Can an RTX 3050 6GB run Phi 4?
Partly
Not entirely. About 20 of its 40 layers fit on the card and the rest run on the CPU, which brings generation down to roughly 5.1 tokens per second.
In practice
It does not fit entirely. About 20 of the 40 layers of Phi 4 fit on the GeForce RTX 3050 6GB at Q4_K_M, and the rest run on the CPU from system RAM. The estimate is about 5.1 tokens per second.
That number is lower than the share on the card suggests, and our own offload sweep shows why: every token waits for the slowest part, so the last layers left in RAM cost far more than the first ones moved. There is no quantization of this model worth using that fits entirely, so the choice is between this split and a smaller model.
The numbers
Phi 4 has 14.66 billion parameters across 40 layers. The GeForce RTX 3050 6GB has 6 GB of VRAM at 168 GB/s, of which about 5.2 GB is left for a model once the operating system has taken its share.
| Quantization | Total needed | Fits | Speed |
|---|---|---|---|
| Q8_0 | 16 GB | partial | 2.5 t/s |
| Q6_K | 12.81 GB | partial | 3.4 t/s |
| Q5_K_M | 11.37 GB | partial | 4.0 t/s |
| Q4_K_M | 9.96 GB | partial | 5.1 t/s |
| IQ4_XS | 9 GB | partial | 6.0 t/s |
| Q3_K_M | 8.44 GB | partial | 6.6 t/s |
| IQ3_XXS degraded | 7.04 GB | partial | 9.7 t/s |
| Q2_K degraded | 7.52 GB | partial | 8.3 t/s |
| IQ2_XXS degraded | 5.39 GB | partial | 21.6 t/s |
Nothing on this ladder fits entirely, including the 2-bit levels we do not recommend. Rows marked partial or experts in RAM run with part of the model in system RAM, at the speed shown.
The settings we would use
llama-server -m <model>.gguf -ngl 20 -c 8192 -fa on20 of 40 layers fit on the card. If the load fails with an out-of-memory error, lower the number by 2 and try again.
This split is set in llama.cpp. Ollama chooses its own placement and does not take these options, so run ollama ps to see what it picked.
The llama.cpp flags guide explains each option above.
If you want it faster
A smaller model from the same family is the straightforward answer. These fit on an RTX 3050 6GB at a quantization worth using:
- Phi 4 Mini Instruct, 3.84B at Q8_0, 5.17 GB, about 25.8 tokens/s
- Phi 3 Mini 4k Instruct, 3.82B at Q8_0, 4.8 GB, about 27.9 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 3050 6GB 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 | IQ4_XS | 4.98 GB | 27.1 t/s |
| Qwen2.5 7B Instruct | Q4_K_M | 4.81 GB | 28.0 t/s |
| Llama 3.1 8B Instruct | IQ4_XS | 5.12 GB | 26.2 t/s |
| Qwen3 8B | Q3_K_M | 4.98 GB | 27.0 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 3070 | 8 GB | 448 GB/s | does not fit |
See how fast it feels
GeForce RTX 3050 6GB running Phi 4 at Q4_K_M, 20 of 40 layers on the GPU
YouWhy does my model use more memory when the conversation gets longer?
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.
Questions people ask
How much VRAM does Phi 4 need on RTX 3050 6GB?
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 3050 6GB makes about 5.2 GB available once the operating system has taken its share.
Can I run Phi 4 on RTX 3050 6GB anyway?
Yes, with part of it in system RAM. About 20 of 40 layers fit on the card and the speed falls to roughly 5.1 tokens per second, which suits batch work better than conversation.
Keep going
Everything the GeForce RTX 3050 6GB runs, the full breakdown for Phi 4, how the numbers are made in the methodology, or a different pairing in the calculator.