Can Nous Hermes 1.0 run on RTX 2070 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Nous Hermes 1.0 needs ~19.7 GB but RTX 2070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 19.7 GB, exceeds 8.0 GB available

19.7 GB required8.0 GB available

246% VRAM needed

11.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.3 tok/s

TTFT

26361 ms

Safe context

Memory

19.7 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights5.5 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNous Hermes 1.0 on RTX 2070 8GB
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: 7.3 tok/s decode · 26.4s TTFT (warm) · 18 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 19.7 GB, but this setup only exposes 8.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	11.1 tok/s	9472 ms	4K
Coding	F	Too heavy	7.3 tok/s	26361 ms	4K
Agentic Coding	F	Too heavy	7.3 tok/s	38343 ms	4K
Reasoning	F	Too heavy	7.3 tok/s	31154 ms	4K
RAG	F	Too heavy	7.3 tok/s	47929 ms	4K

Quantization options

How Nous Hermes 1.0 (9B params) fits at each quantization level on RTX 2070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A74
Q3_K_S	3	4.4 GB	Low	A74
NVFP4Best for your GPU

Upgrade options

Hardware that runs Nous Hermes 1.0 well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.35 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.

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+16)936 GB/s (+488)

Makes the model fit on the accelerator instead of staying completely out of reach.119.3 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.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+16)1008 GB/s (+560)

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.

~$1,599 MSRP

Frequently asked questions

Can RTX 2070 8GB run Nous Hermes 1.0?

No, Nous Hermes 1.0 requires more memory than RTX 2070 8GB provides.

How much VRAM does Nous Hermes 1.0 need?

Nous Hermes 1.0 (9B parameters) requires approximately 19.7 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 2070 8GB?

On RTX 2070 8GB, Nous Hermes 1.0 achieves approximately 7.3 tokens per second decode speed with a time-to-first-token of 26361ms using Q4_K_M quantization.

Can RTX 2070 8GB run Nous Hermes 1.0 for coding?

For coding workloads, Nous Hermes 1.0 on RTX 2070 8GB receives a F grade with 7.3 tok/s and 4K context.

What context window can Nous Hermes 1.0 use on RTX 2070 8GB?

On RTX 2070 8GB, Nous Hermes 1.0 can safely use up to 4K 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 2070 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 2070 8GB See all hardware for Nous Hermes 1.0

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/nous-hermes-1.0-on-rtx-2070-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: