Can Hermes 4.3 36B run on NVIDIA GB200 192GB?

YES — Runs Great

C47Usable

Estimated from fit model

Hermes 4.3 36B needs ~46.6 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~306 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 46.6 GB, 306.0 tok/s, Runs well

46.6 GB required192.0 GB available

24% VRAM used

Fit status

Runs well

Decode

306.0 tok/s

TTFT

633 ms

Safe context

568K

Memory

46.6 GB / 192.0 GB

Memory breakdown

Weights22.0 GB

KV Cache4.2 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsHermes 4.3 36B on NVIDIA GB200 192GB
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: 306.0 tok/s decode · 633ms TTFT (warm) · 765 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	306.0 tok/s	350 ms	568K
Coding	C	Runs well	306.0 tok/s	633 ms	568K
Agentic Coding	C	Runs well	306.0 tok/s	920 ms	568K
Reasoning	C	Runs well	306.0 tok/s	748 ms	568K
RAG	C	Runs well	306.0 tok/s	1150 ms	568K

Inference speed

Hermes 4.3 36B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Hermes 4.3 36B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~55 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	54.7	Offloads
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	53.5	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	28.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	27.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	27.3	Fits
2× RTX 3090 24GB	48 GB	Q4_K_M	25.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	25.4	Fits
4× RTX 3060 12GB	48 GB	Q4_K_M	22.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	21.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	20.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	17.2	Tight
RTX 4090 24GB	24 GB	Q4_K_M	16.6	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	15.3	Too big
RTX 3090 24GB	24 GB	Q4_K_M	14.2	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	10.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	10.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	6.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 Hermes 4.3 36B (36B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	14.0 GB	Low	D37
Q3_K_S	3	17.6 GB	Low	D37
NVFP4	4	20.2 GB	Medium	D37
Q4_K_M	4	22.0 GB	Medium	D38
Q5_K_M	5	25.9 GB	High	D38
Q6_K	6	29.5 GB	High	D38
Q8_0	8	38.5 GB	Very High	D39
F16Best for your GPU	16	73.8 GB	Maximum	C43

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

Frequently asked questions

Can NVIDIA GB200 192GB run Hermes 4.3 36B?

Yes, NVIDIA GB200 192GB can run Hermes 4.3 36B with a C grade (Runs well). Expected decode speed: 306.0 tok/s.

How much VRAM does Hermes 4.3 36B need?

Hermes 4.3 36B (36B parameters) requires approximately 46.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Hermes 4.3 36B?

The recommended quantization for Hermes 4.3 36B is Q4_K_M, which balances quality and memory efficiency.

What speed will Hermes 4.3 36B run at on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Hermes 4.3 36B achieves approximately 306.0 tokens per second decode speed with a time-to-first-token of 633ms using Q4_K_M quantization.

Can NVIDIA GB200 192GB run Hermes 4.3 36B for coding?

For coding workloads, Hermes 4.3 36B on NVIDIA GB200 192GB receives a C grade with 306.0 tok/s and 568K context.

What context window can Hermes 4.3 36B use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Hermes 4.3 36B can safely use up to 568K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA GB200 192GB See all hardware for Hermes 4.3 36B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-nousresearch--hermes-4-3-36b-gguf-on-gb200-192gb" 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

306.0 tok/s

350 ms

568K

Coding

Runs well

306.0 tok/s

633 ms

568K

Agentic Coding

Runs well

306.0 tok/s

920 ms

568K

Reasoning

Runs well

306.0 tok/s

748 ms

568K

RAG

Runs well

306.0 tok/s

1150 ms

568K

Hermes 4.3 36B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

54.7

Offloads

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

53.5

Fits

2× RTX 4090 24GB

48 GB

Q4_K_M

28.1

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

27.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

27.3

Fits

2× RTX 3090 24GB

48 GB

Q4_K_M

25.8

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

25.4

Fits

4× RTX 3060 12GB

48 GB

Q4_K_M

22.7

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

21.1

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

20.0

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

17.2

Tight

RTX 4090 24GB

24 GB

Q4_K_M

16.6

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

15.3

Too big

RTX 3090 24GB

24 GB

Q4_K_M

14.2

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

10.9

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

10.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

6.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.6

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Hermes 4.3 36B (36B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	14.0 GB	Low	D37
Q3_K_S	3	17.6 GB	Low	D37
NVFP4	4	20.2 GB	Medium	D37
Q4_K_M	4	22.0 GB	Medium	D38
Q5_K_M	5	25.9 GB	High	D38
Q6_K	6	29.5 GB	High	D38
Q8_0	8	38.5 GB	Very High	D39
F16Best for your GPU	16	73.8 GB	Maximum	C43

Frequently asked questions

Can NVIDIA GB200 192GB run Hermes 4.3 36B?

Yes, NVIDIA GB200 192GB can run Hermes 4.3 36B with a C grade (Runs well). Expected decode speed: 306.0 tok/s.

How much VRAM does Hermes 4.3 36B need?

Hermes 4.3 36B (36B parameters) requires approximately 46.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Hermes 4.3 36B?

The recommended quantization for Hermes 4.3 36B is Q4_K_M, which balances quality and memory efficiency.

What speed will Hermes 4.3 36B run at on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Hermes 4.3 36B achieves approximately 306.0 tokens per second decode speed with a time-to-first-token of 633ms using Q4_K_M quantization.

Can NVIDIA GB200 192GB run Hermes 4.3 36B for coding?

For coding workloads, Hermes 4.3 36B on NVIDIA GB200 192GB receives a C grade with 306.0 tok/s and 568K context.

What context window can Hermes 4.3 36B use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Hermes 4.3 36B can safely use up to 568K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.