Mistral Nemo 12B quants compared
22 real GGUF builds of Mistral Nemo 12B measured from bartowski/Mistral-Nemo-Instruct-2407-GGUF. Download the Q4_K_M (6.96 GB) unless you know why you need heavier. Probed 2026-09-02.
Download Mistral Nemo 12B Q4_K_M (6.96 GB). It fits 12 GB of memory with 16k context. With Ollama: ollama run mistral-nemo:12b
Every Mistral Nemo 12B quant by real file size
| Quant | Weights | + KV (16k) | Total | Fits comfortably in | Quality |
|---|---|---|---|---|---|
| F32 | 45.63 GB | 3.0 GB | 48.6 GB | 64 GB | Full precision (lossless) |
| F16 | 22.82 GB | 3.0 GB | 25.8 GB | 32 GB | Full precision (lossless) |
| Q8_0 | 12.13 GB | 3.0 GB | 15.1 GB | 24 GB | Near-lossless |
| Q6_K_L | 9.67 GB | 3.0 GB | 12.7 GB | 16 GB | Excellent |
| Q6_K | 9.37 GB | 3.0 GB | 12.4 GB | 16 GB | Excellent |
| Q5_K_L | 8.51 GB | 3.0 GB | 11.5 GB | 16 GB | Very high |
| Q5_K_M | 8.13 GB | 3.0 GB | 11.1 GB | 16 GB | Very high |
| Q5_K_S | 7.93 GB | 3.0 GB | 10.9 GB | 16 GB | Very high |
| Q4_K_M * | 6.96 GB | 3.0 GB | 10.0 GB | 12 GB | High: the default pick |
| Q4_K_L | 7.43 GB | 3.0 GB | 10.4 GB | 12 GB | High |
| Q4_K_S | 6.63 GB | 3.0 GB | 9.6 GB | 12 GB | High |
| Q4_0 | 6.61 GB | 3.0 GB | 9.6 GB | 12 GB | High |
| IQ4_XS | 6.28 GB | 3.0 GB | 9.3 GB | 12 GB | High |
| Q3_K_XL | 6.66 GB | 3.0 GB | 9.7 GB | 12 GB | Acceptable: visible loss |
| Q3_K_L | 6.11 GB | 3.0 GB | 9.1 GB | 12 GB | Acceptable: visible loss |
| Q3_K_M | 5.67 GB | 3.0 GB | 8.7 GB | 12 GB | Acceptable: visible loss |
| IQ3_M | 5.33 GB | 3.0 GB | 8.3 GB | 12 GB | Acceptable: visible loss |
| Q3_K_S | 5.15 GB | 3.0 GB | 8.2 GB | 12 GB | Acceptable: visible loss |
| IQ3_XS | 4.94 GB | 3.0 GB | 7.9 GB | 12 GB | Acceptable: visible loss |
| Q2_K_L | 5.07 GB | 3.0 GB | 8.1 GB | 12 GB | Experimental: not ranked: never recommended |
| Q2_K | 4.46 GB | 3.0 GB | 7.5 GB | 12 GB | Experimental: not ranked: never recommended |
| IQ2_M | 4.13 GB | 3.0 GB | 7.1 GB | 8 GB | Experimental: not ranked: never recommended |
* default pick. Weights = real GGUF file sizes from bartowski/Mistral-Nemo-Instruct-2407-GGUF (probed 2026-09-02). KV = fp16 estimate; a q8_0 cache roughly halves it. "Comfortable" = weights + KV within 90% of memory.
Best Mistral Nemo 12B quant by memory
| Memory | Recommended quant | Total (16k ctx) |
|---|---|---|
| 12 GB | Q4_K_M | 10.0 GB |
| 16 GB | Q6_K | 12.4 GB |
| 24 GB | Q8_0 | 15.1 GB |
| 32 GB | F16 | 25.8 GB |
Why we don't rank Mistral Nemo 12B's 2-bit quants
Quants at 2 bits per weight or below (Q2_K, IQ2, IQ1, TQ1) cut file size by roughly half versus Q4, but the quality collapse is steep and non-linear: perplexity spikes, instruction-following degrades, and hallucinations rise. A model that answers faster but wrong is not a smaller model: it is a worse one. ModelFit lists these builds for completeness but never ranks or recommends them.
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Frequently asked questions
What is the best quantization of Mistral Nemo 12B?
Q4_K_M is the default pick: 6.96 GB of weights, high: the default pick quality, fitting comfortably in 12 GB of memory (weights + 16k context KV-cache). Go Q6_K or Q8_0 if you have headroom.
How much memory does Mistral Nemo 12B need?
At Q4_K_M, Mistral Nemo 12B needs 6.96 GB for the weights plus ~3.0 GB of KV-cache at 16k context, about 10.0 GB total, so a 12 GB card or Mac (90% usable budget) runs it comfortably.
Should I use a Q2_K or IQ2 quant of Mistral Nemo 12B?
No. Mistral Nemo 12B at 2 bits per weight is a visibly worse model. Quality collapse at that bitrate is steep, not gradual. If only a 2-bit build fits your memory, run a smaller model at Q4_K_M instead. ModelFit lists these builds but never recommends them.
Cite this page
ModelFit: Mistral Nemo 12B quantization comparison (real GGUF file sizes). https://modelfit.io/quant-compare/mistral-nemo-12b/ (data probed 2026-09-02, CC BY 4.0).