Llama 3.1 8B Instruct quants compared
21 real GGUF builds of Llama 3.1 8B Instruct measured from bartowski/Meta-Llama-3.1-8B-Instruct-GGUF. Download the Q4_K_M (4.58 GB) unless you know why you need heavier. Probed 2026-09-02.
Download Llama 3.1 8B Instruct Q4_K_M (4.58 GB). It fits 8 GB of memory with 16k context. With Ollama: ollama run llama3.1:8b-instruct-q4_K_M
Every Llama 3.1 8B Instruct quant by real file size
| Quant | Weights | + KV (16k) | Total | Fits comfortably in | Quality |
|---|---|---|---|---|---|
| F32 | 29.92 GB | 2.0 GB | 31.9 GB | 48 GB | Full precision (lossless) |
| Q8_0 | 7.95 GB | 2.0 GB | 9.9 GB | 12 GB | Near-lossless |
| Q6_K_L | 6.38 GB | 2.0 GB | 8.4 GB | 12 GB | Excellent |
| Q6_K | 6.14 GB | 2.0 GB | 8.1 GB | 12 GB | Excellent |
| Q5_K_L | 5.64 GB | 2.0 GB | 7.6 GB | 12 GB | Very high |
| Q5_K_M | 5.34 GB | 2.0 GB | 7.3 GB | 12 GB | Very high |
| Q5_K_S | 5.21 GB | 2.0 GB | 7.2 GB | 12 GB | Very high |
| Q4_K_M * | 4.58 GB | 2.0 GB | 6.6 GB | 8 GB | High: the default pick |
| Q4_K_L | 4.95 GB | 2.0 GB | 7.0 GB | 8 GB | High |
| Q4_K_S | 4.37 GB | 2.0 GB | 6.4 GB | 8 GB | High |
| IQ4_NL | 4.36 GB | 2.0 GB | 6.4 GB | 8 GB | High |
| IQ4_XS | 4.14 GB | 2.0 GB | 6.1 GB | 8 GB | High |
| Q3_K_XL | 4.45 GB | 2.0 GB | 6.5 GB | 8 GB | Acceptable: visible loss |
| Q3_K_L | 4.03 GB | 2.0 GB | 6.0 GB | 8 GB | Acceptable: visible loss |
| Q3_K_M | 3.74 GB | 2.0 GB | 5.7 GB | 8 GB | Acceptable: visible loss |
| IQ3_M | 3.52 GB | 2.0 GB | 5.5 GB | 8 GB | Acceptable: visible loss |
| Q3_K_S | 3.41 GB | 2.0 GB | 5.4 GB | 8 GB | Acceptable: visible loss |
| IQ3_XS | 3.28 GB | 2.0 GB | 5.3 GB | 8 GB | Acceptable: visible loss |
| Q2_K_L | 3.44 GB | 2.0 GB | 5.4 GB | 8 GB | Experimental: not ranked: never recommended |
| Q2_K | 2.96 GB | 2.0 GB | 5.0 GB | 8 GB | Experimental: not ranked: never recommended |
| IQ2_M | 2.75 GB | 2.0 GB | 4.8 GB | 8 GB | Experimental: not ranked: never recommended |
* default pick. Weights = real GGUF file sizes from bartowski/Meta-Llama-3.1-8B-Instruct-GGUF (probed 2026-09-02). KV = fp16 estimate; a q8_0 cache roughly halves it. "Comfortable" = weights + KV within 90% of memory.
Best Llama 3.1 8B Instruct quant by memory
| Memory | Recommended quant | Total (16k ctx) |
|---|---|---|
| 8 GB | Q4_K_M | 6.6 GB |
| 12 GB | Q8_0 | 9.9 GB |
| 48 GB | F32 | 31.9 GB |
Why we don't rank Llama 3.1 8B 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.
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Frequently asked questions
What is the best quantization of Llama 3.1 8B Instruct?
Q4_K_M is the default pick: 4.58 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 Llama 3.1 8B Instruct need?
At Q4_K_M, Llama 3.1 8B Instruct needs 4.58 GB for the weights plus ~2.0 GB of KV-cache at 16k context, about 6.6 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 Llama 3.1 8B Instruct?
No. Llama 3.1 8B 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: Llama 3.1 8B Instruct quantization comparison (real GGUF file sizes). https://modelfit.io/quant-compare/llama3.1-8b (data probed 2026-09-02, CC BY 4.0).