Would CUDA be a better path than Intel Arc A380 6GB for Falcon 7B Instruct?

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 Falcon 7B Instruct run on Intel Arc A380 6GB?

Q: What should I upgrade first if Falcon 7B Instruct 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 — With Offload

B65Good

Estimated from fit model

Falcon 7B Instruct needs ~5.9 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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

Q4_K_M (Medium quality) — 5.9 GB, 23.4 tok/s, Runs with offload

5.9 GB required6.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

23.4 tok/s

TTFT

8256 ms

Safe context

Memory

5.9 GB / 6.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.1 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsFalcon 7B Instruct on Intel Arc A380 6GB
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: 23.4 tok/s decode · 8.3s TTFT (warm) · 59 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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	B	Runs with offload	23.4 tok/s	4503 ms	8K
Coding	B	Runs with offload	21.3 tok/s	9070 ms	8K
Agentic Coding	B	Runs with offload (needs ~0 GB host RAM)	17.5 tok/s	16092 ms	8K
Reasoning	B	Runs with offload	23.4 tok/s	9758 ms	8K
RAG	B	Runs with offload (needs ~0 GB host RAM)	17.5 tok/s	20115 ms

Quantization options

How Falcon 7B Instruct (7B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_SBest for your GPU	3	3.4 GB	Low	B70

Get started

Copy-paste commands to run Falcon 7B Instruct on your machine.

Run

lms load falcon-7b-instruct && lms server start

Upgrade options

Hardware that runs Falcon 7B Instruct well

Intel Arc A580 8GBBudget pick

8 GB VRAM (+2)512 GB/s (+326)

Raises estimated decode speed by about 176%.64.5 tok/s decode

Raises estimated decode speed by about 176%.

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

~$179 MSRP

Intel Arc B570 10GBBest value

10 GB VRAM (+4)380 GB/s (+194)

Raises estimated decode speed by about 126%.52.8 tok/s decode

Raises estimated decode speed by about 126%.

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

~$219 MSRP

Intel Arc B580 12GBIntel upgrade

12 GB VRAM (+6)456 GB/s (+270)

Raises estimated decode speed by about 141%.56.3 tok/s decode

Raises estimated decode speed by about 141%.

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

~$249 MSRP

Frequently asked questions

Can Intel Arc A380 6GB run Falcon 7B Instruct?

Yes, Intel Arc A380 6GB can run Falcon 7B Instruct with a B grade (Runs with offload). Expected decode speed: 21.3 tok/s.

How much VRAM does Falcon 7B Instruct need?

Falcon 7B Instruct (7B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Falcon 7B Instruct?

The recommended quantization for Falcon 7B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Falcon 7B Instruct run at on Intel Arc A380 6GB?

On Intel Arc A380 6GB, Falcon 7B Instruct achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9070ms using Q4_K_M quantization.

Can Intel Arc A380 6GB run Falcon 7B Instruct for coding?

For coding workloads, Falcon 7B Instruct on Intel Arc A380 6GB receives a B grade with 21.3 tok/s and 8K context.

What context window can Falcon 7B Instruct use on Intel Arc A380 6GB?

On Intel Arc A380 6GB, Falcon 7B Instruct 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 Falcon 7B Instruct feels slow on Intel Arc A380 6GB?

See all results for Intel Arc A380 6GB See all hardware for Falcon 7B Instruct

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