Can Phi 3 Mini 3.8B run on RX 5700 XT 8GB?

YES — With Q3_K_S

C53Usable

Estimated from fit model

Phi 3 Mini 3.8B needs ~9.4 GB VRAM. RX 5700 XT 8GB has 8.0 GB. With Q3_K_S quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Phi 3 Mini 3.8B at Q4_K_M needs 9.9 GB — too much for RX 5700 XT 8GB (8.0 GB). Runs at Q3_K_S (9.4 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.9 GB, exceeds 8.0 GB available

9.9 GB required8.0 GB available

124% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

48.4 tok/s

TTFT

4003 ms

Safe context

11K

Memory

9.9 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsPhi 3 Mini 3.8B on RX 5700 XT 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 48.4 tok/s decode · 4.0s TTFT (warm) · 121 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	53.2 tok/s	1985 ms	11K
Coding	F	Too heavy	48.4 tok/s	4003 ms	11K
Agentic Coding	F	Too heavy	18.1 tok/s	15520 ms	11K
Reasoning	F	Too heavy	48.4 tok/s	4731 ms	11K
RAG	F	Too heavy	18.1 tok/s	19401 ms	11K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B68
Q3_K_S	3	1.9 GB	Low	B69
NVFP4	4	2.1 GB	Medium	B69
Q4_K_M	4	2.3 GB	Medium	B69
Q5_K_M	5	2.7 GB	High	A70
Q6_K	6	3.1 GB	High	A71
Q8_0Best for your GPU	8	4.1 GB	Very High	A70
F16	16	7.8 GB	Maximum	F0

Get started

Copy-paste commands to run Phi 3 Mini 3.8B on your machine.

Run

ollama run phi3:mini

Upgrade options

Hardware that runs Phi 3 Mini 3.8B well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.53.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.53.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

12 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.53.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

Frequently asked questions

Can RX 5700 XT 8GB run Phi 3 Mini 3.8B?

Yes, RX 5700 XT 8GB can run Phi 3 Mini 3.8B at Q3_K_S quantization (Very compromised (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 9.9 GB which exceeds available memory, but at Q3_K_S it needs only 9.4 GB. Expected decode speed: 53.2 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 9.9 GB at Q4_K_M quantization. On RX 5700 XT 8GB, it fits at Q3_K_S using 9.4 GB.

What is the best quantization for Phi 3 Mini 3.8B?

The recommended quantization is Q4_K_M, but on RX 5700 XT 8GB the best fitting quantization is Q3_K_S, which uses 9.4 GB.

What speed will Phi 3 Mini 3.8B run at on RX 5700 XT 8GB?

On RX 5700 XT 8GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q3_K_S quantization.

Can RX 5700 XT 8GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on RX 5700 XT 8GB receives a F grade with 48.4 tok/s and 11K context.

What context window can Phi 3 Mini 3.8B use on RX 5700 XT 8GB?

On RX 5700 XT 8GB, Phi 3 Mini 3.8B can safely use up to 12K tokens of context at Q3_K_S quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Phi 3 Mini 3.8B feels slow on RX 5700 XT 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RX 5700 XT 8GB See all hardware for Phi 3 Mini 3.8B

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

53.2 tok/s

1985 ms

11K

Coding

Too heavy

48.4 tok/s

4003 ms

11K

Agentic Coding

Too heavy

18.1 tok/s

15520 ms

11K

Reasoning

Too heavy

48.4 tok/s

4731 ms

11K

RAG

Too heavy

18.1 tok/s

19401 ms

11K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B68
Q3_K_S	3	1.9 GB	Low	B69
NVFP4	4	2.1 GB	Medium	B69
Q4_K_M	4	2.3 GB	Medium	B69
Q5_K_M	5	2.7 GB	High	A70
Q6_K	6	3.1 GB	High	A71
Q8_0Best for your GPU	8	4.1 GB	Very High	A70
F16	16	7.8 GB	Maximum	F0

Frequently asked questions

Can RX 5700 XT 8GB run Phi 3 Mini 3.8B?

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 9.9 GB at Q4_K_M quantization. On RX 5700 XT 8GB, it fits at Q3_K_S using 9.4 GB.

What is the best quantization for Phi 3 Mini 3.8B?

The recommended quantization is Q4_K_M, but on RX 5700 XT 8GB the best fitting quantization is Q3_K_S, which uses 9.4 GB.

What speed will Phi 3 Mini 3.8B run at on RX 5700 XT 8GB?

On RX 5700 XT 8GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q3_K_S quantization.

Can RX 5700 XT 8GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on RX 5700 XT 8GB receives a F grade with 48.4 tok/s and 11K context.

What context window can Phi 3 Mini 3.8B use on RX 5700 XT 8GB?

On RX 5700 XT 8GB, Phi 3 Mini 3.8B can safely use up to 12K tokens of context at Q3_K_S quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Phi 3 Mini 3.8B feels slow on RX 5700 XT 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.