Can Command R 35B run on NVIDIA GB200 192GB?

YES — Runs Great

A73Great

Estimated from fit model

Command R 35B needs ~43.9 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~315 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) — 43.9 GB, 342.3 tok/s, Runs well

43.9 GB required192.0 GB available

23% VRAM used

Fit status

Runs well

Decode

342.3 tok/s

TTFT

566 ms

Safe context

131K

Memory

43.9 GB / 192.0 GB

Memory breakdown

Weights21.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsCommand R 35B on NVIDIA GB200 192GB
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: 342.3 tok/s decode · 566ms TTFT (warm) · 856 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	342.3 tok/s	350 ms	131K
Coding	A	Runs well	314.8 tok/s	615 ms	131K
Agentic Coding	A	Runs well	342.3 tok/s	823 ms	131K
Reasoning	A	Runs well	342.3 tok/s	668 ms	131K
RAG	A	Runs well	342.3 tok/s	1028 ms	131K

Quantization options

How Command R 35B (35B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	B63
Q3_K_S	3	17.2 GB	Low	B64
NVFP4	4	19.6 GB	Medium	B64
Q4_K_M	4	21.3 GB	Medium	B64
Q5_K_M	5	25.2 GB	High	B64
Q6_K	6	28.7 GB	High	B65
Q8_0	8	37.5 GB	Very High	B66
F16Best for your GPU	16	71.8 GB	Maximum	B70

Get started

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

Run

ollama run command-r

Your hardware

More models your NVIDIA GB200 192GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	97.4 tok/s
Qwen 3.5 122B A10B	122B	S	270.2 tok/s
DeepSeek V4 Flash	284B	S	144.8 tok/s
Mistral Small 4 119B	119B	S	292.9 tok/s
GPT-OSS 120B	117B	S	102.4 tok/s

Frequently asked questions

Can NVIDIA GB200 192GB run Command R 35B?

Yes, NVIDIA GB200 192GB can run Command R 35B with a A grade (Runs well). Expected decode speed: 314.8 tok/s.

How much VRAM does Command R 35B need?

Command R 35B (35B parameters) requires approximately 43.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Command R 35B?

The recommended quantization for Command R 35B is Q4_K_M, which balances quality and memory efficiency.

What speed will Command R 35B run at on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Command R 35B achieves approximately 314.8 tokens per second decode speed with a time-to-first-token of 615ms using Q4_K_M quantization.

Can NVIDIA GB200 192GB run Command R 35B for coding?

For coding workloads, Command R 35B on NVIDIA GB200 192GB receives a A grade with 314.8 tok/s and 131K context.

What context window can Command R 35B use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, Command R 35B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA GB200 192GB See all hardware for Command R 35B

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