Mixtral 8x7B needs ~34.7 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~35 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
2.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.2 GB host RAM)
Decode
35.0 tok/s
TTFT
5524 ms
Safe context
4K
Memory
34.7 GB / 32.0 GB
Offload
10%
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 2.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~1.5 GB host RAM) | 37.1 tok/s | 2843 ms | 4K |
| Coding | B | Very compromised (needs ~2.2 GB host RAM) | 35.0 tok/s | 5524 ms | 4K |
| Agentic Coding | B | Very compromised (needs ~3.7 GB host RAM) | 31.3 tok/s | 8984 ms | 4K |
| Reasoning | B | Very compromised (needs ~2.2 GB host RAM) | 35.0 tok/s | 6529 ms | 4K |
| RAG | B | Very compromised (needs ~3.7 GB host RAM) | 31.3 tok/s |
How Mixtral 8x7B (47B params) fits at each quantization level on RTX PRO 4500 Blackwell 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | B65 |
Q3_K_SBest for your GPU | 3 | 23.0 GB | Low | B64 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralUpgrade options
RTX A6000 48GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 132%.
~$4,999 MSRP
NVIDIA L40 48GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$5,500 MSRP
Yes, RTX PRO 4500 Blackwell 32GB can run Mixtral 8x7B with a B grade (Very compromised (needs ~2.2 GB host RAM)). Expected decode speed: 35.0 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 34.7 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 PRO 4500 Blackwell 32GB, Mixtral 8x7B achieves approximately 35.0 tokens per second decode speed with a time-to-first-token of 5524ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on RTX PRO 4500 Blackwell 32GB receives a B grade with 35.0 tok/s and 4K context.
On RTX PRO 4500 Blackwell 32GB, 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-pro-4500-blackwell-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 11230 ms |
| 4K |
| 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 |
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.