Would CUDA be a better path than Intel Arc B580 12GB for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

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 GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on Intel Arc B580 12GB?

Q: What should I upgrade first if GGUF SOLARized GraniStral 14B 1902 YeAM HCT feels slow on Intel Arc B580 12GB?

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

C48Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.3 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 12.3 GB, 18.7 tok/s, Runs with offload (needs ~0.2 GB host RAM)

12.3 GB required12.0 GB available

103% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

18.7 tok/s

TTFT

10355 ms

Safe context

13K

Memory

12.3 GB / 12.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on Intel Arc B580 12GB
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: 18.7 tok/s decode · 10.4s TTFT (warm) · 47 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	C	Runs with offload	25.6 tok/s	4120 ms	13K
Coding	C	Runs with offload	18.7 tok/s	10355 ms	13K
Agentic Coding	D	Very compromised (needs ~1.2 GB host RAM)	14.5 tok/s	19452 ms	13K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	18.7 tok/s	12238 ms	13K
RAG	D	Very compromised (needs ~1.2 GB host RAM)	14.5 tok/s	24315 ms

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C52
Q3_K_S	3	6.9 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run GGUF SOLARized GraniStral 14B 1902 YeAM HCT on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-1902-yeam-hct && lms server start

Upgrade options

Hardware that runs GGUF SOLARized GraniStral 14B 1902 YeAM HCT well

Intel Arc A770 16GBBudget pick

16 GB VRAM (+4)560 GB/s (+104)

Raises estimated decode speed by about 58%.29.5 tok/s decode

Raises estimated decode speed by about 58%.

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

~$349 MSRP

Intel Arc Pro B50 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.14.2 tok/s decode

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

~$399 MSRP

Intel Arc Pro B60 24GBIntel upgrade

24 GB VRAM (+12)

Raises estimated decode speed by about 54%.28.8 tok/s decode

Raises estimated decode speed by about 54%.

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

~$599 MSRP

Frequently asked questions

Can Intel Arc B580 12GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, Intel Arc B580 12GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs with offload). Expected decode speed: 18.7 tok/s.

How much VRAM does GGUF SOLARized GraniStral 14B 1902 YeAM HCT need?

GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B parameters) requires approximately 12.3 GB of memory with Q4_K_M quantization.

What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

The recommended quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT is Q4_K_M, which balances quality and memory efficiency.

What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on Intel Arc B580 12GB?

On Intel Arc B580 12GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 18.7 tokens per second decode speed with a time-to-first-token of 10355ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on Intel Arc B580 12GB receives a C grade with 18.7 tok/s and 13K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on Intel Arc B580 12GB?

See all results for Intel Arc B580 12GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

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