Will It Run AI

Can OpenHermes 2.5 7B run on GTX 1080 8GB?

YES — With Offload

C53Usable
Estimated from fit model

OpenHermes 2.5 7B needs ~7.9 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
Share:

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) — 7.9 GB, 47.5 tok/s, Runs with offload
7.9 GB required8.0 GB available
99% VRAM used

Fit status

Runs with offload

Decode

47.5 tok/s

TTFT

4073 ms

Safe context

8K

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsOpenHermes 2.5 7B on GTX 1080 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 47.5 tok/s decode · 4.1s TTFT (warm) · 119 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.

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

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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit47.5 tok/s2222 ms8K
CodingCRuns with offload47.5 tok/s4073 ms8K
Agentic CodingFToo heavy21.8 tok/s12901 ms8K
ReasoningCRuns with offload47.5 tok/s4814 ms8K
RAGFToo heavy21.8 tok/s16126 ms8K

Quantization options

How OpenHermes 2.5 7B (7B params) fits at each quantization level on GTX 1080 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC54
Q3_K_S
3
3.4 GB
LowC54
NVFP4
4
3.9 GB
MediumC54
Q4_K_M
4
4.3 GB
MediumC54
Q5_K_MBest for your GPU
5
5.0 GB
HighC54
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run OpenHermes 2.5 7B on your machine.

Run

ollama run openhermes

Opções de upgrade

Hardware que roda bem OpenHermes 2.5 7B

NVIDIARTX 3060 12GBOpção econômica
12 GB VRAM (+4)360 GB/s (+40)
B
Adds memory headroom for longer context windows and future model growth.50.2 tok/s decodificação

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMelhor custo-benefício
16 GB VRAM (+8)448 GB/s (+128)
C
Raises estimated decode speed by about 47%.69.9 tok/s decodificação

Raises estimated decode speed by about 47%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

NVIDIARTX 5070 12GBUpgrade NVIDIA
12 GB VRAM (+4)672 GB/s (+352)
B
Raises estimated decode speed by about 124%.106.6 tok/s decodificação

Raises estimated decode speed by about 124%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

Frequently asked questions

Can GTX 1080 8GB run OpenHermes 2.5 7B?

Yes, GTX 1080 8GB can run OpenHermes 2.5 7B with a C grade (Runs with offload). Expected decode speed: 47.5 tok/s.

How much VRAM does OpenHermes 2.5 7B need?

OpenHermes 2.5 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for OpenHermes 2.5 7B?

The recommended quantization for OpenHermes 2.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will OpenHermes 2.5 7B run at on GTX 1080 8GB?

On GTX 1080 8GB, OpenHermes 2.5 7B achieves approximately 47.5 tokens per second decode speed with a time-to-first-token of 4073ms using Q4_K_M quantization.

Can GTX 1080 8GB run OpenHermes 2.5 7B for coding?

For coding workloads, OpenHermes 2.5 7B on GTX 1080 8GB receives a C grade with 47.5 tok/s and 8K context.

What context window can OpenHermes 2.5 7B use on GTX 1080 8GB?

On GTX 1080 8GB, OpenHermes 2.5 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if OpenHermes 2.5 7B feels slow on GTX 1080 8GB?

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 GTX 1080 8GBSee all hardware for OpenHermes 2.5 7B
Embed this result▼

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

<iframe src="https://willitrunai.com/embed/openhermes-2.5-7b-on-gtx-1080-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: