Can Phi 3 Mini 3.8B run on RX 7900 XTX 24GB?

YES — Runs Great

B67Good

Estimated from fit model

Phi 3 Mini 3.8B needs ~11.5 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.5 GB, 53.2 tok/s, Runs well

11.5 GB required24.0 GB available

48% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

50K

Memory

11.5 GB / 24.0 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on RX 7900 XTX 24GB
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: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	53.2 tok/s	1985 ms	50K
Coding	B	Runs well	53.2 tok/s	3639 ms	50K
Agentic Coding	A	Runs well	53.2 tok/s	5293 ms	50K
Reasoning	B	Runs well	53.2 tok/s	4301 ms	50K
RAG	A	Runs well	53.2 tok/s	6617 ms	50K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B61
Q3_K_S	3	1.9 GB	Low	B61
NVFP4	4	2.1 GB	Medium	B61
Q4_K_M	4	2.3 GB	Medium	B61
Q5_K_M	5	2.7 GB	High	B61
Q6_K	6	3.1 GB	High	B62
Q8_0	8	4.1 GB	Very High	B62
F16Best for your GPU	16	7.8 GB	Maximum	B64

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

MacBook Pro M3 Pro 36GBBudget pick

36 GB Unified (+12)

This setup is broadly balanced for this model.47.2 tok/s decode

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+12)

This setup is broadly balanced for this model.53.2 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can RX 7900 XTX 24GB run Phi 3 Mini 3.8B?

Yes, RX 7900 XTX 24GB can run Phi 3 Mini 3.8B with a B grade (Runs well). 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 11.5 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Mini 3.8B run at on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.

Can RX 7900 XTX 24GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on RX 7900 XTX 24GB receives a B grade with 53.2 tok/s and 50K context.

What context window can Phi 3 Mini 3.8B use on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Phi 3 Mini 3.8B can safely use up to 50K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RX 7900 XTX 24GB See all hardware for Phi 3 Mini 3.8B

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<iframe src="https://willitrunai.com/embed/phi-3-mini-3.8b-on-rx-7900-xtx-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

53.2 tok/s

1985 ms

50K

Coding

Runs well

53.2 tok/s

3639 ms

50K

Agentic Coding

Runs well

53.2 tok/s

5293 ms

50K

Reasoning

Runs well

53.2 tok/s

4301 ms

50K

RAG

Runs well

53.2 tok/s

6617 ms

50K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B61
Q3_K_S	3	1.9 GB	Low	B61
NVFP4	4	2.1 GB	Medium	B61
Q4_K_M	4	2.3 GB	Medium	B61
Q5_K_M	5	2.7 GB	High	B61
Q6_K	6	3.1 GB	High	B62
Q8_0	8	4.1 GB	Very High	B62
F16Best for your GPU	16	7.8 GB	Maximum	B64

Frequently asked questions

Can RX 7900 XTX 24GB run Phi 3 Mini 3.8B?

Yes, RX 7900 XTX 24GB can run Phi 3 Mini 3.8B with a B grade (Runs well). 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 11.5 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Mini 3.8B run at on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.

Can RX 7900 XTX 24GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on RX 7900 XTX 24GB receives a B grade with 53.2 tok/s and 50K context.

What context window can Phi 3 Mini 3.8B use on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Phi 3 Mini 3.8B can safely use up to 50K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.