VOOZH about

URL: https://willitrunai.com/can-run/command-r-35b-on-radeon-pro-w7900-ds-48gb

⇱ Command R 35B on Radeon PRO W7900 DS 48GB? YES

Can Command R 35B run on Radeon PRO W7900 DS 48GB?

YES — Runs Great

A77Great
○Estimated from fit model

Command R 35B needs ~29.5 GB VRAM. Radeon PRO W7900 DS 48GB has 48.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 29.5 GB, 26.0 tok/s, Runs well
29.5 GB required48.0 GB available
61% VRAM used

Fit status

Runs well

Decode

26.0 tok/s

TTFT

7456 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 Radeon PRO W7900 DS 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: 26.0 tok/s decode · 7.5s TTFT (warm) · 65 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well26.0 tok/s4067 ms131K
CodingARuns well26.0 tok/s7456 ms131K
Agentic CodingARuns well26.0 tok/s10845 ms131K
ReasoningARuns well26.0 tok/s8812 ms131K
RAGARuns well26.0 tok/s13556 ms131K

Quantization options

How Command R 35B (35B params) fits at each quantization level on Radeon PRO W7900 DS 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.7 GB
LowA71
Q3_K_S
3
17.2 GB
LowA72
NVFP4
4
19.6 GB
MediumA72
Q4_K_M
4
21.3 GB
MediumA73
Q5_K_M
5
25.2 GB
HighA74
Q6_K
6
28.7 GB
HighA74
Q8_0Best for your GPU
8
37.5 GB
Very HighA74
F16
16
71.8 GB
MaximumF0

Get started

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

Run

ollama run command-r

Your hardware

More models your Radeon PRO W7900 DS 48GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen 2.5 VL 72B		72BA7.2 tok/s
👁 Alibaba
Qwen3-Coder-Next		80BA18.7 tok/s
👁 Meta
Llama 3.3 70B		70BA7.8 tok/s
👁 Moonshot AI
Kimi Linear 48B A3B		48BA17.4 tok/s
👁 Meta
Llama 3.1 70B		70BB7.8 tok/s

Frequently asked questions

See all results for Radeon PRO W7900 DS 48GBSee all hardware for Command R 35B