Would CUDA be a better path than Intel Arc Pro A60 12GB for Nous Hermes 2 SOLAR 10.7B?

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 Nous Hermes 2 SOLAR 10.7B run on Intel Arc Pro A60 12GB?

Q: What should I upgrade first if Nous Hermes 2 SOLAR 10.7B feels slow on Intel Arc Pro A60 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 — Tight Fit

C50Usable

Estimated from fit model

Nous Hermes 2 SOLAR 10.7B needs ~9.9 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 9.9 GB, 28.8 tok/s, Tight fit

9.9 GB required12.0 GB available

83% VRAM used

Fit status

Tight fit

Decode

28.8 tok/s

TTFT

6716 ms

Safe context

43K

Memory

9.9 GB / 12.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsNous Hermes 2 SOLAR 10.7B on Intel Arc Pro A60 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: 28.8 tok/s decode · 6.7s TTFT (warm) · 72 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	C	Runs well	28.8 tok/s	3663 ms	43K
Coding	C	Tight fit	28.8 tok/s	6716 ms	43K
Agentic Coding	C	Tight fit	28.8 tok/s	9768 ms	43K
Reasoning	C	Tight fit	28.8 tok/s	7937 ms	43K
RAG	C	Tight fit	28.8 tok/s	12210 ms	43K

Quantization options

How Nous Hermes 2 SOLAR 10.7B (10.699999809265137B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C51
Q3_K_S	3	5.2 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run Nous Hermes 2 SOLAR 10.7B on your machine.

Run

lms load hf-thebloke--nous-hermes-2-solar-10-7b-gguf && lms server start

Upgrade options

Hardware that runs Nous Hermes 2 SOLAR 10.7B well

Intel Arc A770 16GBBudget pick

16 GB VRAM (+4)560 GB/s (+176)

Raises estimated decode speed by about 34%.38.6 tok/s decode

Raises estimated decode speed by about 34%.

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.18.5 tok/s decode

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

~$399 MSRP

Frequently asked questions

Can Intel Arc Pro A60 12GB run Nous Hermes 2 SOLAR 10.7B?

Yes, Intel Arc Pro A60 12GB can run Nous Hermes 2 SOLAR 10.7B with a C grade (Tight fit). Expected decode speed: 28.8 tok/s.

How much VRAM does Nous Hermes 2 SOLAR 10.7B need?

Nous Hermes 2 SOLAR 10.7B (10.699999809265137B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Nous Hermes 2 SOLAR 10.7B?

The recommended quantization for Nous Hermes 2 SOLAR 10.7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nous Hermes 2 SOLAR 10.7B run at on Intel Arc Pro A60 12GB?

On Intel Arc Pro A60 12GB, Nous Hermes 2 SOLAR 10.7B achieves approximately 28.8 tokens per second decode speed with a time-to-first-token of 6716ms using Q4_K_M quantization.

Can Intel Arc Pro A60 12GB run Nous Hermes 2 SOLAR 10.7B for coding?

For coding workloads, Nous Hermes 2 SOLAR 10.7B on Intel Arc Pro A60 12GB receives a C grade with 28.8 tok/s and 43K context.

What context window can Nous Hermes 2 SOLAR 10.7B use on Intel Arc Pro A60 12GB?

On Intel Arc Pro A60 12GB, Nous Hermes 2 SOLAR 10.7B can safely use up to 43K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc Pro A60 12GB See all hardware for Nous Hermes 2 SOLAR 10.7B

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