Can Phi 3 Mini 3.8B run on NVIDIA L20 48GB?

YES — Runs Great

B64Good

Estimated from fit model

Phi 3 Mini 3.8B needs ~13.9 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~61 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 13.9 GB, 60.8 tok/s, Runs well

13.9 GB required48.0 GB available

29% VRAM used

Fit status

Runs well

Decode

60.8 tok/s

TTFT

3184 ms

Safe context

109K

Memory

13.9 GB / 48.0 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on NVIDIA L20 48GB
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: 60.8 tok/s decode · 3.2s TTFT (warm) · 152 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	B	Runs well	60.8 tok/s	1737 ms	109K
Coding	B	Runs well	60.8 tok/s	3184 ms	109K
Agentic Coding	B	Runs well	60.8 tok/s	4632 ms	109K
Reasoning	B	Runs well	60.8 tok/s	3763 ms	109K
RAG	B	Runs well	60.8 tok/s	5789 ms	109K

Inference speed

Phi 3 Mini 3.8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 3 Mini 3.8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~72 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	72.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	60.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	60.8	Fits
RTX 4070 12GB	12 GB	Q4_K_M	60.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	53.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	53.2	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	53.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	53.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	53.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	53.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	38.7	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 Mini 3.8B (3.799999952316284B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B58
Q3_K_S	3	1.9 GB	Low	B58
NVFP4	4	2.1 GB	Medium	B58
Q4_K_M	4	2.3 GB	Medium	B58
Q5_K_M	5	2.7 GB	High	B58
Q6_K	6	3.1 GB	High	B58
Q8_0	8	4.1 GB	Very High	B59
F16Best for your GPU	16	7.8 GB	Maximum	B59

Get started

Copy-paste commands to run Phi 3 Mini 3.8B on your machine.

Run

ollama run phi3:mini

Frequently asked questions

Can NVIDIA L20 48GB run Phi 3 Mini 3.8B?

Yes, NVIDIA L20 48GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 60.8 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 13.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3 Mini 3.8B?

The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Mini 3.8B run at on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Phi 3 Mini 3.8B achieves approximately 60.8 tokens per second decode speed with a time-to-first-token of 3184ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on NVIDIA L20 48GB receives a B grade with 60.8 tok/s and 109K context.

What context window can Phi 3 Mini 3.8B use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Phi 3 Mini 3.8B can safely use up to 109K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA L20 48GB See all hardware for Phi 3 Mini 3.8B

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