Pixtral 12B needs ~8.8 GB VRAM. RX 580 8GB has 8.0 GB. With Q2_K quantization, expect ~13 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
3.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.4 tok/s
TTFT
36160 ms
Safe context
4K
Memory
11.5 GB / 8.0 GB
Offload
30%
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | F | Too heavy | 6.9 tok/s | 15402 ms | 4K |
| Coding | F | Too heavy | 5.4 tok/s | 36160 ms | 4K |
| Agentic Coding | F | Too heavy | 3.5 tok/s | 80375 ms | 4K |
| Reasoning | F | Too heavy | 5.4 tok/s | 42734 ms | 4K |
| RAG | F | Too heavy | 3.5 tok/s | 100469 ms | 4K |
How Pixtral 12B (12B params) fits at each quantization level on RX 580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 4.7 GB | Low | A77 |
Q3_K_S | 3 | 5.9 GB | Low | F0 |
Copy-paste commands to run Pixtral 12B on your machine.
Run
ollama run pixtralUpgrade options
Makes 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.
~$329 MSRP
Makes 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.
~$349 MSRP
Makes 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.
~$449 MSRP
Yes, RX 580 8GB can run Pixtral 12B at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 11.5 GB which exceeds available memory, but at Q2_K it needs only 8.8 GB. Expected decode speed: 12.6 tok/s.
Pixtral 12B (12B parameters) requires approximately 11.5 GB at Q4_K_M quantization. On RX 580 8GB, it fits at Q2_K using 8.8 GB.
The recommended quantization is Q4_K_M, but on RX 580 8GB the best fitting quantization is Q2_K, which uses 8.8 GB.
On RX 580 8GB, Pixtral 12B achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15306ms using Q2_K quantization.
For coding workloads, Pixtral 12B on RX 580 8GB receives a F grade with 5.4 tok/s and 4K context.
On RX 580 8GB, Pixtral 12B can safely use up to 11K tokens of context at Q2_K quantization. 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/pixtral-12b-on-rx-580-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
6.7 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 7.3 GB | Medium | F0 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 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.