Can Phi 3 Mini 3.8B run on NVIDIA H20 96GB?

YES — Runs Great

B61Good

Estimated from fit model

Phi 3 Mini 3.8B needs ~19.0 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~53 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) — 19.0 GB, 53.2 tok/s, Runs well

19.0 GB required96.0 GB available

20% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

128K

Memory

19.0 GB / 96.0 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B on NVIDIA H20 96GB
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: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 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	53.2 tok/s	1985 ms	128K
Coding	B	Runs well	53.2 tok/s	3639 ms	128K
Agentic Coding	B	Runs well	53.2 tok/s	5293 ms	128K
Reasoning	B	Runs well	53.2 tok/s	4301 ms	128K
RAG	B	Runs well	53.2 tok/s	6617 ms	128K

Quantization options

How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B56
Q3_K_S	3	1.9 GB	Low	B56
NVFP4	4

Get started

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

Run

ollama run phi3:mini

Upgrade options

Hardware that runs Phi 3 Mini 3.8B well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+160)

Adds memory headroom for longer context windows and future model growth.53.2 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

NVIDIA DGX Spark 128GBNVIDIA upgrade

128 GB Unified (+32)

This setup is broadly balanced for this model.53.2 tok/s decode

Frequently asked questions

Can NVIDIA H20 96GB run Phi 3 Mini 3.8B?

Yes, NVIDIA H20 96GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 53.2 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 19.0 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 H20 96GB?

On NVIDIA H20 96GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on NVIDIA H20 96GB receives a B grade with 53.2 tok/s and 128K context.

What context window can Phi 3 Mini 3.8B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Phi 3 Mini 3.8B 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 H20 96GB See all hardware for Phi 3 Mini 3.8B

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