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

Inference speed

Command R 35B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Command R 35B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~40 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	39.6	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	30.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	28.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	23.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	22.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	20.5	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	19.3	Tight
RTX 4090 24GB	24 GB	Q4_K_M	13.1	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.2	Fits
RTX 3090 24GB	24 GB	Q4_K_M	12.0	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	4.7	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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
Ornith 1.0 35B A3B	35.1B	S	83.1 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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