Mixtral 8x7B needs ~34.7 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~14 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
15.4 tok/s
TTFT
12541 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~1.5 GB host RAM) | 16.4 tok/s | 6442 ms | 4K |
| Coding | C | Very compromised | 14.4 tok/s | 13481 ms | 4K |
| Agentic Coding | C | Very compromised (needs ~3.7 GB host RAM) | 13.8 tok/s | 20468 ms | 4K |
| Reasoning | C | Very compromised (needs ~2.2 GB host RAM) | 15.4 tok/s | 14821 ms | 4K |
| RAG | C | Very compromised | 12.8 tok/s | 27504 ms |
How Mixtral 8x7B (47B params) fits at each quantization level on Radeon Pro W7800 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
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 138%.
~$3,999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 138%.
~$3,999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 2628%.
~$8,000 MSRP
Yes, Radeon Pro W7800 32GB can run Mixtral 8x7B with a C grade (Very compromised). Expected decode speed: 14.4 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 Radeon Pro W7800 32GB, Mixtral 8x7B achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13481ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on Radeon Pro W7800 32GB receives a C grade with 14.4 tok/s and 4K context.
On Radeon Pro W7800 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-radeon-pro-w7800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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.