Can NousResearch Hermes 4 14B run on Tesla P100 16GB?

YES — Runs Great

C55Usable

○Estimated from fit model

NousResearch Hermes 4 14B needs ~13.0 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~51 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) — 13.0 GB, 50.6 tok/s, Runs well

13.0 GB required16.0 GB available

81% VRAM used

Fit status

Runs well

Decode

50.6 tok/s

TTFT

3828 ms

Safe context

45K

Memory

13.0 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsNousResearch Hermes 4 14B on Tesla P100 16GB
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: 50.6 tok/s decode · 3.8s TTFT (warm) · 126 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	50.6 tok/s	2088 ms	45K
Coding	C	Runs well	50.6 tok/s	3828 ms	45K
Agentic Coding	C	Tight fit	50.6 tok/s	5568 ms	45K
Reasoning	C	Runs well	50.6 tok/s	4524 ms	45K
RAG	C	Tight fit	50.6 tok/s	6960 ms	45K

Quantization options

How NousResearch Hermes 4 14B (14B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C49
Q3_K_S	3	6.9 GB	Low	C51
NVFP4	4	7.8 GB	Medium	C51
Q4_K_M	4	8.5 GB	Medium	C51
Q5_K_M	5	10.1 GB	High	C51
Q6_KBest for your GPU	6	11.5 GB	High	C51
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

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

Frequently asked questions

See all results for Tesla P100 16GB See all hardware for NousResearch Hermes 4 14B

URL: https://willitrunai.com/can-run/hf-bartowski--nousresearch-hermes-4-14b-gguf-on-tesla-p100-16gb

⇱ NousResearch Hermes 4 14B on Tesla P100 16GB? YES