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

Q: What should I upgrade first if Llama 3.1 405B feels slow on H100 NVL 188GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

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	12.0 tok/s	8829 ms	4K
Coding	F	Too heavy	11.7 tok/s	16568 ms	4K
Agentic Coding	F	Too heavy	11.2 tok/s	25223 ms	4K
Reasoning	F	Too heavy	11.7 tok/s	19580 ms	4K
RAG	F	Too heavy	11.2 tok/s	31529 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

Get started

Copy-paste commands to run Llama 3.1 405B on your machine.

Run

ollama run llama3.1:405b

Upgrade options

Hardware that runs Llama 3.1 405B well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+100)8000 GB/s (+200)

Makes the model fit on the accelerator instead of staying completely out of reach.25.9 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.

~$8,000 MSRP

AMD Instinct MI325X 256GBBest value

256 GB VRAM (+68)

Makes the model fit on the accelerator instead of staying completely out of reach.11.9 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$20,000 MSRP

Frequently asked questions

Can H100 NVL 188GB run Llama 3.1 405B?

Yes, H100 NVL 188GB can run Llama 3.1 405B at Q2_K quantization (Runs with offload). The recommended Q4_K_M requires 274.4 GB which exceeds available memory, but at Q2_K it needs only 185.3 GB. Expected decode speed: 37.2 tok/s.

How much VRAM does Llama 3.1 405B need?

Llama 3.1 405B (405B parameters) requires approximately 274.4 GB at Q4_K_M quantization. On H100 NVL 188GB, it fits at Q2_K using 185.3 GB.

What is the best quantization for Llama 3.1 405B?

The recommended quantization is Q4_K_M, but on H100 NVL 188GB the best fitting quantization is Q2_K, which uses 185.3 GB.

What speed will Llama 3.1 405B run at on H100 NVL 188GB?

On H100 NVL 188GB, Llama 3.1 405B achieves approximately 37.2 tokens per second decode speed with a time-to-first-token of 5206ms using Q2_K quantization.

Can H100 NVL 188GB run Llama 3.1 405B for coding?

For coding workloads, Llama 3.1 405B on H100 NVL 188GB receives a F grade with 11.7 tok/s and 4K context.

What context window can Llama 3.1 405B use on H100 NVL 188GB?

On H100 NVL 188GB, Llama 3.1 405B can safely use up to 22K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for H100 NVL 188GB See all hardware for Llama 3.1 405B

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