gemma-3-270m-it
google/gemma-3-270m-itgemma-3-270m-it at Q4_K_M is exactly 253,115,168 bytes (0.24 GiB / 0.25 GB) — an effective 7.553 bits per weight, not the nominal 4. Its KV cache at 32K is 0.11 GiB, not the 0.56 GiB a flat formula predicts.
Shipped quantizations
| Quant | Size● | Exact bytes● | Effective bpw● | Tensors● | Publisher |
|---|---|---|---|---|---|
| UD-IQ2_XXS | 0.17 GiB | 180,104,224 | 5.374 | — | unsloth |
| UD-IQ2_M | 0.17 GiB | 182,787,104 | 5.454 | — | unsloth |
| UD-IQ3_XXS | 0.17 GiB | 184,517,664 | 5.506 | — | unsloth |
| IQ3_XXS | 0.22 GiB | 235,973,408 | 7.041 | — | bartowski |
| Q3_K_S | 0.22 GiB | 236,710,688 | 7.063 | — | bartowski |
| Q3_K_S | 0.22 GiB | 236,710,944 | 7.063 | — | unsloth |
| IQ3_XS | 0.22 GiB | 237,079,328 | 7.074 | — | bartowski |
| Q2_K | 0.22 GiB | 237,079,584 | 7.074 | — | unsloth |
| Q2_K_L | 0.22 GiB | 237,079,584 | 7.074 | — | unsloth |
| IQ3_M | 0.22 GiB | 238,994,208 | 7.131 | — | bartowski |
| IQ4_XS | 0.22 GiB | 240,857,888 | 7.187 | 236 | bartowski |
| IQ4_XS | 0.22 GiB | 240,858,144 | 7.187 | 236 | unsloth |
| Q4_0 | 0.22 GiB | 241,574,688 | 7.208 | 236 | bartowski |
| Q4_0 | 0.22 GiB | 241,574,944 | 7.209 | 236 | unsloth |
| Q3_K_M | 0.23 GiB | 241,963,808 | 7.220 | 236 | bartowski |
| IQ4_NL | 0.23 GiB | 241,963,808 | 7.220 | — | bartowski |
| Q3_K_M | 0.23 GiB | 241,964,064 | 7.220 | 236 | unsloth |
| IQ4_NL | 0.23 GiB | 241,964,064 | 7.220 | — | unsloth |
| Q3_K_L | 0.23 GiB | 246,387,488 | 7.352 | — | bartowski |
| Q4_1 | 0.23 GiB | 247,677,728 | 7.391 | — | bartowski |
| Q4_1 | 0.23 GiB | 247,677,984 | 7.391 | — | unsloth |
| Q4_K_S | 0.23 GiB | 249,889,568 | 7.457 | — | bartowski |
| Q4_K_S | 0.23 GiB | 249,889,824 | 7.457 | — | unsloth |
| Q4_K_M | 0.24 GiB | 253,115,168 | 7.553 | 236 | bartowski |
| Q4_K_L | 0.24 GiB | 253,115,168 | 7.553 | — | bartowski |
| Q4_K_M | 0.24 GiB | 253,115,424 | 7.553 | — | unsloth |
| Q5_K_S | 0.24 GiB | 257,999,648 | 7.699 | — | bartowski |
| Q5_K_S | 0.24 GiB | 257,999,904 | 7.699 | — | unsloth |
| Q5_K_M | 0.24 GiB | 260,027,168 | 7.759 | 236 | bartowski |
| Q5_K_L | 0.24 GiB | 260,027,168 | 7.759 | — | bartowski |
| Q5_K_M | 0.24 GiB | 260,027,424 | 7.759 | 236 | unsloth |
| Q6_K | 0.26 GiB | 282,975,008 | 8.444 | 236 | bartowski |
| Q6_K_L | 0.26 GiB | 282,975,008 | 8.444 | — | bartowski |
| Q6_K | 0.26 GiB | 282,975,264 | 8.444 | 236 | unsloth |
| Q8_0 | 0.27 GiB | 291,545,600 | 8.700 | — | ggml-org |
| Q8_0 | 0.27 GiB | 291,545,888 | 8.700 | 236 | bartowski |
| Q8_0 | 0.27 GiB | 291,546,144 | 8.700 | 236 | unsloth |
| BF16 | 0.51 GiB | 542,835,200 | 16.198 | — | bartowski |
| F16 | 0.51 GiB | 542,835,488 | 16.198 | 236 | unsloth |
KV cache by context
| Context | KV cache (f16)● | Flat formula | Overstated by | Full / windowed / recurrent |
|---|---|---|---|---|
| 4,096 | 0.03 GiB | 0.07 GiB | 2.67× | 3 / 15 / 0 |
| 8,192 | 0.04 GiB | 0.14 GiB | 3.69× | 3 / 15 / 0 |
| 16,384 | 0.06 GiB | 0.28 GiB | 4.57× | 3 / 15 / 0 |
| 32,768 | 0.11 GiB | 0.56 GiB | 5.19× | 3 / 15 / 0 |
| 65,536 | 0.20 GiB | 1.13 GiB | 5.57× | 3 / 15 / 0 |
| 131,072 | 0.39 GiB | 2.25 GiB | 5.77× | 3 / 15 / 0 |
15 of 18 layers cache only a 512-token window rather than the full context, on a period of 6. Figures assume the default configuration; --swa-full disables the saving entirely.
Compare with
Will it run on your card?
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. The larger discrepancy is the cache: a flat formula gives 0.56 GiB at 32K context where the real figure is 0.11 GiB, because most of this model's layers cache a fixed window rather than the whole context.
Architecture
Questions people ask
- How much VRAM does gemma-3-270m-it need?
- Q4_K_M is exactly 253,115,168 bytes (0.24 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
- How large is gemma-3-270m-it's KV cache?
- 0.11 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 gemma-3-270m-it 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.