Can Nous Hermes 1.0 run on Intel Arc A770 16GB?

YES — With Q3_K_S

C55Usable

○Estimated from fit model

Nous Hermes 1.0 needs ~19.1 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q3_K_S quantization, expect ~27 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.

Nous Hermes 1.0 at Q4_K_M needs 20.2 GB — too much for Intel Arc A770 16GB (16.0 GB). Runs at Q3_K_S (19.1 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 20.2 GB, exceeds 16.0 GB available

20.2 GB required16.0 GB available

126% VRAM needed

4.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

21.1 tok/s

TTFT

9183 ms

Safe context

10K

Memory

20.2 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights5.5 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNous Hermes 1.0 on Intel Arc A770 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 21.1 tok/s decode · 9.2s TTFT (warm) · 53 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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	A	Tight fit	45.9 tok/s	2301 ms	10K
Coding	F	Too heavy	21.1 tok/s	9183 ms	10K
Agentic Coding	F	Too heavy	7.8 tok/s	36131 ms	10K
Reasoning	F	Too heavy	21.1 tok/s	10852 ms	10K
RAG	F	Too heavy	7.8 tok/s	45164 ms	10K

Quantization options

How Nous Hermes 1.0 (9B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B68
Q3_K_S	3	4.4 GB	Low	B69
NVFP4	4

Get started

Copy-paste commands to run Nous Hermes 1.0 on your machine.

Run

lms load Nous-Hermes-1.0 && lms server start

Upgrade options

Hardware that runs Nous Hermes 1.0 well

👁 Intel
Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.44.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$599 MSRP

👁 Intel
Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+112)3200 GB/s (+2640)

Makes the model fit on the accelerator instead of staying completely out of reach.126 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

👁 Intel
Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3140)

Makes the model fit on the accelerator instead of staying completely out of reach.126 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

See all results for Intel Arc A770 16GB See all hardware for Nous Hermes 1.0

URL: https://willitrunai.com/can-run/nous-hermes-1.0-on-arc-a770-16gb

⇱

Can Nous Hermes 1.0 run on Intel Arc A770 16GB?

YES — With Q3_K_S

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Nous Hermes 1.0 well

Frequently asked questions