Can NousResearch Hermes 4 14B run on RX 7900 XTX 24GB?

YES — Runs Great

C53Usable

Estimated from fit model

NousResearch Hermes 4 14B needs ~13.5 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~81 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 13.5 GB, 80.9 tok/s, Runs well

13.5 GB required24.0 GB available

56% VRAM used

Fit status

Runs well

Decode

80.9 tok/s

TTFT

2392 ms

Safe context

119K

Memory

13.5 GB / 24.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsNousResearch Hermes 4 14B on RX 7900 XTX 24GB
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: 80.9 tok/s decode · 2.4s TTFT (warm) · 202 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	80.9 tok/s	1305 ms	119K
Coding	C	Runs well	80.9 tok/s	2392 ms	119K
Agentic Coding	C	Runs well	80.9 tok/s	3479 ms	119K
Reasoning	C	Runs well	80.9 tok/s	2827 ms	119K
RAG	C	Runs well	80.9 tok/s	4349 ms	119K

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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	140.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	89.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	80.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	76.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	75.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	65.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	54.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	51.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	33.2	Offloads
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	28.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.7	Fits
RTX 3060 12GB	12 GB	Q4_K_M	19.5	Offloads
RTX 4060 8GB	8 GB	Q4_K_M	7.2	Too 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 RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C46
Q3_K_S	3	6.9 GB	Low	C46
NVFP4	4	7.8 GB	Medium	C47
Q4_K_M	4	8.5 GB	Medium	C47
Q5_K_M	5	10.1 GB	High	C49
Q6_K	6	11.5 GB	High	C49
Q8_0Best for your GPU	8	15.0 GB	Very High	C50
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

Can RX 7900 XTX 24GB run NousResearch Hermes 4 14B?

Yes, RX 7900 XTX 24GB can run NousResearch Hermes 4 14B with a C grade (Runs well). Expected decode speed: 80.9 tok/s.

How much VRAM does NousResearch Hermes 4 14B need?

NousResearch Hermes 4 14B (14B parameters) requires approximately 13.5 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 RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, NousResearch Hermes 4 14B achieves approximately 80.9 tokens per second decode speed with a time-to-first-token of 2392ms using Q4_K_M quantization.

Can RX 7900 XTX 24GB run NousResearch Hermes 4 14B for coding?

For coding workloads, NousResearch Hermes 4 14B on RX 7900 XTX 24GB receives a C grade with 80.9 tok/s and 119K context.

What context window can NousResearch Hermes 4 14B use on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, NousResearch Hermes 4 14B can safely use up to 119K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7900 XTX 24GB See all hardware for NousResearch Hermes 4 14B

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