Can Command R 35B run on RTX 5090 32GB?

YES — Tight Fit

A79Great

○Estimated from fit model

Command R 35B needs ~27.9 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 27.9 GB, 39.6 tok/s, Tight fit

27.9 GB required32.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

39.6 tok/s

TTFT

4885 ms

Safe context

43K

Memory

27.9 GB / 32.0 GB

Memory breakdown

Weights21.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCommand R 35B on RTX 5090 32GB
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: 39.6 tok/s decode · 4.9s TTFT (warm) · 99 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	Tight fit	56.2 tok/s	1878 ms	43K
Coding	A	Tight fit	56.2 tok/s	3443 ms	43K
Agentic Coding	A	Tight fit	56.2 tok/s	5007 ms	43K
Reasoning	A	Tight fit	56.2 tok/s	4069 ms	43K
RAG	A	Tight fit	56.2 tok/s	6259 ms	43K

Quantization options

How Command R 35B (35B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A75
Q3_K_S	3	17.2 GB	Low	A76
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 RTX 5090 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Moonshot AI Kimi Linear 48B A3B	48B	A	16.3 tok/s

Frequently asked questions

See all results for RTX 5090 32GB See all hardware for Command R 35B

URL: https://willitrunai.com/can-run/command-r-35b-on-rtx-5090-32gb

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