Can Hermes 4.3 36B run on RTX 4000 Ada 20GB?

Q: What should I upgrade first if Hermes 4.3 36B feels slow on RTX 4000 Ada 20GB?

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 Q2_K

D37Poor

Estimated from fit model

Hermes 4.3 36B needs ~21.5 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q2_K quantization, expect ~11 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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 29.4 GB — too much for RTX 4000 Ada 20GB (20.0 GB). Runs at Q2_K (21.5 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 29.4 GB, exceeds 20.0 GB available

29.4 GB required20.0 GB available

147% VRAM needed

9.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.3 tok/s

TTFT

45355 ms

Safe context

Memory

29.4 GB / 20.0 GB

Offload

30%

Memory breakdown

Weights22.0 GB

KV Cache4.2 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsHermes 4.3 36B on RTX 4000 Ada 20GB
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: 4.3 tok/s decode · 45.4s TTFT (warm) · 11 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.

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 1.0 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	F	Too heavy	5.0 tok/s	21148 ms	4K
Coding	F	Too heavy	4.3 tok/s	45355 ms	4K
Agentic Coding	F	Too heavy	3.2 tok/s	87501 ms	4K
Reasoning	F	Too heavy	4.3 tok/s	53601 ms	4K
RAG	F	Too heavy	3.2 tok/s	109377 ms	4K

Quantization options

How Hermes 4.3 36B (36B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	14.0 GB	Low	C50
Q3_K_S	3	17.6 GB	Low	F0

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 (+12)1792 GB/s (+1432)

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 (+12)896 GB/s (+536)

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 (+12)576 GB/s (+216)

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 RTX 4000 Ada 20GB run Hermes 4.3 36B?

Yes, RTX 4000 Ada 20GB can run Hermes 4.3 36B at Q2_K quantization (Runs with offload (needs ~1 GB host RAM)). The recommended Q4_K_M requires 29.4 GB which exceeds available memory, but at Q2_K it needs only 21.5 GB. Expected decode speed: 11.0 tok/s.

How much VRAM does Hermes 4.3 36B need?

Hermes 4.3 36B (36B parameters) requires approximately 29.4 GB at Q4_K_M quantization. On RTX 4000 Ada 20GB, it fits at Q2_K using 21.5 GB.

What is the best quantization for Hermes 4.3 36B?

The recommended quantization is Q4_K_M, but on RTX 4000 Ada 20GB the best fitting quantization is Q2_K, which uses 21.5 GB.

What speed will Hermes 4.3 36B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Hermes 4.3 36B achieves approximately 11.0 tokens per second decode speed with a time-to-first-token of 17610ms using Q2_K quantization.

Can RTX 4000 Ada 20GB run Hermes 4.3 36B for coding?

For coding workloads, Hermes 4.3 36B on RTX 4000 Ada 20GB receives a F grade with 4.3 tok/s and 4K context.

What context window can Hermes 4.3 36B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Hermes 4.3 36B can safely use up to 10K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Hermes 4.3 36B

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