Will It Run AI

Can TinyLlama 1.1B run on Intel Arc A380 6GB?

YES — Runs Great

B57Good
Estimated from fit model

TinyLlama 1.1B needs ~2.5 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 2.5 GB, 15.4 tok/s, Runs well
2.5 GB required6.0 GB available
42% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12571 ms

Safe context

4K

Memory

2.5 GB / 6.0 GB

Memory breakdown

Weights0.7 GB
KV Cache0.3 GB
Runtime0.9 GB
Headroom0.6 GB

See how fast it feels

See how fast it feelsTinyLlama 1.1B 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: 15.4 tok/s decode · 12.6s TTFT (warm) · 39 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well15.4 tok/s6857 ms4K
CodingBRuns well15.4 tok/s12571 ms4K
Agentic CodingBRuns well15.4 tok/s18286 ms4K
ReasoningBRuns well15.4 tok/s14857 ms4K
RAGBRuns well15.4 tok/s22857 ms4K

Quantization options

How TinyLlama 1.1B (1.100000023841858B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.4 GB
LowB63
Q3_K_S
3
0.5 GB
LowB63
NVFP4
4
0.6 GB
MediumB63
Q4_K_M
4
0.7 GB
MediumB63
Q5_K_M
5
0.8 GB
HighB64
Q6_K
6
0.9 GB
HighB64
Q8_0
8
1.2 GB
Very HighB65
F16Best for your GPU
16
2.3 GB
MaximumB66

Get started

Copy-paste commands to run TinyLlama 1.1B on your machine.

Run

ollama run tinyllama

Frequently asked questions

Can Intel Arc A380 6GB run TinyLlama 1.1B?

Yes, Intel Arc A380 6GB can run TinyLlama 1.1B with a B grade (Runs well). Expected decode speed: 15.4 tok/s.

How much VRAM does TinyLlama 1.1B need?

TinyLlama 1.1B (1.100000023841858B parameters) requires approximately 2.5 GB of memory with Q4_K_M quantization.

What is the best quantization for TinyLlama 1.1B?

The recommended quantization for TinyLlama 1.1B is Q4_K_M, which balances quality and memory efficiency.

What speed will TinyLlama 1.1B run at on Intel Arc A380 6GB?

On Intel Arc A380 6GB, TinyLlama 1.1B achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.

Can Intel Arc A380 6GB run TinyLlama 1.1B for coding?

For coding workloads, TinyLlama 1.1B on Intel Arc A380 6GB receives a B grade with 15.4 tok/s and 4K context.

What context window can TinyLlama 1.1B use on Intel Arc A380 6GB?

On Intel Arc A380 6GB, TinyLlama 1.1B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if TinyLlama 1.1B 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.

Would CUDA be a better path than Intel Arc A380 6GB for TinyLlama 1.1B?

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.

See all results for Intel Arc A380 6GBSee all hardware for TinyLlama 1.1B
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