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Can Command A 111B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

S94Excellent
Estimated from fit model

Command A 111B needs ~86.6 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~60 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) — 86.6 GB, 65.0 tok/s, Runs well
86.6 GB required141.0 GB available
61% VRAM used

Fit status

Runs well

Decode

65.0 tok/s

TTFT

2978 ms

Safe context

239K

Memory

86.6 GB / 141.0 GB

Memory breakdown

Weights67.7 GB
KV Cache3.9 GB
Runtime0.9 GB
Headroom14.1 GB

See how fast it feels

See how fast it feelsCommand A 111B 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: 65.0 tok/s decode · 3.0s TTFT (warm) · 163 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 well59.5 tok/s1773 ms239K
CodingSRuns well59.5 tok/s3251 ms239K
Agentic CodingSRuns well59.5 tok/s4729 ms239K
ReasoningSRuns well59.5 tok/s3842 ms239K
RAGSRuns well59.5 tok/s5911 ms239K

Quantization options

How Command A 111B (111B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
43.3 GB
LowA83
Q3_K_S
3
54.4 GB
LowA85
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 NVIDIA H200 PCIe 141GB can run

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct
123BS58.4 tok/s
👁 Alibaba
Qwen 3.5 122B A10B
122BS

Frequently asked questions

See all results for NVIDIA H200 PCIe 141GBSee all hardware for Command A 111B
62.2 GB
Medium
S86
Q4_K_M
4
67.7 GB
MediumS87
Q5_K_M
5
79.9 GB
HighS88
Q6_K
6
91.0 GB
HighS88
Q8_0Best for your GPU
8
118.8 GB
Very HighS88
F16
16
227.6 GB
MaximumF0
162.1 tok/s
👁 Mistral
Mistral Small 4 119B
119BS175.8 tok/s
👁 OpenAI
GPT-OSS 120B
117BS61.4 tok/s