Mixtral 8x7B needs ~23.5 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q2_K quantization, expect ~14 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
13.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.0 tok/s
TTFT
38624 ms
Safe context
4K
Memory
33.8 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 2.7 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 | 5.0 tok/s | 21293 ms | 4K |
| Coding | F | Too heavy | 4.7 tok/s | 41521 ms | 4K |
| Agentic Coding | F | Too heavy | 4.1 tok/s | 67970 ms | 4K |
| Reasoning | F | Too heavy | 4.7 tok/s | 49071 ms | 4K |
| RAG | F | Too heavy | 4.1 tok/s | 84963 ms | 4K |
How Mixtral 8x7B (47B params) fits at each quantization level on RTX 4000 Ada 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 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralUpgrade options
RTX 5090 32GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 998%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 588%.
~$2,499 MSRP
RTX A6000 48GBBudget pickMakes 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.
~$4,650 MSRP
Yes, RTX 4000 Ada 20GB can run Mixtral 8x7B at Q2_K quantization (Very compromised (needs ~2.7 GB host RAM)). The recommended Q4_K_M requires 33.8 GB which exceeds available memory, but at Q2_K it needs only 23.5 GB. Expected decode speed: 14.4 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 33.8 GB at Q4_K_M quantization. On RTX 4000 Ada 20GB, it fits at Q2_K using 23.5 GB.
The recommended quantization is Q4_K_M, but on RTX 4000 Ada 20GB the best fitting quantization is Q2_K, which uses 23.5 GB.
On RTX 4000 Ada 20GB, Mixtral 8x7B achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13478ms using Q2_K quantization.
For coding workloads, Mixtral 8x7B on RTX 4000 Ada 20GB receives a F grade with 4.7 tok/s and 4K context.
On RTX 4000 Ada 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.