Will It Run AI

Can Nous Hermes 2 SOLAR 10.7B run on Intel Arc Pro B60 24GB?

YES — Runs Great

C49Usable
Estimated from fit model

Nous Hermes 2 SOLAR 10.7B needs ~11.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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) — 11.1 GB, 37.7 tok/s, Runs well
11.1 GB required24.0 GB available
46% VRAM used

Fit status

Runs well

Decode

37.7 tok/s

TTFT

5132 ms

Safe context

181K

Memory

11.1 GB / 24.0 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsNous Hermes 2 SOLAR 10.7B on Intel Arc Pro B60 24GB
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: 37.7 tok/s decode · 5.1s TTFT (warm) · 94 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well37.7 tok/s2799 ms181K
CodingCRuns well37.7 tok/s5132 ms181K
Agentic CodingCRuns well37.7 tok/s7464 ms181K
ReasoningCRuns well37.7 tok/s6065 ms181K
RAGCRuns well37.7 tok/s9330 ms181K

Quantization options

How Nous Hermes 2 SOLAR 10.7B (10.699999809265137B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC45
Q3_K_S
3
5.2 GB
LowC45
NVFP4
4
6.0 GB
MediumC46
Q4_K_M
4
6.5 GB
MediumC46
Q5_K_M
5
7.7 GB
HighC47
Q6_K
6
8.8 GB
HighC48
Q8_0Best for your GPU
8
11.4 GB
Very HighC49
F16
16
21.9 GB
MaximumF0

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

升级选项

能流畅运行 Nous Hermes 2 SOLAR 10.7B 的硬件

NVIDIARTX 5090 32GB经济之选
32 GB VRAM (+8)1792 GB/s (+1336)
C
Raises estimated decode speed by about 373%.178.4 tok/s 解码

Raises estimated decode speed by about 373%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GB最佳性价比
32 GB VRAM (+8)896 GB/s (+440)
C
Raises estimated decode speed by about 206%.115.3 tok/s 解码

Raises estimated decode speed by about 206%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Nous Hermes 2 SOLAR 10.7B?

Yes, Intel Arc Pro B60 24GB can run Nous Hermes 2 SOLAR 10.7B with a C grade (Runs well). Expected decode speed: 37.7 tok/s.

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

Nous Hermes 2 SOLAR 10.7B (10.699999809265137B parameters) requires approximately 11.1 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 B60 24GB?

On Intel Arc Pro B60 24GB, Nous Hermes 2 SOLAR 10.7B achieves approximately 37.7 tokens per second decode speed with a time-to-first-token of 5132ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Nous Hermes 2 SOLAR 10.7B for coding?

For coding workloads, Nous Hermes 2 SOLAR 10.7B on Intel Arc Pro B60 24GB receives a C grade with 37.7 tok/s and 181K context.

What context window can Nous Hermes 2 SOLAR 10.7B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Nous Hermes 2 SOLAR 10.7B can safely use up to 181K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Nous Hermes 2 SOLAR 10.7B feels slow on Intel Arc Pro B60 24GB?

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 Pro B60 24GB 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.

See all results for Intel Arc Pro B60 24GBSee all hardware for Nous Hermes 2 SOLAR 10.7B
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