Can GLM-5 run on NVIDIA H200 PCIe 141GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

GLM-5 needs ~489.4 GB but NVIDIA H200 PCIe 141GB only has 141.0 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) — 489.4 GB, exceeds 141.0 GB available

489.4 GB required141.0 GB available

347% VRAM needed

348.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.1 tok/s

TTFT

91066 ms

Safe context

Memory

489.4 GB / 141.0 GB

Offload

70%

Memory breakdown

Weights453.8 GB

KV Cache19.0 GB

Runtime2.4 GB

Headroom14.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGLM-5 on NVIDIA H200 PCIe 141GB
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: 2.1 tok/s decode · 91.1s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 489.4 GB, but this setup only exposes 141.0 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	3.0 tok/s	35764 ms	4K
Coding	F	Too heavy	3.0 tok/s	65568 ms	4K
Agentic Coding	F	Too heavy	3.0 tok/s	95371 ms	4K
Reasoning	F	Too heavy	3.0 tok/s	77489 ms	4K
RAG	F	Too heavy	3.0 tok/s	119214 ms	4K

Quantization options

How GLM-5 (744B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	290.2 GB	Low	F0
Q3_K_S	3	364.6 GB	Low	F0
NVFP4	4	416.6 GB	Medium	F0
Q4_K_M	4	453.8 GB	Medium	F0
Q5_K_M	5	535.7 GB	High	F0
Q6_K	6	610.1 GB	High	F0
Q8_0	8	796.1 GB	Very High	F0
F16	16	1525.2 GB	Maximum	F0

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run GLM-5?

No, GLM-5 requires more memory than NVIDIA H200 PCIe 141GB provides.

How much VRAM does GLM-5 need?

GLM-5 (744B parameters) requires approximately 489.4 GB of memory with Q4_K_M quantization.

What is the best quantization for GLM-5?

The recommended quantization for GLM-5 is Q4_K_M, which balances quality and memory efficiency.

What speed will GLM-5 run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, GLM-5 achieves approximately 3.0 tokens per second decode speed with a time-to-first-token of 65568ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run GLM-5 for coding?

For coding workloads, GLM-5 on NVIDIA H200 PCIe 141GB receives a F grade with 3.0 tok/s and 4K context.

What context window can GLM-5 use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, GLM-5 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 feels slow on NVIDIA H200 PCIe 141GB?

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 H200 PCIe 141GB See all hardware for GLM-5

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