Would CUDA be a better path than Intel Arc A380 6GB for gemma 2 2b 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 2 2b it run on Intel Arc A380 6GB?

Q: What should I upgrade first if gemma 2 2b it feels slow on Intel Arc A380 6GB?

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

C51Usable

Estimated from fit model

gemma 2 2b it needs ~3.4 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q6_K quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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

Q6_K (High quality) — 3.4 GB, 28.0 tok/s, Runs well

3.4 GB required6.0 GB available

57% VRAM used

Fit status

Runs well

Decode

28.0 tok/s

TTFT

6914 ms

Safe context

195K

Memory

3.4 GB / 6.0 GB

Memory breakdown

Weights1.6 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsgemma 2 2b it on Intel Arc A380 6GB
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	195K
Coding	C	Runs well	28.0 tok/s	6914 ms	195K
Agentic Coding	C	Runs well	28.0 tok/s	10057 ms	195K
Reasoning	C	Runs well	28.0 tok/s	8171 ms	195K
RAG	C	Runs well	28.0 tok/s	12571 ms	195K

Inference speed

gemma 2 2b it inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for gemma 2 2b it at Q6_K across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~38 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q6_K	38.0	Fits
RTX 4090 24GB	24 GB	Q6_K	32.0	Fits
RTX 4080 Super 16GB	16 GB	Q6_K	32.0

Quantization options

How gemma 2 2b it (2B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	C52
Q3_K_S	3	1.0 GB	Low	C53
NVFP4	4

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bartowski/gemma-2-2b-it-GGUF" \
  --hf-file "gemma-2-2b-it-GGUF-Q6_K.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can Intel Arc A380 6GB run gemma 2 2b it?

Yes, Intel Arc A380 6GB can run gemma 2 2b it with a C grade (Runs well). Expected decode speed: 28.0 tok/s.

How much VRAM does gemma 2 2b it need?

gemma 2 2b it (2B parameters) requires approximately 3.4 GB of memory with Q6_K quantization.

What is the best quantization for gemma 2 2b it?

The recommended quantization for gemma 2 2b it is Q6_K, which balances quality and memory efficiency.

What speed will gemma 2 2b it run at on Intel Arc A380 6GB?

On Intel Arc A380 6GB, gemma 2 2b it achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q6_K quantization.

Can Intel Arc A380 6GB run gemma 2 2b it for coding?

For coding workloads, gemma 2 2b it on Intel Arc A380 6GB receives a C grade with 28.0 tok/s and 195K context.

What context window can gemma 2 2b it use on Intel Arc A380 6GB?

On Intel Arc A380 6GB, gemma 2 2b it can safely use up to 195K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if gemma 2 2b it feels slow on Intel Arc A380 6GB?

See all results for Intel Arc A380 6GB See all hardware for gemma 2 2b it

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