Can Llama 3.1 405B run on NVIDIA B200 180GB?

Q: What should I upgrade first if Llama 3.1 405B feels slow on NVIDIA B200 180GB?

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

A84Great

Estimated from fit model

Llama 3.1 405B needs ~184.5 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q2_K quantization, expect ~32 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 273.6 GB — too much for NVIDIA B200 180GB (180.0 GB). Runs at Q2_K (184.5 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 273.6 GB, exceeds 180.0 GB available

273.6 GB required180.0 GB available

152% VRAM needed

93.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.7 tok/s

TTFT

15228 ms

Safe context

Memory

273.6 GB / 180.0 GB

Offload

30%

Memory breakdown

Weights247.1 GB

KV Cache7.7 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.1 405B on NVIDIA B200 180GB
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: 12.7 tok/s decode · 15.2s 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	13.0 tok/s	8114 ms	4K
Coding	F	Too heavy	11.6 tok/s	16655 ms	4K
Agentic Coding	F	Too heavy	12.1 tok/s	23186 ms	4K
Reasoning	F	Too heavy	12.7 tok/s	17996 ms	4K
RAG	F	Too heavy	12.1 tok/s	28982 ms	4K

Quantization options

How Llama 3.1 405B (405B params) fits at each quantization level on NVIDIA B200 180GB (180.0 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 (+108)

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 (+76)

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 NVIDIA B200 180GB run Llama 3.1 405B?

Yes, NVIDIA B200 180GB can run Llama 3.1 405B at Q2_K quantization (Runs with offload (needs ~3.9 GB host RAM)). The recommended Q4_K_M requires 273.6 GB which exceeds available memory, but at Q2_K it needs only 184.5 GB. Expected decode speed: 32.4 tok/s.

How much VRAM does Llama 3.1 405B need?

Llama 3.1 405B (405B parameters) requires approximately 273.6 GB at Q4_K_M quantization. On NVIDIA B200 180GB, it fits at Q2_K using 184.5 GB.

What is the best quantization for Llama 3.1 405B?

The recommended quantization is Q4_K_M, but on NVIDIA B200 180GB the best fitting quantization is Q2_K, which uses 184.5 GB.

What speed will Llama 3.1 405B run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Llama 3.1 405B achieves approximately 32.4 tokens per second decode speed with a time-to-first-token of 5979ms using Q2_K quantization.

Can NVIDIA B200 180GB run Llama 3.1 405B for coding?

For coding workloads, Llama 3.1 405B on NVIDIA B200 180GB receives a F grade with 11.6 tok/s and 4K context.

What context window can Llama 3.1 405B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Llama 3.1 405B can safely use up to 7K 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 NVIDIA B200 180GB See all hardware for Llama 3.1 405B

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