Can Hermes 4.3 36B run on Quadro RTX 6000 24GB?

Q: What should I upgrade first if Hermes 4.3 36B feels slow on Quadro RTX 6000 24GB?

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.

YES — With NVFP4

D37Poor

Estimated from fit model

Hermes 4.3 36B needs ~28.0 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With NVFP4 quantization, expect ~13 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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.

Hermes 4.3 36B at Q4_K_M needs 29.8 GB — too much for Quadro RTX 6000 24GB (24.0 GB). Runs at NVFP4 (28.0 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 29.8 GB, exceeds 24.0 GB available

29.8 GB required24.0 GB available

124% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.6 tok/s

TTFT

20188 ms

Safe context

Memory

29.8 GB / 24.0 GB

Offload

20%

Memory breakdown

Weights22.0 GB

KV Cache4.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsHermes 4.3 36B on Quadro RTX 6000 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 9.6 tok/s decode · 20.2s TTFT (warm) · 24 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 2.9 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 ~2.9 GB host RAM)	11.3 tok/s	9371 ms	4K
Coding	F	Too heavy	9.6 tok/s	20188 ms	4K
Agentic Coding	F	Too heavy	7.2 tok/s	39281 ms	4K
Reasoning	F	Too heavy	9.6 tok/s	23859 ms	4K
RAG	F	Too heavy	7.2 tok/s	49102 ms	4K

Quantization options

How Hermes 4.3 36B (36B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	14.0 GB	Low	C50
Q3_K_SBest for your GPU	3	17.6 GB	Low	C49

Get started

Copy-paste commands to run Hermes 4.3 36B on your machine.

Run

lms load hf-nousresearch--hermes-4-3-36b-gguf && lms server start

Upgrade options

Hardware that runs Hermes 4.3 36B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1120)

Makes the model fit on the accelerator instead of staying completely out of reach.54.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.34.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.21 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$4,000 MSRP

Frequently asked questions

Can Quadro RTX 6000 24GB run Hermes 4.3 36B?

Yes, Quadro RTX 6000 24GB can run Hermes 4.3 36B at NVFP4 quantization (Very compromised (needs ~2.9 GB host RAM)). The recommended Q4_K_M requires 29.8 GB which exceeds available memory, but at NVFP4 it needs only 28.0 GB. Expected decode speed: 12.6 tok/s.

How much VRAM does Hermes 4.3 36B need?

Hermes 4.3 36B (36B parameters) requires approximately 29.8 GB at Q4_K_M quantization. On Quadro RTX 6000 24GB, it fits at NVFP4 using 28.0 GB.

What is the best quantization for Hermes 4.3 36B?

The recommended quantization is Q4_K_M, but on Quadro RTX 6000 24GB the best fitting quantization is NVFP4, which uses 28.0 GB.

What speed will Hermes 4.3 36B run at on Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, Hermes 4.3 36B achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15392ms using NVFP4 quantization.

Can Quadro RTX 6000 24GB run Hermes 4.3 36B for coding?

For coding workloads, Hermes 4.3 36B on Quadro RTX 6000 24GB receives a F grade with 9.6 tok/s and 4K context.

What context window can Hermes 4.3 36B use on Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, Hermes 4.3 36B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 6000 24GB See all hardware for Hermes 4.3 36B

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