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gemma-3-270m-it-qat-q4_0-unquantized

google/gemma-3-270m-it-qat-q4_0-unquantized

gemma-3-270m-it-qat-q4_0-unquantized at Q4_K_M is exactly 253,115,200 bytes (0.24 GiB / 0.25 GB) — an effective 7.553 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)
Parameters
268M
Architecture
gemma3
Context
—
native (config.json)
License
gemma

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
UD-IQ2_XXS0.17 GiB180,104,2885.374—unsloth
UD-IQ2_M0.17 GiB182,787,1685.454—unsloth
UD-IQ3_XXS0.17 GiB184,517,7285.506—unsloth
IQ3_XXS0.22 GiB235,973,4407.041—bartowski
Q3_K_S0.22 GiB236,710,7207.063—bartowski
Q3_K_S0.22 GiB236,711,0087.063—unsloth
IQ3_XS0.22 GiB237,079,3607.074—bartowski
Q2_K_L0.22 GiB237,079,6487.074—unsloth
Q2_K0.22 GiB237,079,6487.074—unsloth
IQ3_M0.22 GiB238,994,2407.131—bartowski
IQ4_XS0.22 GiB240,857,9207.187—bartowski
IQ4_XS0.22 GiB240,858,2087.187—unsloth
Q4_00.22 GiB241,410,2087.204—lmstudio-community
Q4_00.22 GiB241,574,7207.208—bartowski
Q3_K_M0.23 GiB241,963,8407.220—bartowski
IQ4_NL0.23 GiB241,963,8407.220—bartowski
IQ4_NL0.23 GiB241,964,1287.220—unsloth
Q3_K_M0.23 GiB241,964,1287.220—unsloth
Q3_K_L0.23 GiB246,387,5207.352—bartowski
Q4_10.23 GiB247,677,7607.391—bartowski
Q4_10.23 GiB247,678,0487.391—unsloth
Q4_K_S0.23 GiB249,889,6007.457—bartowski
Q4_K_S0.23 GiB249,889,8887.457—unsloth
Q4_K_M0.24 GiB253,115,2007.553—bartowski
Q4_K_L0.24 GiB253,115,2007.553—bartowski
Q4_K_M0.24 GiB253,115,4887.553—unsloth
Q5_K_S0.24 GiB257,999,6807.699—bartowski
Q5_K_S0.24 GiB257,999,9687.699—unsloth
Q5_K_L0.24 GiB260,027,2007.759—bartowski
Q5_K_M0.24 GiB260,027,2007.759—bartowski
Q5_K_M0.24 GiB260,027,4887.759—unsloth
Q6_K0.26 GiB282,975,0408.444—bartowski
Q6_K_L0.26 GiB282,975,0408.444—bartowski
Q6_K0.26 GiB282,975,3288.444—unsloth
Q8_00.27 GiB291,545,9208.700—bartowski
Q4_00.37 GiB398,697,31211.897—unsloth
Q8_00.42 GiB448,832,60813.393—unsloth
BF160.51 GiB542,835,23216.198—bartowski
F160.51 GiB542,835,55216.198—unsloth

Compare with

same modality, comparable size
vs Llama-3.2-1B-Instructvs ced-basevs embeddinggemma-300mvs Qwen3-0.6Bvs Qwen2.5-0.5B-Instructvs SmolLM2-135M-InstructAll 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 0.14 GiB. The real file is 0.24 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

Architecture unavailable — this repository is gated and no ungated mirror was found. Exact file sizes above are still authoritative; only the KV math needs the config.

Questions people ask

How much VRAM does gemma-3-270m-it-qat-q4_0-unquantized need?
Q4_K_M is exactly 253,115,200 bytes (0.24 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of gemma-3-270m-it-qat-q4_0-unquantized 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.