willitrun·ai

Can Phi-4-reasoning-plus 14B run on NVIDIA A10 24GB?

YES — Runs Great

S94Excellent
Estimated from fit model

Phi-4-reasoning-plus 14B needs ~15.6 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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) — 15.6 GB, 56.1 tok/s, Runs well
15.6 GB required24.0 GB available
65% VRAM used

Fit status

Runs well

Decode

56.1 tok/s

TTFT

3451 ms

Safe context

33K

Memory

15.6 GB / 24.0 GB

Memory breakdown

Weights9.0 GB
KV Cache3.1 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on NVIDIA A10 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: 56.1 tok/s decode · 3.5s TTFT (warm) · 140 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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well56.1 tok/s1882 ms33K
CodingSRuns well56.1 tok/s3451 ms33K
Agentic CodingSRuns well56.1 tok/s5019 ms33K
ReasoningSRuns well56.1 tok/s4078 ms33K
RAGSRuns well56.1 tok/s6274 ms33K

Inference speed

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

Estimated decode speed (tokens/sec) for Phi-4-reasoning-plus 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~144 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M143.9Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M91.8Fits
RX 7900 XTX 24GB
24 GBQ4_K_M82.9Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M78.5Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M75.5Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M66.8Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M55.6Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M52.7Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M37.6Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M37.6Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M28.8Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M26.4Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M24.9Heavy offload
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M23.0Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M14.6Heavy offload
NVIDIARTX 4060 8GB
8 GBQ4_K_M5.5Too 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-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.7 GB
LowS86
Q3_K_S
3
7.2 GB
LowS86
NVFP4
4
8.2 GB
MediumS87
Q4_K_M
4
9.0 GB
MediumS88
Q5_K_M
5
10.6 GB
HighS89
Q6_K
6
12.1 GB
HighS90
Q8_0Best for your GPU
8
15.7 GB
Very HighS90
F16
16
30.1 GB
MaximumF0

Get started

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

Run

ollama run phi4-reasoning

Your hardware

More models your NVIDIA A10 24GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BS70.8 tok/s
AlibabaQwen 3.5 27B27BS30.7 tok/s
AlibabaQwen 3.6 27B27BS30.8 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BS73.2 tok/s
AlibabaQwen 3.5 35B A3B35BA39.6 tok/s

Frequently asked questions

Can NVIDIA A10 24GB run Phi-4-reasoning-plus 14B?

Yes, NVIDIA A10 24GB can run Phi-4-reasoning-plus 14B with a S grade (Runs well). Expected decode speed: 56.1 tok/s.

How much VRAM does Phi-4-reasoning-plus 14B need?

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.

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

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

What speed will Phi-4-reasoning-plus 14B run at on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Phi-4-reasoning-plus 14B achieves approximately 56.1 tokens per second decode speed with a time-to-first-token of 3451ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on NVIDIA A10 24GB receives a S grade with 56.1 tok/s and 33K context.

What context window can Phi-4-reasoning-plus 14B use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Phi-4-reasoning-plus 14B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA A10 24GBSee all hardware for Phi-4-reasoning-plus 14B
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