willitrun·ai

Can Phi 3 Medium 14B run on NVIDIA A16 64GB?

YES — Runs Great

B58Good
Estimated from fit model

Phi 3 Medium 14B needs ~19.2 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~59 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) — 19.2 GB, 58.9 tok/s, Runs well
19.2 GB required64.0 GB available
30% VRAM used

Fit status

Runs well

Decode

58.9 tok/s

TTFT

3286 ms

Safe context

128K

Memory

19.2 GB / 64.0 GB

Memory breakdown

Weights8.5 GB
KV Cache3.1 GB
Runtime1.2 GB
Headroom6.4 GB

See how fast it feels

See how fast it feelsPhi 3 Medium 14B on NVIDIA A16 64GB
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: 58.9 tok/s decode · 3.3s TTFT (warm) · 147 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
ChatBRuns well58.9 tok/s1793 ms128K
CodingBRuns well58.9 tok/s3286 ms128K
Agentic CodingBRuns well58.9 tok/s4780 ms128K
ReasoningBRuns well58.9 tok/s3884 ms128K
RAGBRuns well58.9 tok/s5975 ms128K

Inference speed

Phi 3 Medium 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 3 Medium 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M151.1Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M96.4Fits
RX 7900 XTX 24GB
24 GBQ4_K_M87.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M82.5Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M80.7Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M70.1Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M58.4Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M55.4Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M38.1Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M38.1Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M30.2Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M28.4Heavy offload
MacBook Pro M1 Max 64GB
64 GBQ4_K_M27.7Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M23.3Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M16.7Heavy offload
NVIDIARTX 4060 8GB
8 GBQ4_K_M6.1Too 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 Medium 14B (14B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC52
Q3_K_S
3
6.9 GB
LowC52
NVFP4
4
7.8 GB
MediumC52
Q4_K_M
4
8.5 GB
MediumC52
Q5_K_M
5
10.1 GB
HighC52
Q6_K
6
11.5 GB
HighC52
Q8_0
8
15.0 GB
Very HighC53
F16Best for your GPU
16
28.7 GB
MaximumB56

Get started

Copy-paste commands to run Phi 3 Medium 14B on your machine.

Run

ollama run phi3:medium

Opciones de mejora

Hardware que ejecuta bien Phi 3 Medium 14B

NVIDIANVIDIA A100 80GBOpción económica
80 GB VRAM (+16)2039 GB/s (+1439)
B
Sube la velocidad estimada de decodificación alrededor de un 233%.196 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 233%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$15,000 MSRP

NVIDIANVIDIA A800 80GBMejor relación calidad-precio
80 GB VRAM (+16)1935 GB/s (+1335)
B
Sube la velocidad estimada de decodificación alrededor de un 223%.190 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 223%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$15,000 MSRP

NVIDIANVIDIA H800 80GBMejora NVIDIA
80 GB VRAM (+16)3000 GB/s (+2400)
B
Sube la velocidad estimada de decodificación alrededor de un 233%.196 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 233%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Phi 3 Medium 14B?

Yes, NVIDIA A16 64GB can run Phi 3 Medium 14B with a B grade (Runs well). Expected decode speed: 58.9 tok/s.

How much VRAM does Phi 3 Medium 14B need?

Phi 3 Medium 14B (14B parameters) requires approximately 19.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3 Medium 14B?

The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Medium 14B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Phi 3 Medium 14B achieves approximately 58.9 tokens per second decode speed with a time-to-first-token of 3286ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Phi 3 Medium 14B for coding?

For coding workloads, Phi 3 Medium 14B on NVIDIA A16 64GB receives a B grade with 58.9 tok/s and 128K context.

What context window can Phi 3 Medium 14B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Phi 3 Medium 14B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GBSee all hardware for Phi 3 Medium 14B
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