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URL: https://willitrunai.com/can-run/command-a-111b-on-instinct-mi300a-128gb


Can Command A 111B run on AMD Instinct MI300A 128GB?

YES — Runs Great

S95Excellent
Estimated from fit model

Command A 111B needs ~85.3 GB VRAM. AMD Instinct MI300A 128GB has 128.0 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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) — 85.3 GB, 59.8 tok/s, Runs well
85.3 GB required128.0 GB available
67% VRAM used

Fit status

Runs well

Decode

59.8 tok/s

TTFT

3237 ms

Safe context

191K

Memory

85.3 GB / 128.0 GB

Memory breakdown

Weights67.7 GB
KV Cache3.9 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsCommand A 111B on AMD Instinct MI300A 128GB
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: 59.8 tok/s decode · 3.2s TTFT (warm) · 150 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 well54.8 tok/s1927 ms191K
CodingSRuns well54.8 tok/s3533 ms191K
Agentic CodingSRuns well54.8 tok/s5139 ms191K
ReasoningSRuns well54.8 tok/s4176 ms191K
RAGSRuns well54.8 tok/s6424 ms191K

Quantization options

How Command A 111B (111B params) fits at each quantization level on AMD Instinct MI300A 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
43.3 GB
LowA84
Q3_K_S
3
54.4 GB
LowS86
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 AMD Instinct MI300A 128GB can run

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct
123BS53.8 tok/s
👁 Alibaba
Qwen 3.5 122B A10B
122BS

Frequently asked questions

See all results for AMD Instinct MI300A 128GBSee all hardware for Command A 111B
62.2 GB
Medium
S87
Q4_K_M
4
67.7 GB
MediumS88
Q5_K_M
5
79.9 GB
HighS88
Q6_KBest for your GPU
6
91.0 GB
HighS88
Q8_0
8
118.8 GB
Very HighF0
F16
16
227.6 GB
MaximumF0
149.2 tok/s
👁 Mistral
Mistral Small 4 119B
119BS161.7 tok/s
👁 OpenAI
GPT-OSS 120B
117BS56.5 tok/s