Which quant level should you download?
For each common card, every level each recommended model ships, sized at 4K context, and the one to fetch: the highest-quality level that still fits comfortably.
Rows are the models this site recommends for each card: the largest that fits comfortably, and the largest that leaves a third of the card free, for chat, code and image tasks. Every figure is the VRAM calculator's own at 4K context and batch 1, and each row opens there with the same settings.
Green fits comfortably (at most 88% of the card). Amber is tight: it may load, with little room for context. Red does not fit. "Won't load here" means this card's runtime cannot read that format at any size — AWQ on a Mac, for example.
Perplexity rise is the published figure for that level against the full-precision model; a dash means none was published, and none is invented.
RTX 5060 Ti 16G · RTX 4060 Ti 16G
16 GB
| Model | Download | Perplexity rise | Every level at 4K | |
|---|---|---|---|---|
| Mistral Small 24B Instruct | AWQ INT4 · 13.2 GB | +3.8% |
| Calculator |
| Devstral Small 1.1 24B | AWQ INT4 · 13.0 GB | +4.0% |
| Calculator |
| Magistral Small 1.2 24B | AWQ INT4 · 13.0 GB | +4.0% |
| Calculator |
| Falcon 3 10B Instruct | Q4_K_M · 7.3 GB | +3.1% |
| Calculator |
| Gemma 4 E4B IT | Q8_0 · 8.7 GB | — |
| Calculator |
RTX 4060 Ti 8G
8 GB
| Model | Download | Perplexity rise | Every level at 4K | |
|---|---|---|---|---|
| Falcon 3 10B Instruct | GPTQ INT4 · 6.1 GB | +4.8% |
| Calculator |
| Qwen3.5 9B | Q4_K_M · 6.6 GB | — |
| Calculator |
| Qwen3 4B Instruct | Q6_K · 4.0 GB | +0.7% |
| Calculator |
| Qwen3.5 2B | Q8_0 · 2.2 GB | — |
| Calculator |
RTX 5070
12 GB
| Model | Download | Perplexity rise | Every level at 4K | |
|---|---|---|---|---|
| DeepSeek-V2-Lite Chat | Q4_K_M · 10.1 GB | +3.0% |
| Calculator |
| Gemma 3 12B IT | Q5_K_M · 9.8 GB | +1.3% |
| Calculator |
| Granite 3.1 8B Instruct | Q4_K_M · 5.9 GB | +3.0% |
| Calculator |
| Gemma 4 E2B IT | Q8_0 · 5.2 GB | — |
| Calculator |
RTX 3090 · RTX 4090
24 GB
| Model | Download | Perplexity rise | Every level at 4K | |
|---|---|---|---|---|
| Recursive Flint 40B-A2.5B | Q3_K_M · 20.6 GB | — |
| Calculator |
| MiniCPM-V 4.7 35B-A3B | Q4_K_S · 20.9 GB | — |
| Calculator |
| ERNIE 4.5 21B-A3B | Q4_K_M · 14.2 GB | — |
| Calculator |
| GPT-OSS 20B | MXFP4 · 12.8 GB | +0.0% |
| Calculator |
| Gemma 3 12B IT | Q5_K_M · 9.8 GB | +1.3% |
| Calculator |
RTX 5090
32 GB
| Model | Download | Perplexity rise | Every level at 4K | |
|---|---|---|---|---|
| Nemotron 3.5 Bolt 74B-A9B | IQ2_XXS · 23.1 GB | — |
| Calculator |
| GLM-4.7-Flash | Q4_K_M · 19.2 GB | — |
| Calculator |
| Gemma 3 27B IT | Q4_K_M · 18.4 GB | +2.8% |
| Calculator |
Radeon RX 9070 XT
16 GB
| Model | Download | Perplexity rise | Every level at 4K | |
|---|---|---|---|---|
| Mistral Small 24B Instruct | AWQ INT4 · 13.2 GB | +3.8% |
| Calculator |
| Devstral Small 1.1 24B | AWQ INT4 · 13.0 GB | +4.0% |
| Calculator |
| Magistral Small 1.2 24B | AWQ INT4 · 13.0 GB | +4.0% |
| Calculator |
| Falcon 3 10B Instruct | Q4_K_M · 7.3 GB | +3.1% |
| Calculator |
| Gemma 4 E4B IT | Q8_0 · 8.7 GB | — |
| Calculator |
Mac M4 Pro 24G
24 GB
| Model | Download | Perplexity rise | Every level at 4K | |
|---|---|---|---|---|
| Qwen3-VL 30B-A3B Instruct | Q3_K_M · 15.8 GB | +5.4% |
| Calculator |
| North Mini Code 1.0 30B-A3B | Q3_K_M · 15.3 GB | — |
| Calculator |
| DeepSeek-V2-Lite Chat | Q4_K_M · 10.1 GB | +3.0% |
| Calculator |
| Gemma 3 12B IT | Q5_K_M · 9.8 GB | +1.3% |
| Calculator |
32 GB RAM (CPU)
32 GB
| Model | Download | Perplexity rise | Every level at 4K | |
|---|---|---|---|---|
| Nemotron 3.5 Bolt 74B-A9B | IQ2_XXS · 23.1 GB | — |
| Calculator |
| GLM-4.7-Flash | Q4_K_M · 19.2 GB | — |
| Calculator |
| Gemma 3 27B IT | Q4_K_M · 18.4 GB | +2.8% |
| Calculator |
Another card? Each of the 78 cards in the index has its own page with every model that fits: all GPU pages
How the pick is made
Every level the model is published at is sized with the VRAM calculator at 4K context, then judged against the card: green is at most 88% of it, amber up to 105%, and a format the card's runtime cannot read is out at any size. Of the green levels, the pick is the most faithful one: the lowest published perplexity rise when the level has one, otherwise the most bits per weight — within one model more bits is closer to the original, which is the only ordering the data supports when no perplexity was published. It is the same rule the calculator's "Best fit" uses, so the two always agree.
What the level names mean
The number is roughly the bits stored per weight. GGUF Q8_0 keeps about 8.5 bits, Q6_K about 6.6, Q5_K_M about 5.7, Q4_K_M about 4.9 and Q3_K_M about 3.9; the file shrinks in step, and the perplexity rise grows as the bits fall. AWQ INT4 and GPTQ INT4 are 4-bit formats that vLLM runs, usually a little smaller than Q4_K_M. A model released in a native low-bit format, such as GPT-OSS in MXFP4, has no full-precision original to lose against, so that level is the reference.
Common questions
- Q4 or Q5 — which should I pick?
- If both are green on your card at the context you need, take the higher one: it is closer to the original model and costs only memory you have spare. If only Q4 is green, take Q4 rather than a tight Q5 — a level that barely loads leaves no room for context, and the runtime may push layers to system memory, which is far slower. Where a model's perplexity is published for both levels, its own page shows the difference.
- Why does the pick change when I raise the context?
- The weights stay the same size at any context, but the KV cache grows with every token of context, and it shares the card with them. A level that is green at 4K can turn amber at 32K, and the pick moves down a level to make room. The context-length page shows how far each pick stretches; the calculator recomputes the pick at whatever context you set.
- Why is the pick AWQ on some cards, and can I use it on Windows?
- AWQ INT4 stores every layer at 4 bits, so it is smaller than GGUF
Q4_K_Mand can be the only level that fits a card comfortably. It runs in vLLM, which needs Linux or WSL2 — Ollama and LM Studio on native Windows load GGUF only. If that is your setup, take the best GGUF row in the table instead, even if it is amber, and keep the context short.