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URL: https://willitrunai.com/can-run/phi-3.5-mini-4b-on-rx-7600-8gb

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Can Phi 3.5 Mini 4B run on RX 7600 8GB?

YES — With Q3_K_S

C51Usable
○Estimated from fit model

Phi 3.5 Mini 4B needs ~9.5 GB VRAM. RX 7600 8GB has 8.0 GB. With Q3_K_S quantization, expect ~41 tok/s.

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

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 3.5 Mini 4B at Q4_K_M needs 10.0 GB — too much for RX 7600 8GB (8.0 GB). Runs at Q3_K_S (9.5 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.0 GB, exceeds 8.0 GB available
10.0 GB required8.0 GB available
125% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

32.1 tok/s

TTFT

6030 ms

Safe context

11K

Memory

10.0 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights2.4 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 3.5 Mini 4B on RX 7600 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: 32.1 tok/s decode · 6.0s TTFT (warm) · 80 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

WorkloadGradeFitDecodeTTFTContext
ChatBTight fit56.0 tok/s1886 ms11K
CodingFToo heavy32.1 tok/s6030 ms11K
Agentic CodingFToo heavy12.2 tok/s23158 ms11K
ReasoningFToo heavy32.1 tok/s7127 ms11K
RAGFToo heavy12.2 tok/s28948 ms11K

Quantization options

How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RX 7600 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.6 GB
LowB67
Q3_K_S
3
2.0 GB
LowB67
NVFP4
4

Get started

Copy-paste commands to run Phi 3.5 Mini 4B on your machine.

Run

ollama run phi3.5

Upgrade options

Hardware that runs Phi 3.5 Mini 4B well

RX 7600 XT 16GBBudget pick
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.56 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)320 GB/s (+32)
A
Makes the model fit on the accelerator instead of staying completely out of reach.56 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)432 GB/s (+144)
B
Makes the model fit on the accelerator instead of staying completely out of reach.56 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

See all results for RX 7600 8GBSee all hardware for Phi 3.5 Mini 4B
2.2 GB
Medium
B68
Q4_K_M
4
2.4 GB
MediumB68
Q5_K_M
5
2.9 GB
HighB69
Q6_K
6
3.3 GB
HighB69
Q8_0Best for your GPU
8
4.3 GB
Very HighB69
F16
16
8.2 GB
MaximumF0

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.