Would CUDA be a better path than Intel Arc A770 16GB for gemma 2b?

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 gemma 2b run on Intel Arc A770 16GB?

YES — Runs Great

C44Usable

Estimated from fit model

gemma 2b needs ~4.0 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 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) — 4.0 GB, 28.0 tok/s, Runs well

4.0 GB required16.0 GB available

25% VRAM used

Fit status

Runs well

Decode

28.0 tok/s

TTFT

6914 ms

Safe context

838K

Memory

4.0 GB / 16.0 GB

Memory breakdown

Weights1.2 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsgemma 2b 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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 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	C	Runs well	28.0 tok/s	3771 ms	838K
Coding	C	Runs well	28.0 tok/s	6914 ms	838K
Agentic Coding	C	Runs well	28.0 tok/s	10057 ms	838K
Reasoning	C	Runs well	28.0 tok/s	8171 ms	838K
RAG	C	Runs well	28.0 tok/s	12571 ms	838K

Quantization options

How gemma 2b (2B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	C46
Q3_K_S	3	1.0 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run gemma 2b on your machine.

Run

lms load hf-google--gemma-2b && lms server start

Upgrade options

Hardware that runs gemma 2b well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.28 tok/s decode

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

~$799 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.28 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run gemma 2b?

Yes, Intel Arc A770 16GB can run gemma 2b with a C grade (Runs well). Expected decode speed: 28.0 tok/s.

How much VRAM does gemma 2b need?

gemma 2b (2B parameters) requires approximately 4.0 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 2b?

The recommended quantization for gemma 2b is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 2b run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, gemma 2b achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run gemma 2b for coding?

For coding workloads, gemma 2b on Intel Arc A770 16GB receives a C grade with 28.0 tok/s and 838K context.

What context window can gemma 2b use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, gemma 2b can safely use up to 838K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if gemma 2b 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.

See all results for Intel Arc A770 16GB See all hardware for gemma 2b

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