Can Phi-4 14B run on NVIDIA A30 24GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Phi-4 14B needs ~15.2 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q4_K_M quantization, expect ~92 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 15.2 GB, 91.6 tok/s, Runs well

15.2 GB required24.0 GB available

63% VRAM used

Fit status

Runs well

Decode

91.6 tok/s

TTFT

2113 ms

Safe context

16K

Memory

15.2 GB / 24.0 GB

Memory breakdown

Weights8.5 GB

KV Cache3.1 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsPhi-4 14B on NVIDIA A30 24GB
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: 91.6 tok/s decode · 2.1s TTFT (warm) · 229 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	S	Runs well	91.6 tok/s	1153 ms	16K
Coding	S	Runs well	91.6 tok/s	2113 ms	16K
Agentic Coding	S	Runs well	91.6 tok/s	3074 ms	16K
Reasoning	S	Runs well	91.6 tok/s	2498 ms	16K
RAG	S	Runs well	91.6 tok/s	3843 ms	16K

Inference speed

Phi-4 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi-4 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 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	151.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	82.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	80.7	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.1	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	28.4	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.3	Fits
RTX 3060 12GB	12 GB	Q4_K_M	16.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	6.1	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-4 14B (14B params) fits at each quantization level on NVIDIA A30 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A77
Q3_K_S	3	6.9 GB	Low	A78
NVFP4	4	7.8 GB	Medium	A79
Q4_K_M	4	8.5 GB	Medium	A79
Q5_K_M	5	10.1 GB	High	A80
Q6_K	6	11.5 GB	High	A81
Q8_0Best for your GPU	8	15.0 GB	Very High	A82
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Phi-4 14B on your machine.

Run

ollama run phi4

Your hardware

More models your NVIDIA A30 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	110 tok/s
Qwen 3.5 27B	27B	S	47.7 tok/s
Qwen 3.6 27B	27B	S	47.9 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	113.8 tok/s
Qwen 3.5 35B A3B	35B	A	61.6 tok/s

Frequently asked questions

Can NVIDIA A30 24GB run Phi-4 14B?

Yes, NVIDIA A30 24GB can run Phi-4 14B with a S grade (Runs well). Expected decode speed: 91.6 tok/s.

How much VRAM does Phi-4 14B need?

Phi-4 14B (14B parameters) requires approximately 15.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4 14B?

The recommended quantization for Phi-4 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4 14B run at on NVIDIA A30 24GB?

On NVIDIA A30 24GB, Phi-4 14B achieves approximately 91.6 tokens per second decode speed with a time-to-first-token of 2113ms using Q4_K_M quantization.

Can NVIDIA A30 24GB run Phi-4 14B for coding?

For coding workloads, Phi-4 14B on NVIDIA A30 24GB receives a S grade with 91.6 tok/s and 16K context.

What context window can Phi-4 14B use on NVIDIA A30 24GB?

On NVIDIA A30 24GB, Phi-4 14B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for NVIDIA A30 24GB See all hardware for Phi-4 14B

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