Mixtral 8x7B needs ~28.6 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q3_K_S quantization, expect ~32 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
10.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
18.6 tok/s
TTFT
10408 ms
Safe context
4K
Memory
34.2 GB / 24.0 GB
Offload
30%
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.
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 3.7 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 | 18.4 tok/s | 5742 ms | 4K |
| Coding | F | Too heavy | 17.3 tok/s | 11188 ms | 4K |
| Agentic Coding | F | Too heavy | 15.4 tok/s | 18291 ms | 4K |
| Reasoning | F | Too heavy | 17.3 tok/s | 13222 ms | 4K |
| RAG | F | Too heavy | 15.4 tok/s | 22863 ms | 4K |
How Mixtral 8x7B (47B params) fits at each quantization level on NVIDIA A30 24GB (24.0 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 195%.
~$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 85%.
~$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, NVIDIA A30 24GB can run Mixtral 8x7B at Q3_K_S quantization (Very compromised (needs ~3.7 GB host RAM)). The recommended Q4_K_M requires 34.2 GB which exceeds available memory, but at Q3_K_S it needs only 28.6 GB. Expected decode speed: 31.5 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 34.2 GB at Q4_K_M quantization. On NVIDIA A30 24GB, it fits at Q3_K_S using 28.6 GB.
The recommended quantization is Q4_K_M, but on NVIDIA A30 24GB the best fitting quantization is Q3_K_S, which uses 28.6 GB.
On NVIDIA A30 24GB, Mixtral 8x7B achieves approximately 31.5 tokens per second decode speed with a time-to-first-token of 6153ms using Q3_K_S quantization.
For coding workloads, Mixtral 8x7B on NVIDIA A30 24GB receives a F grade with 17.3 tok/s and 4K context.
On NVIDIA A30 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-on-a30-24gb" 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.