willitrun·ai

Can NousResearch Hermes 4 14B run on Intel Arc Pro B60 24GB?

YES — Runs Great

C50Usable
Estimated from fit model

NousResearch Hermes 4 14B needs ~13.5 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~29 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) — 13.5 GB, 28.8 tok/s, Runs well
13.5 GB required24.0 GB available
56% VRAM used

Fit status

Runs well

Decode

28.8 tok/s

TTFT

6714 ms

Safe context

119K

Memory

13.5 GB / 24.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsNousResearch Hermes 4 14B 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: 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well28.8 tok/s3662 ms119K
CodingCRuns well28.8 tok/s6714 ms119K
Agentic CodingCRuns well28.8 tok/s9766 ms119K
ReasoningCRuns well28.8 tok/s7935 ms119K
RAGCRuns well28.8 tok/s12208 ms119K

Inference speed

NousResearch Hermes 4 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for NousResearch Hermes 4 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M140.6Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M89.7Fits
RX 7900 XTX 24GB
24 GBQ4_K_M80.9Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M76.7Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M75.1Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M65.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M54.3Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M51.5Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M35.4Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M35.4Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M33.2Offloads
MacBook Pro M3 Max 64GB
64 GBQ4_K_M28.1Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M25.8Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M21.7Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M19.5Offloads
NVIDIARTX 4060 8GB
8 GBQ4_K_M7.2Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How NousResearch Hermes 4 14B (14B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC46
Q3_K_S
3
6.9 GB
LowC46
NVFP4
4
7.8 GB
MediumC47
Q4_K_M
4
8.5 GB
MediumC47
Q5_K_M
5
10.1 GB
HighC49
Q6_K
6
11.5 GB
HighC49
Q8_0Best for your GPU
8
15.0 GB
Very HighC50
F16
16
28.7 GB
MaximumF0

Get started

Copy-paste commands to run NousResearch Hermes 4 14B on your machine.

Run

lms load hf-bartowski--nousresearch-hermes-4-14b-gguf && lms server start

升级选项

能流畅运行 NousResearch Hermes 4 14B 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 374%.136.4 tok/s 解码

Raises estimated decode speed by about 374%.

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%.88.1 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 NousResearch Hermes 4 14B?

Yes, Intel Arc Pro B60 24GB can run NousResearch Hermes 4 14B with a C grade (Runs well). Expected decode speed: 28.8 tok/s.

How much VRAM does NousResearch Hermes 4 14B need?

NousResearch Hermes 4 14B (14B parameters) requires approximately 13.5 GB of memory with Q4_K_M quantization.

What is the best quantization for NousResearch Hermes 4 14B?

The recommended quantization for NousResearch Hermes 4 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will NousResearch Hermes 4 14B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, NousResearch Hermes 4 14B achieves approximately 28.8 tokens per second decode speed with a time-to-first-token of 6714ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run NousResearch Hermes 4 14B for coding?

For coding workloads, NousResearch Hermes 4 14B on Intel Arc Pro B60 24GB receives a C grade with 28.8 tok/s and 119K context.

What context window can NousResearch Hermes 4 14B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, NousResearch Hermes 4 14B can safely use up to 119K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if NousResearch Hermes 4 14B 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 NousResearch Hermes 4 14B?

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 NousResearch Hermes 4 14B
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