Would CUDA be a better path than Intel Arc A580 8GB for Falcon H1 Tiny 90M 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 H1 Tiny 90M Instruct run on Intel Arc A580 8GB?

Q: What should I upgrade first if Falcon H1 Tiny 90M Instruct feels slow on Intel Arc A580 8GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

D35Poor

Estimated from fit model

Falcon H1 Tiny 90M Instruct needs ~1.9 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~2 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 1.9 GB, 2.0 tok/s, Runs well

1.9 GB required8.0 GB available

24% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

999K

Memory

1.9 GB / 8.0 GB

Memory breakdown

Weights0.1 GB

KV Cache0.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsFalcon H1 Tiny 90M Instruct on Intel Arc A580 8GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

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	D	Runs well	2.0 tok/s	52800 ms	500K
Coding	D	Runs well	2.0 tok/s	96800 ms	999K
Agentic Coding	D	Runs well	2.0 tok/s	140800 ms	2.0M
Reasoning	D	Runs well	2.0 tok/s	114400 ms	999K
RAG	D	Runs well	2.0 tok/s	176000 ms	2.0M

Quantization options

How Falcon H1 Tiny 90M Instruct (0.09000000357627869B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.0 GB	Low	C48
Q3_K_S	3	0.0 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run Falcon H1 Tiny 90M Instruct on your machine.

Run

lms load hf-tiiuae--falcon-h1-tiny-90m-instruct-gguf && lms server start

Upgrade options

Hardware that runs Falcon H1 Tiny 90M Instruct well

MacBook Pro M4 16GBBudget pick

16 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.2 tok/s decode

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

~$599 MSRP

MacBook Air M1 16GBBest value

16 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.2 tok/s decode

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

~$999 MSRP

Frequently asked questions

Can Intel Arc A580 8GB run Falcon H1 Tiny 90M Instruct?

Yes, Intel Arc A580 8GB can run Falcon H1 Tiny 90M Instruct with a D grade (Runs well). Expected decode speed: 2.0 tok/s.

How much VRAM does Falcon H1 Tiny 90M Instruct need?

Falcon H1 Tiny 90M Instruct (0.09000000357627869B parameters) requires approximately 1.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Falcon H1 Tiny 90M Instruct?

The recommended quantization for Falcon H1 Tiny 90M Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Falcon H1 Tiny 90M Instruct run at on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Falcon H1 Tiny 90M Instruct achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can Intel Arc A580 8GB run Falcon H1 Tiny 90M Instruct for coding?

For coding workloads, Falcon H1 Tiny 90M Instruct on Intel Arc A580 8GB receives a D grade with 2.0 tok/s and 999K context.

What context window can Falcon H1 Tiny 90M Instruct use on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Falcon H1 Tiny 90M Instruct can safely use up to 999K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A580 8GB See all hardware for Falcon H1 Tiny 90M Instruct

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