Mixtral 8x7B needs ~32.6 GB but GTX 1080 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
24.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
95038 ms
Safe context
4K
Memory
32.6 GB / 8.0 GB
Offload
80%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 32.6 GB, but this setup only exposes 8.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 51839 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 95038 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 138238 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 112318 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 172797 ms | 4K |
How Mixtral 8x7B (47B params) fits at each quantization level on GTX 1080 8GB (8.0 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 |
Upgrade-Optionen
RTX 5090 32GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 2645%.
ca. $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 1620%.
ca. $2,499 MSRP
RTX A6000 48GBGünstige WahlMakes 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. $4,650 MSRP
No, Mixtral 8x7B requires more memory than GTX 1080 8GB provides.
Mixtral 8x7B (47B parameters) requires approximately 32.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.
On GTX 1080 8GB, Mixtral 8x7B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 95038ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on GTX 1080 8GB receives a F grade with 2.0 tok/s and 4K context.
On GTX 1080 8GB, Mixtral 8x7B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-on-gtx-1080-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
