Can Nous Hermes 1.0 run on RX 7600 XT 16GB?

Q: What should I upgrade first if Nous Hermes 1.0 feels slow on RX 7600 XT 16GB?

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.

YES — With Q3_K_S

C53Usable

Estimated from fit model

Nous Hermes 1.0 needs ~19.1 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q3_K_S quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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 RX 7600 XT 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

14.0 tok/s

TTFT

13853 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 RX 7600 XT 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: 14.0 tok/s decode · 13.9s TTFT (warm) · 35 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.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	30.4 tok/s	3471 ms	10K
Coding	F	Too heavy	14.0 tok/s	13853 ms	10K
Agentic Coding	F	Too heavy	5.2 tok/s	54508 ms	10K
Reasoning	F	Too heavy	14.0 tok/s	16372 ms	10K
RAG	F	Too heavy	5.2 tok/s	68135 ms	10K

Quantization options

How Nous Hermes 1.0 (9B params) fits at each quantization level on RX 7600 XT 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

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+512)

Makes the model fit on the accelerator instead of staying completely out of reach.61.6 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.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.125.9 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.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.68.8 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.

~$1,899 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run Nous Hermes 1.0?

Yes, RX 7600 XT 16GB can run Nous Hermes 1.0 at Q3_K_S quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 20.2 GB which exceeds available memory, but at Q3_K_S it needs only 19.1 GB. Expected decode speed: 18.2 tok/s.

How much VRAM does Nous Hermes 1.0 need?

Nous Hermes 1.0 (9B parameters) requires approximately 20.2 GB at Q4_K_M quantization. On RX 7600 XT 16GB, it fits at Q3_K_S using 19.1 GB.

What is the best quantization for Nous Hermes 1.0?

The recommended quantization is Q4_K_M, but on RX 7600 XT 16GB the best fitting quantization is Q3_K_S, which uses 19.1 GB.

What speed will Nous Hermes 1.0 run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Nous Hermes 1.0 achieves approximately 18.2 tokens per second decode speed with a time-to-first-token of 10659ms using Q3_K_S quantization.

Can RX 7600 XT 16GB run Nous Hermes 1.0 for coding?

For coding workloads, Nous Hermes 1.0 on RX 7600 XT 16GB receives a F grade with 14.0 tok/s and 10K context.

What context window can Nous Hermes 1.0 use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Nous Hermes 1.0 can safely use up to 12K tokens of context at Q3_K_S quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GB See all hardware for Nous Hermes 1.0

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