Would CUDA be a better path than Intel Arc A770 16GB for Phi 3.5 Mini 4B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Phi 3.5 Mini 4B run on Intel Arc A770 16GB?

Q: What should I upgrade first if Phi 3.5 Mini 4B feels slow on Intel Arc A770 16GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

YES — Runs Great

A70Great

Estimated from fit model

Phi 3.5 Mini 4B needs ~10.8 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 10.8 GB, 56.0 tok/s, Runs well

10.8 GB required16.0 GB available

68% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

30K

Memory

10.8 GB / 16.0 GB

Memory breakdown

Weights2.4 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsPhi 3.5 Mini 4B on Intel Arc A770 16GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	56.0 tok/s	1886 ms	30K
Coding	A	Runs well	56.0 tok/s	3457 ms	30K
Agentic Coding	B	Runs with offload	56.0 tok/s	5029 ms	30K
Reasoning	A	Runs well	56.0 tok/s	4086 ms	30K
RAG	B	Runs with offload (needs ~0.1 GB host RAM)	56.0 tok/s	6286 ms	30K

Quantization options

How Phi 3.5 Mini 4B (4B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B62
Q3_K_S	3	2.0 GB	Low	B62
NVFP4	4

Get started

Copy-paste commands to run Phi 3.5 Mini 4B on your machine.

Run

ollama run phi3.5

Your hardware

More models your Intel Arc A770 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	49.3 tok/s
Qwen 3 14B	14B	S	31.9 tok/s

Frequently asked questions

Can Intel Arc A770 16GB run Phi 3.5 Mini 4B?

Yes, Intel Arc A770 16GB can run Phi 3.5 Mini 4B with a A grade (Runs well). Expected decode speed: 56.0 tok/s.

How much VRAM does Phi 3.5 Mini 4B need?

Phi 3.5 Mini 4B (4B parameters) requires approximately 10.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3.5 Mini 4B?

The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3.5 Mini 4B run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Phi 3.5 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run Phi 3.5 Mini 4B for coding?

For coding workloads, Phi 3.5 Mini 4B on Intel Arc A770 16GB receives a A grade with 56.0 tok/s and 30K context.

What context window can Phi 3.5 Mini 4B use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Phi 3.5 Mini 4B can safely use up to 30K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Phi 3.5 Mini 4B feels slow on Intel Arc A770 16GB?

See all results for Intel Arc A770 16GB See all hardware for Phi 3.5 Mini 4B

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