Can an RTX 4060 run Phi 4?
Barely
It only loads at IQ3_XXS, which compresses the weights far enough to visibly degrade the model, so we would not call that running it.
In practice
It loads, and we would not call that running it. The only quantization that fits the GeForce RTX 4060 is IQ3_XXS, which compresses the weights far enough that Phi 4 loses a visible amount of its ability, so we do not recommend it. At Q4_K_M it needs 9.96 GB against the 7.2 GB available.
Running it properly means a split with system RAM: at Q4_K_M about 28 of 40 layers fit on the card, and the estimate falls to 7.8 tokens per second. A smaller model at a proper quantization is a better answer than a larger one squeezed in, and the next section lists the ones that fit.
The numbers
Phi 4 has 14.66 billion parameters across 40 layers. The GeForce RTX 4060 has 8 GB of VRAM at 272 GB/s, of which about 7.2 GB is left for a model once the operating system has taken its share.
| Quantization | Total needed | Fits | Speed |
|---|---|---|---|
| Q8_0 | 16 GB | partial | 3.0 t/s |
| Q6_K | 12.81 GB | partial | 4.4 t/s |
| Q5_K_M | 11.37 GB | partial | 5.6 t/s |
| Q4_K_M | 9.96 GB | partial | 7.8 t/s |
| IQ4_XS | 9 GB | partial | 10.3 t/s |
| Q3_K_M | 8.44 GB | partial | 12.5 t/s |
| IQ3_XXS degraded | 7.04 GB | yes | 30.3 t/s |
| Q2_K degraded | 7.52 GB | partial | 22.0 t/s |
| IQ2_XXS degraded | 5.39 GB | yes | 40.4 t/s |
2 of the 9 levels fit entirely. Levels marked degraded are listed for completeness, not as advice. Rows marked partial or experts in RAM run with part of the model in system RAM, at the speed shown.
What to run instead
A smaller model from the same family is the straightforward answer. These fit on an RTX 4060 at a quantization worth using:
- Phi 4 Mini Instruct, 3.84B at Q8_0, 5.17 GB, about 41.7 tokens/s
- Phi 3 Mini 4k Instruct, 3.82B at Q8_0, 4.8 GB, about 45.2 tokens/s
Shortening the context will not rescue this one: at 4k it still wants 9.18 GB against 9.96 GB at 8k, because the weights, not the cache, are what fill the card.
Or the hardware that does run it
The cheapest device we track that runs Phi 4 at a quantization worth using is the GeForce RTX 2060 12GB, around $160 used , running it at Q4_K_M at about 26.0 tokens per second. Check the current price (affiliate link, indicative price reviewed 2026-09-01). Compare every option.
What else an RTX 4060 runs well
For comparison, these are the other popular models that fit this device at a sound quantization, best pairings first.
| Model | Quantization | Memory | Speed |
|---|---|---|---|
| Qwen3.5 9B | Q5_K_M | 6.42 GB | 33.3 t/s |
| Qwen3 8B | Q5_K_M | 6.54 GB | 32.6 t/s |
| Qwen2.5 7B Instruct | Q6_K | 6.23 GB | 34.3 t/s |
| Llama 3.1 8B Instruct | Q6_K | 7.14 GB | 29.7 t/s |
The same model on nearby hardware
Memory decides whether Phi 4 loads and bandwidth decides how quickly it answers. Here is what a step down, a card with the same memory and a step up do with it.
| Device | Memory | Bandwidth | Result |
|---|---|---|---|
| GeForce RTX 3050 6GB | 6 GB | 168 GB/s | partial, 5.1 t/s |
| GeForce RTX 3070 | 8 GB | 448 GB/s | does not fit |
| GeForce RTX 4070 | 12 GB | 504 GB/s | Q4_K_M, 39.0 t/s |
Questions people ask
How much VRAM does Phi 4 need on RTX 4060?
At Q4_K_M with an 8k context window it needs 9.96 GB in total: 7.95 GB of weights, 1.56 GB of KV cache and 0.44 GB of runtime overhead. The GeForce RTX 4060 makes about 7.2 GB available once the operating system has taken its share.
What should I run instead of Phi 4 on RTX 4060?
Phi 4 Mini Instruct fits at Q8_0 and runs at about 41.7 tokens per second. Smaller models from the same family are the usual answer.
Keep going
Everything the GeForce RTX 4060 runs, the full breakdown for Phi 4, how the numbers are made in the methodology, or a different pairing in the calculator.