Can Phi 3.5 Mini 4B run on RX 5600 XT 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Phi 3.5 Mini 4B needs ~9.8 GB but RX 5600 XT 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 9.8 GB, exceeds 6.0 GB available

9.8 GB required6.0 GB available

163% VRAM needed

3.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

16.4 tok/s

TTFT

11811 ms

Safe context

Memory

9.8 GB / 6.0 GB

Offload

40%

Memory breakdown

Weights2.4 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom0.6 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 5600 XT 6GB
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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 9.8 GB, but this setup only exposes 6.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Very compromised (needs ~0.3 GB host RAM)	34.6 tok/s	3050 ms	6K
Coding	F	Too heavy	16.4 tok/s	11811 ms	6K
Agentic Coding	F	Too heavy	9.2 tok/s	30587 ms	6K
Reasoning	F	Too heavy	16.4 tok/s	13959 ms	6K
RAG	F	Too heavy	9.2 tok/s	38234 ms	6K

Inference speed

Phi 3.5 Mini 4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 3.5 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 4070 12GB	12 GB	Q4_K_M	64.0	Tight
RTX 3090 24GB	24 GB	Q4_K_M	56.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	56.0	Tight
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
RTX 4060 8GB	8 GB	Q4_K_M	35.9	Too big

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 3.5 Mini 4B (4B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B70
Q3_K_S	3	2.0 GB	Low	A70
NVFP4	4	2.2 GB	Medium	A70
Q4_K_M	4	2.4 GB	Medium	A70
Q5_K_M	5	2.9 GB	High	B70
Q6_KBest for your GPU	6	3.3 GB	High	B69
Q8_0	8	4.3 GB	Very High	F0
F16	16	8.2 GB	Maximum	F0

アップグレードオプション

Phi 3.5 Mini 4Bを快適に動かすハードウェア

RX 7600 XT 16GBコスパ重視

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/sデコード

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 16GBコスパ最良

16 GB VRAM (+10)320 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/sデコード

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アップグレード

12 GB VRAM (+6)432 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/sデコード

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

Can RX 5600 XT 6GB run Phi 3.5 Mini 4B?

No, Phi 3.5 Mini 4B requires more memory than RX 5600 XT 6GB provides.

How much VRAM does Phi 3.5 Mini 4B need?

Phi 3.5 Mini 4B (4B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3.5 Mini 4B?

The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3.5 Mini 4B run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, Phi 3.5 Mini 4B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11811ms using Q4_K_M quantization.

Can RX 5600 XT 6GB run Phi 3.5 Mini 4B for coding?

For coding workloads, Phi 3.5 Mini 4B on RX 5600 XT 6GB receives a F grade with 16.4 tok/s and 6K context.

What context window can Phi 3.5 Mini 4B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, Phi 3.5 Mini 4B can safely use up to 6K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Phi 3.5 Mini 4B feels slow on RX 5600 XT 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RX 5600 XT 6GB See all hardware for Phi 3.5 Mini 4B

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