Mixtral 8x7B needs ~33.4 GB but RTX 6000 Ada Laptop 16GB only has 16.0 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
17.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.8 tok/s
TTFT
40238 ms
Safe context
4K
Memory
33.4 GB / 16.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 33.4 GB, but this setup only exposes 16.0 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 | 5.1 tok/s | 20620 ms | 4K |
| Coding | F | Too heavy | 4.5 tok/s | 43255 ms | 4K |
| Agentic Coding | F | Too heavy | 4.5 tok/s | 62070 ms | 4K |
| Reasoning | F | Too heavy | 4.8 tok/s | 47554 ms | 4K |
| RAG | F | Too heavy | 4.5 tok/s | 77588 ms | 4K |
How Mixtral 8x7B (47B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 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 |
Upgrade options
RTX 5090 32GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 1044%.
~$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 617%.
~$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
No, Mixtral 8x7B requires more memory than RTX 6000 Ada Laptop 16GB provides.
Mixtral 8x7B (47B parameters) requires approximately 33.4 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 6000 Ada Laptop 16GB, Mixtral 8x7B achieves approximately 4.5 tokens per second decode speed with a time-to-first-token of 43255ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on RTX 6000 Ada Laptop 16GB receives a F grade with 4.5 tok/s and 4K context.
On RTX 6000 Ada Laptop 16GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-on-rtx-6000-ada-laptop-16gb" 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 |
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.
