Can Phi 4 Mini 4B run on RX 6600 8GB?

YES — Runs Great

A75Great

Estimated from fit model

Phi 4 Mini 4B needs ~5.6 GB VRAM. RX 6600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 5.6 GB, 48.4 tok/s, Runs well

5.6 GB required8.0 GB available

70% VRAM used

Fit status

Runs well

Decode

48.4 tok/s

TTFT

4004 ms

Safe context

42K

Memory

5.6 GB / 8.0 GB

Memory breakdown

Weights2.4 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsPhi 4 Mini 4B on RX 6600 8GB
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: 48.4 tok/s decode · 4.0s TTFT (warm) · 121 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	A	Runs well	48.4 tok/s	2184 ms	42K
Coding	A	Runs well	48.4 tok/s	4004 ms	42K
Agentic Coding	A	Tight fit	48.4 tok/s	5823 ms	42K
Reasoning	A	Runs well	48.4 tok/s	4731 ms	42K
RAG	A	Tight fit	48.4 tok/s	7279 ms	42K

Inference speed

Phi 4 Mini 4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 4 Mini 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	76.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	64.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	64.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	64.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	56.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	56.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	56.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	56.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	56.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	56.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	56.0	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Phi 4 Mini 4B (4B params) fits at each quantization level on RX 6600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	A72
Q3_K_S	3	2.0 GB	Low	A72
NVFP4	4	2.2 GB	Medium	A73
Q4_K_M	4	2.4 GB	Medium	A73
Q5_K_M	5	2.9 GB	High	A74
Q6_K	6	3.3 GB	High	A74
Q8_0Best for your GPU	8	4.3 GB	Very High	A74
F16	16	8.2 GB	Maximum	F0

Get started

Copy-paste commands to run Phi 4 Mini 4B on your machine.

Run

ollama run phi4-mini

Your hardware

More models your RX 6600 8GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	A	11.5 tok/s
Qwen 3 8B	8B	A	14.9 tok/s
Nemotron Nano 8B	8B	A	15.8 tok/s
InternVL2 8B	8B	A	15.8 tok/s
Ministral 3 8B	8B	B	14.9 tok/s

Frequently asked questions

Can RX 6600 8GB run Phi 4 Mini 4B?

Yes, RX 6600 8GB can run Phi 4 Mini 4B with a A grade (Runs well). Expected decode speed: 48.4 tok/s.

How much VRAM does Phi 4 Mini 4B need?

Phi 4 Mini 4B (4B parameters) requires approximately 5.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 4 Mini 4B?

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

What speed will Phi 4 Mini 4B run at on RX 6600 8GB?

On RX 6600 8GB, Phi 4 Mini 4B achieves approximately 48.4 tokens per second decode speed with a time-to-first-token of 4004ms using Q4_K_M quantization.

Can RX 6600 8GB run Phi 4 Mini 4B for coding?

For coding workloads, Phi 4 Mini 4B on RX 6600 8GB receives a A grade with 48.4 tok/s and 42K context.

What context window can Phi 4 Mini 4B use on RX 6600 8GB?

On RX 6600 8GB, Phi 4 Mini 4B can safely use up to 42K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RX 6600 8GB See all hardware for Phi 4 Mini 4B

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