Mixtral 8x7B needs ~32.6 GB but RTX 3070 Ti 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
4.7 tok/s
TTFT
40989 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.
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 | 4.7 tok/s | 22358 ms | 4K |
| Coding | F | Too heavy | 4.7 tok/s | 40989 ms | 4K |
| Agentic Coding | F | Too heavy | 4.7 tok/s | 59620 ms | 4K |
| Reasoning | F | Too heavy | 4.7 tok/s | 48441 ms | 4K |
| RAG | F | Too heavy | 4.7 tok/s | 74525 ms | 4K |
How Mixtral 8x7B (47B params) fits at each quantization level on RTX 3070 Ti 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 |
Opções de upgrade
RTX 5090 32GBMelhor custo-benefícioMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 1068%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBUpgrade NVIDIAMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 632%.
~$2,499 MSRP
RTX A6000 48GBOpção econômicaMakes 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
No, Mixtral 8x7B requires more memory than RTX 3070 Ti 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 RTX 3070 Ti 8GB, Mixtral 8x7B achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 40989ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on RTX 3070 Ti 8GB receives a F grade with 4.7 tok/s and 4K context.
On RTX 3070 Ti 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-rtx-3070-ti-8gb" 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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