Can Agents-A1 4B run on Intel Arc A580 8GB?

YES — Runs Great

A82Great

Estimated from fit model

Agents-A1 4B needs ~6.4 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~63 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) — 4.9 GB, 63.0 tok/s, Runs well

4.9 GB required8.0 GB available

61% VRAM used

Fit status

Runs well

Decode

63.0 tok/s

TTFT

3073 ms

Safe context

116K

Memory

4.9 GB / 8.0 GB

Memory breakdown

Weights2.7 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsAgents-A1 4B on Intel Arc A580 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 63.0 tok/s decode · 3.1s TTFT (warm) · 158 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	A	Runs well	63.0 tok/s	1676 ms	29K
Coding	A	Runs well	63.0 tok/s	3073 ms	29K
Agentic Coding	A	Runs with offload	47.5 tok/s	5934 ms	29K
Reasoning	A	Runs well	63.0 tok/s	3632 ms	29K
RAG	A	Runs with offload	47.5 tok/s	7417 ms	29K

Inference speed

Agents-A1 4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Agents-A1 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~86 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	Q4_K_M	85.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	72.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	72.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	63.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	63.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	63.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	63.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	63.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	63.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	63.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	63.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	63.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	63.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	63.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	63.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	57.4	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Agents-A1 4B (4.5B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	0.6 GB	Very Low	A75
Q2_0_G128	1.71	1.2 GB	Low	A76
Q2_K	2	1.8 GB	Low	A77
Q3_K_S	3	2.2 GB	Low	A78
NVFP4	4	2.5 GB	Medium	A79
Q4_K_M	4	2.7 GB	Medium	A79
Q5_K_M	5	3.2 GB	High	A80
Q6_K	6	3.7 GB	High	A79
Q8_0Best for your GPU	8	4.8 GB	Very High	A79
F16	16	9.2 GB	Maximum	F0

Get started

Copy-paste commands to run Agents-A1 4B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "InternScience/Agents-A1-4B" \
  --hf-file "Agents-A1-4B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your Intel Arc A580 8GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	A	26.3 tok/s
Qwen 3 8B	8B	A	34.1 tok/s
1-bit Bonsai 27B	27B	S	55.6 tok/s
Nemotron Nano 8B	8B	A	36.2 tok/s
InternVL2 8B	8B	A	36.2 tok/s

Frequently asked questions

Can Intel Arc A580 8GB run Agents-A1 4B?

Yes, Intel Arc A580 8GB can run Agents-A1 4B with a A grade (Runs well). Expected decode speed: 63.0 tok/s.

How much VRAM does Agents-A1 4B need?

Agents-A1 4B (4.5B parameters) requires approximately 6.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Agents-A1 4B?

The recommended quantization for Agents-A1 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Agents-A1 4B run at on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Agents-A1 4B achieves approximately 63.0 tokens per second decode speed with a time-to-first-token of 3073ms using Q4_K_M quantization.

Can Intel Arc A580 8GB run Agents-A1 4B for coding?

For coding workloads, Agents-A1 4B on Intel Arc A580 8GB receives a A grade with 63.0 tok/s and 29K context.

What context window can Agents-A1 4B use on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Agents-A1 4B can safely use up to 29K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Agents-A1 4B feels slow on Intel Arc A580 8GB?

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 A580 8GB for Agents-A1 4B?

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 A580 8GB See all hardware for Agents-A1 4B

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