Would CUDA be a better path than Intel Arc A770 16GB for gemma 3 27b it?

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 3 27b it run on Intel Arc A770 16GB?

Q: What should I upgrade first if gemma 3 27b it feels slow on Intel Arc A770 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

D31Poor

Estimated from fit model

gemma 3 27b it needs ~18.9 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q3_K_S quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

gemma 3 27b it at Q4_K_M needs 22.1 GB — too much for Intel Arc A770 16GB (16.0 GB). Runs at Q3_K_S (18.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.1 GB, exceeds 16.0 GB available

22.1 GB required16.0 GB available

138% VRAM needed

6.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.8 tok/s

TTFT

33406 ms

Safe context

Memory

22.1 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsgemma 3 27b it 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: 5.8 tok/s decode · 33.4s TTFT (warm) · 15 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.8 tok/s	15588 ms	4K
Coding	F	Too heavy	5.8 tok/s	33406 ms	4K
Agentic Coding	F	Too heavy	4.4 tok/s	64371 ms	4K
Reasoning	F	Too heavy	5.8 tok/s	39479 ms	4K
RAG	F	Too heavy	4.4 tok/s	80464 ms	4K

Quantization options

How gemma 3 27b it (27B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	10.5 GB	Low	C51
Q3_K_S	3	13.2 GB	Low	F0

Get started

Copy-paste commands to run gemma 3 27b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-27b-it-gguf && lms server start

Upgrade options

Hardware that runs gemma 3 27b it well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.15 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$599 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+16)1792 GB/s (+1232)

Makes the model fit on the accelerator instead of staying completely out of reach.52.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+112)3200 GB/s (+2640)

Makes the model fit on the accelerator instead of staying completely out of reach.122.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3140)

Makes the model fit on the accelerator instead of staying completely out of reach.157.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run gemma 3 27b it?

Yes, Intel Arc A770 16GB can run gemma 3 27b it at Q3_K_S quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 22.1 GB which exceeds available memory, but at Q3_K_S it needs only 18.9 GB. Expected decode speed: 9.4 tok/s.

How much VRAM does gemma 3 27b it need?

gemma 3 27b it (27B parameters) requires approximately 22.1 GB at Q4_K_M quantization. On Intel Arc A770 16GB, it fits at Q3_K_S using 18.9 GB.

What is the best quantization for gemma 3 27b it?

The recommended quantization is Q4_K_M, but on Intel Arc A770 16GB the best fitting quantization is Q3_K_S, which uses 18.9 GB.

What speed will gemma 3 27b it run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, gemma 3 27b it achieves approximately 9.4 tokens per second decode speed with a time-to-first-token of 20678ms using Q3_K_S quantization.

Can Intel Arc A770 16GB run gemma 3 27b it for coding?

For coding workloads, gemma 3 27b it on Intel Arc A770 16GB receives a F grade with 5.8 tok/s and 4K context.

What context window can gemma 3 27b it use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, gemma 3 27b it can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A770 16GB See all hardware for gemma 3 27b it

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