Will It Run AI

Can Command R 35B run on RTX A4500 20GB?

YES — With Q3_K_S

B65Good
Estimated from fit model

Command R 35B needs ~22.5 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q3_K_S quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
Share:

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.

Command R 35B at Q4_K_M needs 26.7 GB — too much for RTX A4500 20GB (20.0 GB). Runs at Q3_K_S (22.5 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.7 GB, exceeds 20.0 GB available
26.7 GB required20.0 GB available
134% VRAM needed

6.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.4 tok/s

TTFT

18637 ms

Safe context

4K

Memory

26.7 GB / 20.0 GB

Offload

30%

Memory breakdown

Weights21.3 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsCommand R 35B on RTX A4500 20GB
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: 10.4 tok/s decode · 18.6s TTFT (warm) · 26 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 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy11.5 tok/s9212 ms4K
CodingFToo heavy10.4 tok/s18637 ms4K
Agentic CodingFToo heavy8.6 tok/s32593 ms4K
ReasoningFToo heavy10.4 tok/s22026 ms4K
RAGFToo heavy8.6 tok/s40741 ms4K

Quantization options

How Command R 35B (35B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
13.7 GB
LowA76
Q3_K_S
3
17.2 GB
LowF0
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

Upgrade-Optionen

Hardware, die Command R 35B gut ausführt

NVIDIARTX 3090 24GBBestes Preis-Leistungs-Verhältnis
24 GB VRAM (+4)936 GB/s (+296)
B
Makes the model fit on the accelerator instead of staying completely out of reach.12 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

ca. $1,499 MSRP

NVIDIARTX 5090 32GBGünstige Wahl
32 GB VRAM (+12)1792 GB/s (+1152)
A
Makes the model fit on the accelerator instead of staying completely out of reach.39.6 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,999 MSRP

NVIDIARTX A5500 24GBNVIDIA-Upgrade
24 GB VRAM (+4)768 GB/s (+128)
B
Makes the model fit on the accelerator instead of staying completely out of reach.17.7 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 70%.

ca. $3,200 MSRP

Frequently asked questions

Can RTX A4500 20GB run Command R 35B?

Yes, RTX A4500 20GB can run Command R 35B at Q3_K_S quantization (Very compromised (needs ~1.9 GB host RAM)). The recommended Q4_K_M requires 26.7 GB which exceeds available memory, but at Q3_K_S it needs only 22.5 GB. Expected decode speed: 17.2 tok/s.

How much VRAM does Command R 35B need?

Command R 35B (35B parameters) requires approximately 26.7 GB at Q4_K_M quantization. On RTX A4500 20GB, it fits at Q3_K_S using 22.5 GB.

What is the best quantization for Command R 35B?

The recommended quantization is Q4_K_M, but on RTX A4500 20GB the best fitting quantization is Q3_K_S, which uses 22.5 GB.

What speed will Command R 35B run at on RTX A4500 20GB?

On RTX A4500 20GB, Command R 35B achieves approximately 17.2 tokens per second decode speed with a time-to-first-token of 11227ms using Q3_K_S quantization.

Can RTX A4500 20GB run Command R 35B for coding?

For coding workloads, Command R 35B on RTX A4500 20GB receives a F grade with 10.4 tok/s and 4K context.

What context window can Command R 35B use on RTX A4500 20GB?

On RTX A4500 20GB, Command R 35B can safely use up to 4K tokens of context at Q3_K_S quantization. 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 A4500 20GB?

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 A4500 20GBSee all hardware for Command R 35B
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/command-r-35b-on-rtx-a4500-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: