Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on RX 6950 XT 16GB?

Q: What should I upgrade first if cognitivecomputations Dolphin3.0 R1 Mistral 24B feels slow on RX 6950 XT 16GB?

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.

YES — With NVFP4

D38Poor

Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~18.8 GB VRAM. RX 6950 XT 16GB has 16.0 GB. With NVFP4 quantization, expect ~14 tok/s.

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

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.

cognitivecomputations Dolphin3.0 R1 Mistral 24B at Q4_K_M needs 20.0 GB — too much for RX 6950 XT 16GB (16.0 GB). Runs at NVFP4 (18.8 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 20.0 GB, exceeds 16.0 GB available

20.0 GB required16.0 GB available

125% VRAM needed

4.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.8 tok/s

TTFT

18003 ms

Safe context

Memory

20.0 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on RX 6950 XT 16GB
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: 10.8 tok/s decode · 18.0s TTFT (warm) · 27 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 2.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Very compromised (needs ~2 GB host RAM)	12.5 tok/s	8420 ms	4K
Coding	F	Too heavy	10.8 tok/s	18003 ms	4K
Agentic Coding	F	Too heavy	8.1 tok/s	34565 ms	4K
Reasoning	F	Too heavy	10.8 tok/s	21277 ms	4K
RAG	F	Too heavy	8.1 tok/s	43206 ms	4K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on RX 6950 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C51
Q3_K_SBest for your GPU	3	11.8 GB	Low	C51

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

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.23.7 tok/s decode

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.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+384)

Makes the model fit on the accelerator instead of staying completely out of reach.47.2 tok/s decode

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.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.25.8 tok/s decode

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.

~$1,899 MSRP

Frequently asked questions

Can RX 6950 XT 16GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?

Yes, RX 6950 XT 16GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B at NVFP4 quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 20.0 GB which exceeds available memory, but at NVFP4 it needs only 18.8 GB. Expected decode speed: 14.0 tok/s.

How much VRAM does cognitivecomputations Dolphin3.0 R1 Mistral 24B need?

cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 20.0 GB at Q4_K_M quantization. On RX 6950 XT 16GB, it fits at NVFP4 using 18.8 GB.

What is the best quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B?

The recommended quantization is Q4_K_M, but on RX 6950 XT 16GB the best fitting quantization is NVFP4, which uses 18.8 GB.

What speed will cognitivecomputations Dolphin3.0 R1 Mistral 24B run at on RX 6950 XT 16GB?

On RX 6950 XT 16GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13814ms using NVFP4 quantization.

Can RX 6950 XT 16GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?

For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on RX 6950 XT 16GB receives a F grade with 10.8 tok/s and 4K context.

What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on RX 6950 XT 16GB?

See all results for RX 6950 XT 16GB See all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B

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