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URL: https://willitrunai.com/can-run/command-a-111b-on-h100-nvl-188gb


Can Command A 111B run on H100 NVL 188GB?

YES — Runs Great

S93Excellent
Estimated from fit model

Command A 111B needs ~91.3 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~93 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 91.3 GB, 101.9 tok/s, Runs well
91.3 GB required188.0 GB available
49% VRAM used

Fit status

Runs well

Decode

101.9 tok/s

TTFT

1901 ms

Safe context

262K

Memory

91.3 GB / 188.0 GB

Memory breakdown

Weights67.7 GB
KV Cache3.9 GB
Runtime0.9 GB
Headroom18.8 GB

See how fast it feels

See how fast it feelsCommand A 111B 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: 101.9 tok/s decode · 1.9s TTFT (warm) · 255 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well93.3 tok/s1132 ms262K
CodingSRuns well93.3 tok/s2075 ms262K
Agentic CodingSRuns well101.9 tok/s2764 ms262K
ReasoningSRuns well101.9 tok/s2246 ms262K
RAGSRuns well101.9 tok/s3456 ms262K

Quantization options

How Command A 111B (111B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
43.3 GB
LowA81
Q3_K_S
3
54.4 GB
LowA82
NVFP4
4

Get started

Copy-paste commands to run Command A 111B on your machine.

Run

ollama run command-a

Your hardware

More models your H100 NVL 188GB can run

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct
123BS91.6 tok/s
👁 Alibaba
Qwen 3.5 122B A10B
122BS

Frequently asked questions

See all results for H100 NVL 188GBSee all hardware for Command A 111B
62.2 GB
Medium
A83
Q4_K_M
4
67.7 GB
MediumA84
Q5_K_M
5
79.9 GB
HighS85
Q6_K
6
91.0 GB
HighS87
Q8_0Best for your GPU
8
118.8 GB
Very HighS88
F16
16
227.6 GB
MaximumF0
254 tok/s
👁 DeepSeek
DeepSeek V4 Flash
284BS136.1 tok/s
👁 Mistral
Mistral Small 4 119B
119BS275.4 tok/s
👁 OpenAI
GPT-OSS 120B
117BS96.3 tok/s