Can Phi 4 reasoning vision 15B run on RX 9070 16GB?

YES — Tight Fit

C51Usable

Estimated from fit model

Phi 4 reasoning vision 15B needs ~13.4 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 13.4 GB, 43.4 tok/s, Tight fit

13.4 GB required16.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

43.4 tok/s

TTFT

4464 ms

Safe context

40K

Memory

13.4 GB / 16.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsPhi 4 reasoning vision 15B on RX 9070 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 43.4 tok/s decode · 4.5s TTFT (warm) · 108 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	C	Runs well	43.4 tok/s	2435 ms	40K
Coding	C	Tight fit	43.4 tok/s	4464 ms	40K
Agentic Coding	C	Tight fit	43.4 tok/s	6494 ms	40K
Reasoning	C	Tight fit	43.4 tok/s	5276 ms	40K
RAG	C	Tight fit	43.4 tok/s	8117 ms	40K

Quantization options

How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C50
Q3_K_S	3	7.4 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run Phi 4 reasoning vision 15B on your machine.

Run

lms load hf-jamesburton--phi-4-reasoning-vision-15b-gguf && lms server start

Upgrade options

Hardware that runs Phi 4 reasoning vision 15B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+160)

Adds memory headroom for longer context windows and future model growth.52.5 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+320)

Raises estimated decode speed by about 74%.75.5 tok/s decode

Raises estimated decode speed by about 74%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

AMD Instinct MI100 32GBAMD upgrade

32 GB VRAM (+16)1228 GB/s (+588)

Raises estimated decode speed by about 101%.87.2 tok/s decode

Raises estimated decode speed by about 101%.

Adds memory headroom for longer context windows and future model growth.

~$11,500 MSRP

Frequently asked questions

Can RX 9070 16GB run Phi 4 reasoning vision 15B?

Yes, RX 9070 16GB can run Phi 4 reasoning vision 15B with a C grade (Tight fit). Expected decode speed: 43.4 tok/s.

How much VRAM does Phi 4 reasoning vision 15B need?

Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 4 reasoning vision 15B?

The recommended quantization for Phi 4 reasoning vision 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 4 reasoning vision 15B run at on RX 9070 16GB?

On RX 9070 16GB, Phi 4 reasoning vision 15B achieves approximately 43.4 tokens per second decode speed with a time-to-first-token of 4464ms using Q4_K_M quantization.

Can RX 9070 16GB run Phi 4 reasoning vision 15B for coding?

For coding workloads, Phi 4 reasoning vision 15B on RX 9070 16GB receives a C grade with 43.4 tok/s and 40K context.

What context window can Phi 4 reasoning vision 15B use on RX 9070 16GB?

On RX 9070 16GB, Phi 4 reasoning vision 15B can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 9070 16GB See all hardware for Phi 4 reasoning vision 15B

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