Can Nous Hermes 1.0 run on RTX 4000 Ada 20GB?

YES — With Offload

A71Great

Estimated from fit model

Nous Hermes 1.0 needs ~20.6 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 20.6 GB, 36.1 tok/s, Runs with offload (needs ~0.2 GB host RAM)

20.6 GB required20.0 GB available

103% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

36.1 tok/s

TTFT

5369 ms

Safe context

15K

Memory

20.6 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsNous Hermes 1.0 on RTX 4000 Ada 20GB
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: 36.1 tok/s decode · 5.4s TTFT (warm) · 90 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

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	Runs well	51.1 tok/s	2065 ms	15K
Coding	A	Runs with offload (needs ~0.2 GB host RAM)	36.1 tok/s	5369 ms	15K
Agentic Coding	F	Too heavy	13.5 tok/s	20802 ms	15K
Reasoning	A	Runs with offload (needs ~0.2 GB host RAM)	36.1 tok/s	6345 ms	15K
RAG	F	Too heavy	13.5 tok/s	26003 ms	15K

Inference speed

Nous Hermes 1.0 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nous Hermes 1.0 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	125.9	Tight
RTX 3090 24GB	24 GB	Q4_K_M	119.3	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	101.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	84.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	80.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	68.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	68.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	49.5	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	43.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	40.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	35.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	17.4	Too big
RTX 3060 12GB	12 GB	Q4_K_M	11.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	5.4	Too 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 Nous Hermes 1.0 (9B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B66
Q3_K_S	3	4.4 GB	Low	B67
NVFP4	4	5.0 GB	Medium	B67
Q4_K_M	4	5.5 GB	Medium	B68
Q5_K_M	5	6.5 GB	High	B69
Q6_K	6	7.4 GB	High	B69
Q8_0Best for your GPU	8	9.6 GB	Very High	A71
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

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

Your hardware

More models your RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	23.8 tok/s
Qwen 3.5 27B	27B	A	10.7 tok/s
Qwen 3.6 27B	27B	S	10.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	25.3 tok/s
Magistral Small 2507	24B	S	20.6 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run Nous Hermes 1.0?

Yes, RTX 4000 Ada 20GB can run Nous Hermes 1.0 with a A grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 36.1 tok/s.

How much VRAM does Nous Hermes 1.0 need?

Nous Hermes 1.0 (9B parameters) requires approximately 20.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Nous Hermes 1.0?

The recommended quantization for Nous Hermes 1.0 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nous Hermes 1.0 run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Nous Hermes 1.0 achieves approximately 36.1 tokens per second decode speed with a time-to-first-token of 5369ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Nous Hermes 1.0 for coding?

For coding workloads, Nous Hermes 1.0 on RTX 4000 Ada 20GB receives a A grade with 36.1 tok/s and 15K context.

What context window can Nous Hermes 1.0 use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Nous Hermes 1.0 can safely use up to 15K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if Nous Hermes 1.0 feels slow on RTX 4000 Ada 20GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4000 Ada 20GB See all hardware for Nous Hermes 1.0

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