Can Command R 35B run on NVIDIA A40 48GB?

YES — Runs Great

A77Great

Estimated from fit model

Command R 35B needs ~29.5 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 29.5 GB, 27.7 tok/s, Runs well

29.5 GB required48.0 GB available

61% VRAM used

Fit status

Runs well

Decode

27.7 tok/s

TTFT

7001 ms

Safe context

131K

Memory

29.5 GB / 48.0 GB

Memory breakdown

Weights21.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCommand R 35B on NVIDIA A40 48GB
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: 27.7 tok/s decode · 7.0s TTFT (warm) · 69 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	27.7 tok/s	3819 ms	131K
Coding	A	Runs well	27.7 tok/s	7001 ms	131K
Agentic Coding	A	Runs well	27.7 tok/s	10184 ms	131K
Reasoning	A	Runs well	27.7 tok/s	8274 ms	131K
RAG	A	Runs well	27.7 tok/s	12729 ms	131K

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 A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A71
Q3_K_S	3	17.2 GB	Low	A72
NVFP4	4	19.6 GB	Medium	A72
Q4_K_M	4	21.3 GB	Medium	A73
Q5_K_M	5	25.2 GB	High	A74
Q6_K	6	28.7 GB	High	A74
Q8_0Best for your GPU	8	37.5 GB	Very High	A74
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 A40 48GB can run

Model	Params	Grade	Decode
Qwen 2.5 VL 72B	72B	A	7.7 tok/s
Qwen3-Coder-Next	80B	A	19.9 tok/s
Llama 3.3 70B	70B	A	8.3 tok/s
Kimi Linear 48B A3B	48B	A	18.5 tok/s
Llama 3.1 70B	70B	B	8.3 tok/s

Frequently asked questions

Can NVIDIA A40 48GB run Command R 35B?

Yes, NVIDIA A40 48GB can run Command R 35B with a A grade (Runs well). Expected decode speed: 27.7 tok/s.

How much VRAM does Command R 35B need?

Command R 35B (35B parameters) requires approximately 29.5 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 A40 48GB?

On NVIDIA A40 48GB, Command R 35B achieves approximately 27.7 tokens per second decode speed with a time-to-first-token of 7001ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run Command R 35B for coding?

For coding workloads, Command R 35B on NVIDIA A40 48GB receives a A grade with 27.7 tok/s and 131K context.

What context window can Command R 35B use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, 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 A40 48GB See all hardware for Command R 35B

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