Can Llama 3.1 405B run on B100 192GB?

YES — With Q3_K_S

B67Good

Estimated from fit model

Llama 3.1 405B needs ~226.2 GB VRAM. B100 192GB has 192.0 GB. With Q3_K_S quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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.

Llama 3.1 405B at Q4_K_M needs 274.8 GB — too much for B100 192GB (192.0 GB). Runs at Q3_K_S (226.2 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 274.8 GB, exceeds 192.0 GB available

274.8 GB required192.0 GB available

143% VRAM needed

82.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.0 tok/s

TTFT

13789 ms

Safe context

Memory

274.8 GB / 192.0 GB

Offload

30%

Memory breakdown

Weights247.1 GB

KV Cache7.7 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.1 405B on B100 192GB
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: 14.0 tok/s decode · 13.8s TTFT (warm) · 35 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 20% 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 30.0 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	14.4 tok/s	7348 ms	4K
Coding	F	Too heavy	14.0 tok/s	13789 ms	4K
Agentic Coding	F	Too heavy	13.4 tok/s	20990 ms	4K
Reasoning	F	Too heavy	14.0 tok/s	16296 ms	4K
RAG	F	Too heavy	13.4 tok/s	26238 ms	4K

Inference speed

Llama 3.1 405B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.1 405B 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 ~2 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	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	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
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	2.0	Too big
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	2.0	Too big
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	2.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 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 Llama 3.1 405B (405B params) fits at each quantization level on B100 192GB (192.0 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

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 (+96)

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 (+64)

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 B100 192GB run Llama 3.1 405B?

Yes, B100 192GB can run Llama 3.1 405B at Q3_K_S quantization (Very compromised (needs ~30 GB host RAM)). The recommended Q4_K_M requires 274.8 GB which exceeds available memory, but at Q3_K_S it needs only 226.2 GB. Expected decode speed: 22.4 tok/s.

How much VRAM does Llama 3.1 405B need?

Llama 3.1 405B (405B parameters) requires approximately 274.8 GB at Q4_K_M quantization. On B100 192GB, it fits at Q3_K_S using 226.2 GB.

What is the best quantization for Llama 3.1 405B?

The recommended quantization is Q4_K_M, but on B100 192GB the best fitting quantization is Q3_K_S, which uses 226.2 GB.

What speed will Llama 3.1 405B run at on B100 192GB?

On B100 192GB, Llama 3.1 405B achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8639ms using Q3_K_S quantization.

Can B100 192GB run Llama 3.1 405B for coding?

For coding workloads, Llama 3.1 405B on B100 192GB receives a F grade with 14.0 tok/s and 4K context.

What context window can Llama 3.1 405B use on B100 192GB?

On B100 192GB, Llama 3.1 405B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.1 405B feels slow on B100 192GB?

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.

See all results for B100 192GB See all hardware for Llama 3.1 405B

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Preview:

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 20% 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 30.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

14.4 tok/s

7348 ms

Coding

Too heavy

14.0 tok/s

13789 ms

Agentic Coding

Too heavy

13.4 tok/s

20990 ms

Reasoning

Too heavy

14.0 tok/s

16296 ms

RAG

Too heavy

13.4 tok/s

26238 ms

Llama 3.1 405B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

2.0

Too big

RTX 4090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

2.0

Too big

RTX 3090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.0

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

RX 7900 XTX 24GB

24 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

2.0

Too big

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

2.0

Too big

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

2.0

Too big

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

2.0

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

2.0

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

2.0

Too big

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

2.0

Too big

4× RTX 3060 12GB

48 GB

Q4_K_M

2.0

Too big

Quant

Bits

VRAM

Quality

Fit

Q2_K

158.0 GB

Low

Q3_K_S

198.5 GB

Low

NVFP4

226.8 GB

Medium

Q4_K_M

247.1 GB

Medium

Q5_K_M

291.6 GB

High

Q6_K

332.1 GB

High

Q8_0

433.4 GB

Very High

F16

830.2 GB

Maximum

Frequently asked questions

Can B100 192GB run Llama 3.1 405B?

How much VRAM does Llama 3.1 405B need?

Llama 3.1 405B (405B parameters) requires approximately 274.8 GB at Q4_K_M quantization. On B100 192GB, it fits at Q3_K_S using 226.2 GB.

What is the best quantization for Llama 3.1 405B?

The recommended quantization is Q4_K_M, but on B100 192GB the best fitting quantization is Q3_K_S, which uses 226.2 GB.

What speed will Llama 3.1 405B run at on B100 192GB?

On B100 192GB, Llama 3.1 405B achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8639ms using Q3_K_S quantization.

Can B100 192GB run Llama 3.1 405B for coding?

For coding workloads, Llama 3.1 405B on B100 192GB receives a F grade with 14.0 tok/s and 4K context.

What context window can Llama 3.1 405B use on B100 192GB?

On B100 192GB, Llama 3.1 405B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.1 405B feels slow on B100 192GB?

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.