Mistral 7B Instruct quants compared
23 GGUF builds by real file size, probed from bartowski/Mistral-7B-Instruct-v0.3-GGUF on Hugging Face (2026-09-02). 7B params.
Download Mistral 7B Instruct Q4_K_M (4.07 GB) — it fits 8 GB of memory with 16k context. With Ollama: ollama run mistral:7b-instruct-q4_K_M
Every Mistral 7B Instruct quant by real file size
| Quant | Weights | + KV (16k) | Total | Fits comfortably in | Quality |
|---|---|---|---|---|---|
| F32 | 27 GB | 2.0 GB | 29.0 GB | 48 GB | Full precision (lossless) |
| Q8_0 | 7.17 GB | 2.0 GB | 9.2 GB | 12 GB | Near-lossless |
| Q6_K | 5.54 GB | 2.0 GB | 7.5 GB | 12 GB | Excellent |
| Q5_K_M | 4.78 GB | 2.0 GB | 6.8 GB | 8 GB | Very high |
| Q5_K_S | 4.66 GB | 2.0 GB | 6.7 GB | 8 GB | Very high |
| Q4_K_M * | 4.07 GB | 2.0 GB | 6.1 GB | 8 GB | High — the default pick |
| Q4_K_S | 3.86 GB | 2.0 GB | 5.9 GB | 8 GB | High |
| IQ4_NL | 3.85 GB | 2.0 GB | 5.8 GB | 8 GB | High |
| IQ4_XS | 3.64 GB | 2.0 GB | 5.6 GB | 8 GB | High |
| Q3_K_L | 3.56 GB | 2.0 GB | 5.6 GB | 8 GB | Acceptable — visible loss |
| Q3_K_M | 3.28 GB | 2.0 GB | 5.3 GB | 8 GB | Acceptable — visible loss |
| IQ3_M | 3.06 GB | 2.0 GB | 5.1 GB | 8 GB | Acceptable — visible loss |
| IQ3_S | 2.97 GB | 2.0 GB | 5.0 GB | 8 GB | Acceptable — visible loss |
| Q3_K_S | 2.95 GB | 2.0 GB | 5.0 GB | 8 GB | Acceptable — visible loss |
| IQ3_XS | 2.82 GB | 2.0 GB | 4.8 GB | 8 GB | Acceptable — visible loss |
| IQ3_XXS | 2.64 GB | 2.0 GB | 4.6 GB | 8 GB | Acceptable — visible loss |
| Q2_K | 2.54 GB | 2.0 GB | 4.5 GB | 8 GB | Experimental — not ranked — never recommended |
| IQ2_M | 2.33 GB | 2.0 GB | 4.3 GB | 8 GB | Experimental — not ranked — never recommended |
| IQ2_S | 2.16 GB | 2.0 GB | 4.2 GB | 8 GB | Experimental — not ranked — never recommended |
| IQ2_XS | 2.05 GB | 2.0 GB | 4.0 GB | 8 GB | Experimental — not ranked — never recommended |
| IQ2_XXS | 1.86 GB | 2.0 GB | 3.9 GB | 8 GB | Experimental — not ranked — never recommended |
| IQ1_M | 1.64 GB | 2.0 GB | 3.6 GB | 8 GB | Experimental — not ranked — never recommended |
| IQ1_S | 1.5 GB | 2.0 GB | 3.5 GB | 8 GB | Experimental — not ranked — never recommended |
* default pick. Weights = real GGUF file sizes from bartowski/Mistral-7B-Instruct-v0.3-GGUF (probed 2026-09-02). KV = fp16 estimate; a q8_0 cache roughly halves it. "Comfortable" = weights + KV within 90% of memory.
Best Mistral 7B Instruct quant by memory
| Memory | Recommended quant | Total (16k ctx) |
|---|---|---|
| 8 GB | Q5_K_M | 6.8 GB |
| 12 GB | Q8_0 | 9.2 GB |
| 48 GB | F32 | 29.0 GB |
Why we don't rank Mistral 7B Instruct'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.
Frequently asked questions
What is the best quantization of Mistral 7B Instruct?
Q4_K_M is the default pick: 4.07 GB of weights, high — the default pick quality, fitting comfortably in 8 GB of memory (weights + 16k context KV-cache). Go Q6_K or Q8_0 if you have headroom.
How much memory does Mistral 7B Instruct need?
At Q4_K_M, Mistral 7B Instruct needs 4.07 GB for the weights plus ~2.0 GB of KV-cache at 16k context — about 6.1 GB total, so a 8 GB card or Mac (90% usable budget) runs it comfortably.
Should I use a Q2_K or IQ2 quant of Mistral 7B Instruct?
No. Mistral 7B Instruct 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 7B Instruct quantization comparison (real GGUF file sizes). https://modelfit.io/quant-compare/mistral-7b/ (data probed 2026-09-02, CC BY 4.0).