Can GLM-5.1 run on NVIDIA B200 180GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

GLM-5.1 needs ~499.4 GB but NVIDIA B200 180GB only has 180.0 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: HighStack: OptimizedBottleneck: Memory capacity

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) — 499.4 GB, exceeds 180.0 GB available

499.4 GB required180.0 GB available

277% VRAM needed

319.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.1 tok/s

TTFT

37697 ms

Safe context

Memory

499.4 GB / 180.0 GB

Offload

60%

Memory breakdown

Weights459.9 GB

KV Cache19.0 GB

Runtime2.4 GB

Headroom18.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGLM-5.1 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: 5.1 tok/s decode · 37.7s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 499.4 GB, but this setup only exposes 180.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.3 tok/s	19919 ms	4K
Coding	F	Too heavy	5.1 tok/s	37697 ms	4K
Agentic Coding	F	Too heavy	4.9 tok/s	57895 ms	4K
Reasoning	F	Too heavy	5.1 tok/s	44551 ms	4K
RAG	F	Too heavy	4.9 tok/s	72369 ms	4K

Quantization options

How GLM-5.1 (754B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	294.1 GB	Low	F0
Q3_K_S	3	369.5 GB	Low	F0
NVFP4	4	422.2 GB	Medium	F0
Q4_K_M	4	459.9 GB	Medium	F0
Q5_K_M	5	542.9 GB	High	F0
Q6_K	6	618.3 GB	High	F0
Q8_0	8	806.8 GB	Very High	F0
F16	16	1545.7 GB	Maximum	F0

Frequently asked questions

Can NVIDIA B200 180GB run GLM-5.1?

No, GLM-5.1 requires more memory than NVIDIA B200 180GB provides.

How much VRAM does GLM-5.1 need?

GLM-5.1 (754B parameters) requires approximately 499.4 GB of memory with Q4_K_M quantization.

What is the best quantization for GLM-5.1?

The recommended quantization for GLM-5.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will GLM-5.1 run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, GLM-5.1 achieves approximately 5.1 tokens per second decode speed with a time-to-first-token of 37697ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run GLM-5.1 for coding?

For coding workloads, GLM-5.1 on NVIDIA B200 180GB receives a F grade with 5.1 tok/s and 4K context.

What context window can GLM-5.1 use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, GLM-5.1 can safely use up to 4K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

What should I upgrade first if GLM-5.1 feels slow on NVIDIA B200 180GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for NVIDIA B200 180GB See all hardware for GLM-5.1

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

5.3 tok/s

19919 ms

Coding

Too heavy

5.1 tok/s

37697 ms

Agentic Coding

Too heavy

4.9 tok/s

57895 ms

Reasoning

Too heavy

5.1 tok/s

44551 ms

RAG

Too heavy

4.9 tok/s

72369 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

294.1 GB

Low

Q3_K_S

369.5 GB

Low

NVFP4

422.2 GB

Medium

Q4_K_M

459.9 GB

Medium

Q5_K_M

542.9 GB

High

Q6_K

618.3 GB

High

Q8_0

806.8 GB

Very High

F16

1545.7 GB

Maximum

Frequently asked questions

Can NVIDIA B200 180GB run GLM-5.1?

No, GLM-5.1 requires more memory than NVIDIA B200 180GB provides.

How much VRAM does GLM-5.1 need?

GLM-5.1 (754B parameters) requires approximately 499.4 GB of memory with Q4_K_M quantization.

What is the best quantization for GLM-5.1?

The recommended quantization for GLM-5.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will GLM-5.1 run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, GLM-5.1 achieves approximately 5.1 tokens per second decode speed with a time-to-first-token of 37697ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run GLM-5.1 for coding?

For coding workloads, GLM-5.1 on NVIDIA B200 180GB receives a F grade with 5.1 tok/s and 4K context.

What context window can GLM-5.1 use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, GLM-5.1 can safely use up to 4K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

What should I upgrade first if GLM-5.1 feels slow on NVIDIA B200 180GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.