Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on Radeon Pro W7900 48GB?

YES — Runs Great

C49Usable

○Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~23.2 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 23.2 GB, 34.8 tok/s, Runs well

23.2 GB required48.0 GB available

48% VRAM used

Fit status

Runs well

Decode

34.8 tok/s

TTFT

5560 ms

Safe context

157K

Memory

23.2 GB / 48.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on Radeon Pro W7900 48GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 34.8 tok/s decode · 5.6s TTFT (warm) · 87 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	C	Runs well	34.8 tok/s	3033 ms	157K
Coding	C	Runs well	34.8 tok/s	5560 ms	157K
Agentic Coding	C	Runs well	34.8 tok/s	8087 ms	157K
Reasoning	C	Runs well	34.8 tok/s	6571 ms	157K
RAG	C	Runs well	34.8 tok/s	10109 ms	157K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C43
Q3_K_S	3	11.8 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run cognitivecomputations Dolphin3.0 R1 Mistral 24B on your machine.

Run

lms load hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf && lms server start

Upgrade options

Hardware that runs cognitivecomputations Dolphin3.0 R1 Mistral 24B well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+774)

Raises estimated decode speed by about 119%.76.1 tok/s decode

Raises estimated decode speed by about 119%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

See all results for Radeon Pro W7900 48GB See all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B

URL: https://willitrunai.com/can-run/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-radeon-pro-w7900-48gb

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