Will It Run AI

Can Mixtral 8x7B run on RX 7900 XTX 24GB?

YES — With Q3_K_S

C50Usable
Estimated from fit model

Mixtral 8x7B needs ~28.3 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q3_K_S quantization, expect ~31 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.

Mixtral 8x7B at Q4_K_M needs 33.9 GB — too much for RX 7900 XTX 24GB (24.0 GB). Runs at Q3_K_S (28.3 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) 33.9 GB, exceeds 24.0 GB available
33.9 GB required24.0 GB available
141% VRAM needed

9.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

18.0 tok/s

TTFT

10757 ms

Safe context

4K

Memory

33.9 GB / 24.0 GB

Offload

30%

Memory breakdown

Weights28.7 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsMixtral 8x7B on RX 7900 XTX 24GB
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: 18.0 tok/s decode · 10.8s TTFT (warm) · 45 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 20% 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 3.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy19.1 tok/s5517 ms4K
CodingFToo heavy18.0 tok/s10757 ms4K
Agentic CodingFToo heavy16.0 tok/s17603 ms4K
ReasoningFToo heavy18.0 tok/s12712 ms4K
RAGFToo heavy16.0 tok/s22003 ms4K

Quantization options

How Mixtral 8x7B (47B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
18.3 GB
LowF0
Q3_K_S
3
23.0 GB
LowF0
NVFP4
4
26.3 GB
MediumF0
Q4_K_M
4
28.7 GB
MediumF0
Q5_K_M
5
33.8 GB
HighF0
Q6_K
6
38.5 GB
HighF0
Q8_0
8
50.3 GB
Very HighF0
F16
16
96.4 GB
MaximumF0

Get started

Copy-paste commands to run Mixtral 8x7B on your machine.

Run

ollama run mixtral

Opciones de mejora

Hardware que ejecuta bien Mixtral 8x7B

Frequently asked questions

Can RX 7900 XTX 24GB run Mixtral 8x7B?

Yes, RX 7900 XTX 24GB can run Mixtral 8x7B at Q3_K_S quantization (Very compromised (needs ~3.5 GB host RAM)). The recommended Q4_K_M requires 33.9 GB which exceeds available memory, but at Q3_K_S it needs only 28.3 GB. Expected decode speed: 30.6 tok/s.

How much VRAM does Mixtral 8x7B need?

Mixtral 8x7B (47B parameters) requires approximately 33.9 GB at Q4_K_M quantization. On RX 7900 XTX 24GB, it fits at Q3_K_S using 28.3 GB.

What is the best quantization for Mixtral 8x7B?

The recommended quantization is Q4_K_M, but on RX 7900 XTX 24GB the best fitting quantization is Q3_K_S, which uses 28.3 GB.

What speed will Mixtral 8x7B run at on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Mixtral 8x7B achieves approximately 30.6 tokens per second decode speed with a time-to-first-token of 6336ms using Q3_K_S quantization.

Can RX 7900 XTX 24GB run Mixtral 8x7B for coding?

For coding workloads, Mixtral 8x7B on RX 7900 XTX 24GB receives a F grade with 18.0 tok/s and 4K context.

What context window can Mixtral 8x7B use on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Mixtral 8x7B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Mixtral 8x7B feels slow on RX 7900 XTX 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 RX 7900 XTX 24GBSee all hardware for Mixtral 8x7B
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