Can Command R 35B run on NVIDIA GH200 96GB?

YES — Runs Great

A76Great

○Estimated from fit model

Command R 35B needs ~34.3 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~152 tok/s.

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

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) — 34.3 GB, 165.0 tok/s, Runs well

34.3 GB required96.0 GB available

36% VRAM used

Fit status

Runs well

Decode

165.0 tok/s

TTFT

1173 ms

Safe context

131K

Memory

34.3 GB / 96.0 GB

Memory breakdown

Weights21.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsCommand R 35B on NVIDIA GH200 96GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 165.0 tok/s decode · 1.2s TTFT (warm) · 413 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	151.8 tok/s	696 ms	131K
Coding	A	Runs well	151.8 tok/s	1276 ms	131K
Agentic Coding	A	Runs well	151.8 tok/s	1856 ms	131K
Reasoning	A	Runs well	151.8 tok/s	1508 ms	131K
RAG	A	Runs well	151.8 tok/s	2320 ms	131K

Quantization options

How Command R 35B (35B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	B66
Q3_K_S	3	17.2 GB	Low	B67
NVFP4	4

Get started

Copy-paste commands to run Command R 35B on your machine.

Run

ollama run command-r

Your hardware

More models your NVIDIA GH200 96GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	S	47 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S

Frequently asked questions

See all results for NVIDIA GH200 96GB See all hardware for Command R 35B

URL: https://willitrunai.com/can-run/command-r-35b-on-gh200-96gb

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