Would CUDA be a better path than Intel Arc A770 16GB for Gemma 2 9B?

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 2 9B run on Intel Arc A770 16GB?

Q: What should I upgrade first if Gemma 2 9B 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

B68Good

Estimated from fit model

Gemma 2 9B needs ~13.1 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 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) — 13.1 GB, 36.5 tok/s, Runs well

13.1 GB required16.0 GB available

82% VRAM used

Fit status

Runs well

Decode

36.5 tok/s

TTFT

5300 ms

Safe context

Memory

13.1 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 2 9B 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: 36.5 tok/s decode · 5.3s TTFT (warm) · 91 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	36.5 tok/s	2891 ms	8K
Coding	B	Runs well	36.5 tok/s	5300 ms	8K
Agentic Coding	C	Very compromised (needs ~0.7 GB host RAM)	20.8 tok/s	13550 ms	8K
Reasoning	B	Runs well	36.5 tok/s	6264 ms	8K
RAG	C	Very compromised (needs ~0.7 GB host RAM)	20.8 tok/s	16938 ms

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B62
Q3_K_S	3	4.4 GB	Low	B63
NVFP4	4

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Upgrade options

Hardware that runs Gemma 2 9B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+240)

Raises estimated decode speed by about 91%.69.6 tok/s decode

Raises estimated decode speed by about 91%.

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

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+400)

Raises estimated decode speed by about 137%.86.6 tok/s decode

Raises estimated decode speed by about 137%.

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

~$999 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run Gemma 2 9B?

Yes, Intel Arc A770 16GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 36.5 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 9B?

The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 9B run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Gemma 2 9B achieves approximately 36.5 tokens per second decode speed with a time-to-first-token of 5300ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on Intel Arc A770 16GB receives a B grade with 36.5 tok/s and 8K context.

What context window can Gemma 2 9B use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 2 9B feels slow on Intel Arc A770 16GB?

See all results for Intel Arc A770 16GB See all hardware for Gemma 2 9B

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