Can Command R 35B run on NVIDIA V100 32GB?

YES — Tight Fit

A77Great

Estimated from fit model

Command R 35B needs ~27.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 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, 30.7 tok/s, Tight fit

27.9 GB required32.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

30.7 tok/s

TTFT

6303 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 NVIDIA V100 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: 30.7 tok/s decode · 6.3s TTFT (warm) · 77 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	30.7 tok/s	3438 ms	43K
Coding	A	Tight fit	30.7 tok/s	6303 ms	43K
Agentic Coding	A	Tight fit	30.7 tok/s	9168 ms	43K
Reasoning	A	Tight fit	30.7 tok/s	7449 ms	43K
RAG	A	Tight fit	30.7 tok/s	11460 ms	43K

Quantization options

How Command R 35B (35B params) fits at each quantization level on NVIDIA V100 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	19.6 GB	Medium	A75
Q4_K_M	4	21.3 GB	Medium	A75
Q5_K_MBest for your GPU	5	25.2 GB	High	A75
Q6_K	6	28.7 GB	High	F0
Q8_0	8	37.5 GB	Very High	F0
F16	16	71.8 GB	Maximum	F0

Get started

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

Run

ollama run command-r

Your hardware

More models your NVIDIA V100 32GB can run

Model	Params	Grade	Decode	Capabilities
Kimi Linear 48B A3B	48B	A	15.7 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Command R 35B?

Yes, NVIDIA V100 32GB can run Command R 35B with a A grade (Tight fit). Expected decode speed: 30.7 tok/s.

How much VRAM does Command R 35B need?

Command R 35B (35B parameters) requires approximately 27.9 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 V100 32GB?

On NVIDIA V100 32GB, Command R 35B achieves approximately 30.7 tokens per second decode speed with a time-to-first-token of 6303ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Command R 35B for coding?

For coding workloads, Command R 35B on NVIDIA V100 32GB receives a A grade with 30.7 tok/s and 43K context.

What context window can Command R 35B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Command R 35B can safely use up to 43K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Command R 35B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

30.7 tok/s

3438 ms

43K

Coding

Tight fit

30.7 tok/s

6303 ms

43K

Agentic Coding

Tight fit

30.7 tok/s

9168 ms

43K

Reasoning

Tight fit

30.7 tok/s

7449 ms

43K

RAG

Tight fit

30.7 tok/s

11460 ms

43K

Quantization options

How Command R 35B (35B params) fits at each quantization level on NVIDIA V100 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	19.6 GB	Medium	A75
Q4_K_M	4	21.3 GB	Medium	A75
Q5_K_MBest for your GPU	5	25.2 GB	High	A75
Q6_K	6	28.7 GB	High	F0
Q8_0	8	37.5 GB	Very High	F0
F16	16	71.8 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Kimi Linear 48B A3B

48B

15.7 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Command R 35B?

Yes, NVIDIA V100 32GB can run Command R 35B with a A grade (Tight fit). Expected decode speed: 30.7 tok/s.

How much VRAM does Command R 35B need?

Command R 35B (35B parameters) requires approximately 27.9 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 V100 32GB?

On NVIDIA V100 32GB, Command R 35B achieves approximately 30.7 tokens per second decode speed with a time-to-first-token of 6303ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Command R 35B for coding?

For coding workloads, Command R 35B on NVIDIA V100 32GB receives a A grade with 30.7 tok/s and 43K context.

What context window can Command R 35B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Command R 35B can safely use up to 43K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.