Can Nous Hermes 1.0 run on RTX 3090 24GB?

YES — Tight Fit

A75Great

Estimated from fit model

Nous Hermes 1.0 needs ~21.3 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~119 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: 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) — 21.3 GB, 119.3 tok/s, Tight fit

21.3 GB required24.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

119.3 tok/s

TTFT

1622 ms

Safe context

16K

Memory

21.3 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsNous Hermes 1.0 on RTX 3090 24GB
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: 119.3 tok/s decode · 1.6s TTFT (warm) · 298 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	119.3 tok/s	885 ms	16K
Coding	A	Tight fit	119.3 tok/s	1622 ms	16K
Agentic Coding	F	Too heavy	44.3 tok/s	6350 ms	16K
Reasoning	A	Tight fit	119.3 tok/s	1917 ms	16K
RAG	F	Too heavy	44.3 tok/s	7937 ms	16K

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 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B65
Q3_K_S	3	4.4 GB	Low	B66
NVFP4	4	5.0 GB	Medium	B66
Q4_K_M	4	5.5 GB	Medium	B66
Q5_K_M	5	6.5 GB	High	B67
Q6_K	6	7.4 GB	High	B67
Q8_0	8	9.6 GB	Very High	B69
F16Best for your GPU	16	18.5 GB	Maximum	A70

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 3090 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	99.1 tok/s
Qwen 3.5 27B	27B	S	43 tok/s
Qwen 3.6 27B	27B	S	43.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	102.5 tok/s
Qwen 3.5 35B A3B	35B	A	55.5 tok/s

Frequently asked questions

Can RTX 3090 24GB run Nous Hermes 1.0?

Yes, RTX 3090 24GB can run Nous Hermes 1.0 with a A grade (Tight fit). Expected decode speed: 119.3 tok/s.

How much VRAM does Nous Hermes 1.0 need?

Nous Hermes 1.0 (9B parameters) requires approximately 21.3 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 3090 24GB?

On RTX 3090 24GB, Nous Hermes 1.0 achieves approximately 119.3 tokens per second decode speed with a time-to-first-token of 1622ms using Q4_K_M quantization.

Can RTX 3090 24GB run Nous Hermes 1.0 for coding?

For coding workloads, Nous Hermes 1.0 on RTX 3090 24GB receives a A grade with 119.3 tok/s and 16K context.

What context window can Nous Hermes 1.0 use on RTX 3090 24GB?

On RTX 3090 24GB, Nous Hermes 1.0 can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RTX 3090 24GB See all hardware for Nous Hermes 1.0

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