Can NousResearch Hermes 4 14B run on Quadro RTX 8000 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

NousResearch Hermes 4 14B needs ~16.2 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 16.2 GB, 54.3 tok/s, Runs well

16.2 GB required48.0 GB available

34% VRAM used

Fit status

Runs well

Decode

54.3 tok/s

TTFT

3566 ms

Safe context

326K

Memory

16.2 GB / 48.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsNousResearch Hermes 4 14B on Quadro RTX 8000 48GB
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: 54.3 tok/s decode · 3.6s TTFT (warm) · 136 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	54.3 tok/s	1945 ms	326K
Coding	C	Runs well	54.3 tok/s	3566 ms	326K
Agentic Coding	C	Runs well	54.3 tok/s	5186 ms	326K
Reasoning	C	Runs well	54.3 tok/s	4214 ms	326K
RAG	C	Runs well	54.3 tok/s	6483 ms	326K

Quantization options

How NousResearch Hermes 4 14B (14B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C42
Q3_K_S	3	6.9 GB	Low	C42
NVFP4	4	7.8 GB	Medium	C42
Q4_K_M	4	8.5 GB	Medium	C42
Q5_K_M	5	10.1 GB	High	C43
Q6_K	6	11.5 GB	High	C43
Q8_0	8	15.0 GB	Very High	C44
F16Best for your GPU	16	28.7 GB	Maximum	C48

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

Upgrade options

Hardware that runs NousResearch Hermes 4 14B well

Mac Studio M3 Ultra 96GBBudget pick

96 GB Unified (+48)819 GB/s (+147)

Adds memory headroom for longer context windows and future model growth.65.2 tok/s decode

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

~$3,999 MSRP

AMD Instinct MI210 64GBBest value

64 GB VRAM (+16)1638 GB/s (+966)

Raises estimated decode speed by about 140%.130.4 tok/s decode

Raises estimated decode speed by about 140%.

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

~$10,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run NousResearch Hermes 4 14B?

Yes, Quadro RTX 8000 48GB can run NousResearch Hermes 4 14B with a C grade (Runs well). Expected decode speed: 54.3 tok/s.

How much VRAM does NousResearch Hermes 4 14B need?

NousResearch Hermes 4 14B (14B parameters) requires approximately 16.2 GB of memory with Q4_K_M quantization.

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

The recommended quantization for NousResearch Hermes 4 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will NousResearch Hermes 4 14B run at on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, NousResearch Hermes 4 14B achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3566ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run NousResearch Hermes 4 14B for coding?

For coding workloads, NousResearch Hermes 4 14B on Quadro RTX 8000 48GB receives a C grade with 54.3 tok/s and 326K context.

What context window can NousResearch Hermes 4 14B use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, NousResearch Hermes 4 14B can safely use up to 326K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for NousResearch Hermes 4 14B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-bartowski--nousresearch-hermes-4-14b-gguf-on-quadro-rtx-8000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

54.3 tok/s

1945 ms

326K

Coding

Runs well

54.3 tok/s

3566 ms

326K

Agentic Coding

Runs well

54.3 tok/s

5186 ms

326K

Reasoning

Runs well

54.3 tok/s

4214 ms

326K

RAG

Runs well

54.3 tok/s

6483 ms

326K

Quantization options

How NousResearch Hermes 4 14B (14B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C42
Q3_K_S	3	6.9 GB	Low	C42
NVFP4	4	7.8 GB	Medium	C42
Q4_K_M	4	8.5 GB	Medium	C42
Q5_K_M	5	10.1 GB	High	C43
Q6_K	6	11.5 GB	High	C43
Q8_0	8	15.0 GB	Very High	C44
F16Best for your GPU	16	28.7 GB	Maximum	C48