Ministral 8B needs ~8.8 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.4 GB host RAM)
Decode
23.1 tok/s
TTFT
8383 ms
Safe context
10K
Memory
8.8 GB / 8.0 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 0.4 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 | 37.4 tok/s | 2821 ms | 10K |
| Coding | C | Very compromised (needs ~0.4 GB host RAM) | 23.1 tok/s | 8383 ms | 10K |
| Agentic Coding | F | Too heavy | 14.4 tok/s | 19514 ms | 10K |
| Reasoning | C | Very compromised (needs ~0.4 GB host RAM) | 23.1 tok/s | 9907 ms | 10K |
| RAG | F | Too heavy | 14.4 tok/s | 24393 ms |
How Ministral 8B (8B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B63 |
Q3_K_S | 3 | 3.9 GB | Low | B63 |
NVFP4 | 4 |
Copy-paste commands to run Ministral 8B on your machine.
Run
ollama run ministralUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 59%.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 92%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 147%.
~$449 MSRP
Yes, Radeon PRO W7600 8GB can run Ministral 8B with a C grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 23.1 tok/s.
Ministral 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 8B is Q4_K_M, which balances quality and memory efficiency.
On Radeon PRO W7600 8GB, Ministral 8B achieves approximately 23.1 tokens per second decode speed with a time-to-first-token of 8383ms using Q4_K_M quantization.
For coding workloads, Ministral 8B on Radeon PRO W7600 8GB receives a C grade with 23.1 tok/s and 10K context.
On Radeon PRO W7600 8GB, Ministral 8B can safely use up to 10K tokens of context. The model's official context limit is 131K, 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/ministral-8b-on-radeon-pro-w7600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 10K |
| Medium |
| B63 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | B62 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 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.