885—bartowski
Q3_K_M1.82 GiB1,955,477,2164.094197MaziyarPanahi
Q3_K_M1.82 GiB1,955,477,2804.094197bartowski
IQ4_XS1.92 GiB2,059,853,0244.313197MaziyarPanahi
IQ4_XS1.92 GiB2,059,853,0884.313197bartowski
Q3_K_L1.94 GiB2,087,597,7924.371—MaziyarPanahi
Q3_K_L1.94 GiB2,087,597,8564.371—bartowski
Q4_02.03 GiB2,182,468,8964.569197bartowski
Q4_K_S2.04 GiB2,188,760,2884.582—MaziyarPanahi
Q4_K_S2.04 GiB2,188,760,3524.582—bartowski
Q4_K_M2.23 GiB2,393,232,3845.011197cowoho
Q4_K_M2.23 GiB2,393,232,6085.011—MaziyarPanahi
Q4_K_M2.23 GiB2,393,232,6725.011197bartowski
Q4_K_L2.30 GiB2,466,338,5925.164—bartowski
Q5_K_S2.46 GiB2,641,474,7845.530—MaziyarPanahi
Q5_K_S2.46 GiB2,641,474,8485.530—bartowski
Q5_K_M2.62 GiB2,815,276,2565.894197MaziyarPanahi
Q5_K_M2.62 GiB2,815,276,3205.894197bartowski
Q5_K_L2.68 GiB2,876,069,6646.021—bartowski
Q6_K2.92 GiB3,135,853,2806.565197MaziyarPanahi
Q6_K2.92 GiB3,135,853,3446.565197bartowski
Q6_K_L2.96 GiB3,183,564,5766.665—bartowski
Q8_03.78 GiB4,061,222,6248.503197MaziyarPanahi
Q8_03.78 GiB4,061,222,6888.503197bartowski
F3214.24 GiB15,285,057,02432.002—bartowski

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0961.50 GiB1.50 GiB—32 / 0 / 0
8,1923.00 GiB3.00 GiB—32 / 0 / 0
16,3846.00 GiB6.00 GiB—32 / 0 / 0
32,76812.00 GiB12.00 GiB—32 / 0 / 0
65,53624.00 GiB24.00 GiB—32 / 0 / 0
131,07248.00 GiB48.00 GiB—32 / 0 / 0

Compare with

same modality, comparable size
vs Qwen3-4Bvs HyperCLOVAX-SEED-Text-Instruct-1.5Bvs Llama-3.2-1B-Instructvs ced-basevs gemma-3-1b-itvs Qwen3-1.7BAll alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 2.00 GiB. The real file is 2.23 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
32
Attention heads
32
KV heads
32
Head dim
96
Hidden size
3072
Vocab
32,064
Sliding window
262144
SWA period
1
MLA
no
Experts
—
Experts per token
—
use_sliding_window
—

Questions people ask

How much VRAM does Phi-3.5-mini-instruct need?
Q4_K_M is exactly 2,393,232,384 bytes (2.23 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Phi-3.5-mini-instruct's KV cache?
12.00 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Which quantization of Phi-3.5-mini-instruct should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.