Will It Run AI

Can Command R 35B run on RTX 5090 Laptop 24GB?

BARELY — Tight on Memory

B65Good
Estimated from fit model

Command R 35B needs ~27.1 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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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) — 27.1 GB, 22.3 tok/s, Very compromised (needs ~2.4 GB host RAM)
27.1 GB required24.0 GB available
113% VRAM needed

3.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.4 GB host RAM)

Decode

22.3 tok/s

TTFT

8689 ms

Safe context

4K

Memory

27.1 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights21.3 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsCommand R 35B on RTX 5090 Laptop 24GB
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: 22.3 tok/s decode · 8.7s TTFT (warm) · 56 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload22.6 tok/s4678 ms4K
CodingBVery compromised20.5 tok/s9450 ms4K
Agentic CodingFToo heavy17.1 tok/s16483 ms4K
ReasoningBVery compromised20.5 tok/s11168 ms4K
RAGFToo heavy17.1 tok/s20603 ms4K

Quantization options

How Command R 35B (35B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.7 GB
LowA76
Q3_K_SBest for your GPU
3
17.2 GB
LowA76
NVFP4
4
19.6 GB
MediumF0
Q4_K_M
4
21.3 GB
MediumF0
Q5_K_M
5
25.2 GB
HighF0
Q6_K
6
28.7 GB
HighF0
Q8_0
8
37.5 GB
Very HighF0
F16
16
71.8 GB
MaximumF0

Get started

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

Run

ollama run command-r

Opciones de mejora

Hardware que ejecuta bien Command R 35B

NVIDIARTX 5090 32GBOpción económica
32 GB VRAM (+8)1792 GB/s (+896)
A
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.39.6 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 78%.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMejor relación calidad-precio
32 GB VRAM (+8)
A
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.38.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 72%.

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBMejora NVIDIA
32 GB VRAM (+8)
A
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.23.5 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can RTX 5090 Laptop 24GB run Command R 35B?

Yes, RTX 5090 Laptop 24GB can run Command R 35B with a B grade (Very compromised). Expected decode speed: 20.5 tok/s.

How much VRAM does Command R 35B need?

Command R 35B (35B parameters) requires approximately 27.1 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 RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Command R 35B achieves approximately 20.5 tokens per second decode speed with a time-to-first-token of 9450ms using Q4_K_M quantization.

Can RTX 5090 Laptop 24GB run Command R 35B for coding?

For coding workloads, Command R 35B on RTX 5090 Laptop 24GB receives a B grade with 20.5 tok/s and 4K context.

What context window can Command R 35B use on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Command R 35B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Command R 35B feels slow on RTX 5090 Laptop 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 5090 Laptop 24GBSee all hardware for Command R 35B
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