Can Phi 4 reasoning vision 15B run on Radeon RX 6850M XT 12GB?

Q: What should I upgrade first if Phi 4 reasoning vision 15B feels slow on Radeon RX 6850M XT 12GB?

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.

BARELY — Tight on Memory

D38Poor

Estimated from fit model

Phi 4 reasoning vision 15B needs ~13.0 GB VRAM. Radeon RX 6850M XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~17 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.0 GB, 16.7 tok/s, Very compromised (needs ~0.7 GB host RAM)

13.0 GB required12.0 GB available

108% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

16.7 tok/s

TTFT

11570 ms

Safe context

Memory

13.0 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsPhi 4 reasoning vision 15B on Radeon RX 6850M XT 12GB
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: 16.7 tok/s decode · 11.6s TTFT (warm) · 42 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 10% 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 {ram} 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	C	Runs with offload (needs ~0.1 GB host RAM)	19.4 tok/s	5447 ms	7K
Coding	D	Very compromised	16.7 tok/s	11570 ms	7K
Agentic Coding	F	Too heavy	12.8 tok/s	21975 ms	7K
Reasoning	D	Very compromised (needs ~0.7 GB host RAM)	16.7 tok/s	13673 ms	7K
RAG	F	Too heavy	12.8 tok/s	27468 ms

Quantization options

How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on Radeon RX 6850M XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C52
Q3_K_S	3	7.4 GB	Low	C52
NVFP4Best for your GPU

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 7600 XT 16GBBudget pick

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.18.3 tok/s decode

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

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

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.22 tok/s decode

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

Raises estimated decode speed by about 32%.

~$349 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+4)640 GB/s (+208)

Removes host-memory offload, which is usually the single biggest latency and throughput win.43.4 tok/s decode

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

Raises estimated decode speed by about 160%.

~$479 MSRP

Frequently asked questions

Can Radeon RX 6850M XT 12GB run Phi 4 reasoning vision 15B?

Yes, Radeon RX 6850M XT 12GB can run Phi 4 reasoning vision 15B with a D grade (Very compromised). Expected decode speed: 16.7 tok/s.

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

Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.0 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 Radeon RX 6850M XT 12GB?

On Radeon RX 6850M XT 12GB, Phi 4 reasoning vision 15B achieves approximately 16.7 tokens per second decode speed with a time-to-first-token of 11570ms using Q4_K_M quantization.

Can Radeon RX 6850M XT 12GB run Phi 4 reasoning vision 15B for coding?

For coding workloads, Phi 4 reasoning vision 15B on Radeon RX 6850M XT 12GB receives a D grade with 16.7 tok/s and 7K context.

What context window can Phi 4 reasoning vision 15B use on Radeon RX 6850M XT 12GB?

On Radeon RX 6850M XT 12GB, Phi 4 reasoning vision 15B can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 6850M XT 12GB See all hardware for Phi 4 reasoning vision 15B

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