willitrun·ai

Can NousResearch Hermes 4 14B run on GTX 1070 Ti 8GB?

YES — With Q2_K

D37Poor
Estimated from fit model

NousResearch Hermes 4 14B needs ~9.1 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q2_K quantization, expect ~13 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload
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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.

NousResearch Hermes 4 14B at Q4_K_M needs 12.2 GB — too much for GTX 1070 Ti 8GB (8.0 GB). Runs at Q2_K (9.1 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.2 GB, exceeds 8.0 GB available
12.2 GB required8.0 GB available
153% VRAM needed

4.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.1 tok/s

TTFT

37778 ms

Safe context

4K

Memory

12.2 GB / 8.0 GB

Offload

30%

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsNousResearch Hermes 4 14B on GTX 1070 Ti 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: 5.1 tok/s decode · 37.8s TTFT (warm) · 13 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 10% 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.

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

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 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy6.0 tok/s17681 ms4K
CodingFToo heavy5.1 tok/s37778 ms4K
Agentic CodingFToo heavy3.9 tok/s72524 ms4K
ReasoningFToo heavy5.1 tok/s44647 ms4K
RAGFToo heavy3.9 tok/s90655 ms4K

Inference speed

NousResearch Hermes 4 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for NousResearch Hermes 4 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M140.6Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M89.7Fits
RX 7900 XTX 24GB
24 GBQ4_K_M80.9Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M76.7Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M75.1Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M65.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M54.3Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M51.5Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M35.4Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M35.4Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M33.2Offloads
MacBook Pro M3 Max 64GB
64 GBQ4_K_M28.1Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M25.8Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M21.7Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M19.5Offloads
NVIDIARTX 4060 8GB
8 GBQ4_K_M7.2Too 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 NousResearch Hermes 4 14B (14B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowF0
Q3_K_S
3
6.9 GB
LowF0
NVFP4
4
7.8 GB
MediumF0
Q4_K_M
4
8.5 GB
MediumF0
Q5_K_M
5
10.1 GB
HighF0
Q6_K
6
11.5 GB
HighF0
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

Get started

Copy-paste commands to run NousResearch Hermes 4 14B on your machine.

Run

lms load hf-bartowski--nousresearch-hermes-4-14b-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien NousResearch Hermes 4 14B

NVIDIARTX 3060 12GBOpción económica
12 GB VRAM (+4)360 GB/s (+104)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.18.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+8)448 GB/s (+192)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.32.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejora NVIDIA
16 GB VRAM (+8)288 GB/s (+32)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.24.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$499 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run NousResearch Hermes 4 14B?

Yes, GTX 1070 Ti 8GB can run NousResearch Hermes 4 14B at Q2_K quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 12.2 GB which exceeds available memory, but at Q2_K it needs only 9.1 GB. Expected decode speed: 12.9 tok/s.

How much VRAM does NousResearch Hermes 4 14B need?

NousResearch Hermes 4 14B (14B parameters) requires approximately 12.2 GB at Q4_K_M quantization. On GTX 1070 Ti 8GB, it fits at Q2_K using 9.1 GB.

What is the best quantization for NousResearch Hermes 4 14B?

The recommended quantization is Q4_K_M, but on GTX 1070 Ti 8GB the best fitting quantization is Q2_K, which uses 9.1 GB.

What speed will NousResearch Hermes 4 14B run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, NousResearch Hermes 4 14B achieves approximately 12.9 tokens per second decode speed with a time-to-first-token of 14981ms using Q2_K quantization.

Can GTX 1070 Ti 8GB run NousResearch Hermes 4 14B for coding?

For coding workloads, NousResearch Hermes 4 14B on GTX 1070 Ti 8GB receives a F grade with 5.1 tok/s and 4K context.

What context window can NousResearch Hermes 4 14B use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, NousResearch Hermes 4 14B can safely use up to 5K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if NousResearch Hermes 4 14B feels slow on GTX 1070 Ti 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.

See all results for GTX 1070 Ti 8GBSee all hardware for NousResearch Hermes 4 14B
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