Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 102%.
ca. $1,899 MSRP
Mixtral 8x7B needs ~23.2 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q2_K quantization, expect ~25 tok/s.
Operating mode
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.
Select quantization to explore
13.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.7 tok/s
TTFT
22175 ms
Safe context
4K
Memory
33.5 GB / 20.0 GB
Offload
40%
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.
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 2.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 9.3 tok/s | 11366 ms | 4K |
| Coding | F | Too heavy | 8.7 tok/s | 22175 ms | 4K |
| Agentic Coding | F | Too heavy | 7.7 tok/s | 36339 ms | 4K |
| Reasoning | F | Too heavy | 8.7 tok/s | 26207 ms | 4K |
| RAG | F | Too heavy | 7.7 tok/s | 45423 ms | 4K |
How Mixtral 8x7B (47B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | F0 |
Q3_K_S | 3 | 23.0 GB | Low | F0 |
NVFP4 | 4 | 26.3 GB | Medium | F0 |
Q4_K_M | 4 | 28.7 GB | Medium | F0 |
Q5_K_M | 5 | 33.8 GB | High | F0 |
Q6_K | 6 | 38.5 GB | High | F0 |
Q8_0 | 8 | 50.3 GB | Very High | F0 |
F16 | 16 | 96.4 GB | Maximum | F0 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralUpgrade-Optionen
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 102%.
ca. $1,899 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 49%.
ca. $2,249 MSRP
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. $3,999 MSRP
Yes, RX 7900 XT 20GB can run Mixtral 8x7B at Q2_K quantization (Very compromised (needs ~2.5 GB host RAM)). The recommended Q4_K_M requires 33.5 GB which exceeds available memory, but at Q2_K it needs only 23.2 GB. Expected decode speed: 25.2 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 33.5 GB at Q4_K_M quantization. On RX 7900 XT 20GB, it fits at Q2_K using 23.2 GB.
The recommended quantization is Q4_K_M, but on RX 7900 XT 20GB the best fitting quantization is Q2_K, which uses 23.2 GB.
On RX 7900 XT 20GB, Mixtral 8x7B achieves approximately 25.2 tokens per second decode speed with a time-to-first-token of 7674ms using Q2_K quantization.
For coding workloads, Mixtral 8x7B on RX 7900 XT 20GB receives a F grade with 8.7 tok/s and 4K context.
On RX 7900 XT 20GB, Mixtral 8x7B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 33K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-on-rx-7900-xt-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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