Can Nous Hermes 2 SOLAR 10.7B run on RTX 4060 8GB?

Q: What should I upgrade first if Nous Hermes 2 SOLAR 10.7B feels slow on RTX 4060 8GB?

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.

BARELY — Tight on Memory

D32Poor

Estimated from fit model

Nous Hermes 2 SOLAR 10.7B needs ~9.5 GB VRAM. RTX 4060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~17 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.5 GB, 16.8 tok/s, Very compromised (needs ~1 GB host RAM)

9.5 GB required8.0 GB available

119% VRAM needed

1.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

16.8 tok/s

TTFT

11554 ms

Safe context

Memory

9.5 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsNous Hermes 2 SOLAR 10.7B on RTX 4060 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: 16.8 tok/s decode · 11.6s TTFT (warm) · 42 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 1.0 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	D	Very compromised (needs ~0.6 GB host RAM)	19.4 tok/s	5457 ms	4K
Coding	D	Very compromised (needs ~1 GB host RAM)	16.8 tok/s	11554 ms	4K
Agentic Coding	F	Too heavy	12.9 tok/s	21828 ms	4K
Reasoning	D	Very compromised (needs ~1 GB host RAM)	16.8 tok/s	13655 ms	4K
RAG	F	Too heavy	12.9 tok/s	27285 ms

Quantization options

How Nous Hermes 2 SOLAR 10.7B (10.699999809265137B params) fits at each quantization level on RTX 4060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C53
Q3_K_SBest for your GPU	3	5.2 GB	Low	C53

Get started

Copy-paste commands to run Nous Hermes 2 SOLAR 10.7B on your machine.

Run

lms load hf-thebloke--nous-hermes-2-solar-10-7b-gguf && lms server start

Upgrade options

Hardware that runs Nous Hermes 2 SOLAR 10.7B well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+88)

Removes host-memory offload, which is usually the single biggest latency and throughput win.35.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 113%.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+176)

Removes host-memory offload, which is usually the single biggest latency and throughput win.41.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 146%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.33.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 101%.

~$499 MSRP

Frequently asked questions

Can RTX 4060 8GB run Nous Hermes 2 SOLAR 10.7B?

Yes, RTX 4060 8GB can run Nous Hermes 2 SOLAR 10.7B with a D grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 16.8 tok/s.

How much VRAM does Nous Hermes 2 SOLAR 10.7B need?

Nous Hermes 2 SOLAR 10.7B (10.699999809265137B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Nous Hermes 2 SOLAR 10.7B?

The recommended quantization for Nous Hermes 2 SOLAR 10.7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nous Hermes 2 SOLAR 10.7B run at on RTX 4060 8GB?

On RTX 4060 8GB, Nous Hermes 2 SOLAR 10.7B achieves approximately 16.8 tokens per second decode speed with a time-to-first-token of 11554ms using Q4_K_M quantization.

Can RTX 4060 8GB run Nous Hermes 2 SOLAR 10.7B for coding?

For coding workloads, Nous Hermes 2 SOLAR 10.7B on RTX 4060 8GB receives a D grade with 16.8 tok/s and 4K context.

What context window can Nous Hermes 2 SOLAR 10.7B use on RTX 4060 8GB?

On RTX 4060 8GB, Nous Hermes 2 SOLAR 10.7B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 8GB See all hardware for Nous Hermes 2 SOLAR 10.7B

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