Can Llama 3.1 405B run on H100 NVL 188GB?

YES — With Q2_K

A85Great

○Estimated from fit model

Llama 3.1 405B needs ~185.3 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q2_K quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Llama 3.1 405B at Q4_K_M needs 274.4 GB — too much for H100 NVL 188GB (188.0 GB). Runs at Q2_K (185.3 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 274.4 GB, exceeds 188.0 GB available

274.4 GB required188.0 GB available

146% VRAM needed

86.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.8 tok/s

TTFT

15148 ms

Safe context

Memory

274.4 GB / 188.0 GB

Offload

30%

Memory breakdown

Weights247.1 GB

KV Cache7.7 GB

Runtime0.9 GB

Headroom18.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.1 405B on H100 NVL 188GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 12.8 tok/s decode · 15.1s TTFT (warm) · 32 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	13.1 tok/s	8072 ms	4K
Coding	F	Too heavy	12.8 tok/s	15148 ms	4K
Agentic Coding	F	Too heavy	12.2 tok/s	23061 ms	4K
Reasoning	F	Too heavy	12.8 tok/s	17902 ms	4K
RAG	F	Too heavy	12.2 tok/s	28827 ms	4K

Quantization options

How Llama 3.1 405B (405B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	158.0 GB	Low	F0
Q3_K_S	3	198.5 GB	Low	F0
NVFP4	4	226.8 GB	Medium	F0
Q4_K_M	4	247.1 GB	Medium	F0
Q5_K_M	5	291.6 GB	High	F0
Q6_K	6	332.1 GB	High	F0
Q8_0	8	433.4 GB	Very High	F0
F16	16	830.2 GB	Maximum	F0