Mistral Small 3.2 24B needs ~14.2 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q2_K quantization, expect ~10 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
7.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.0 tok/s
TTFT
48822 ms
Safe context
4K
Memory
19.5 GB / 12.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 1.5 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 | 4.5 tok/s | 23239 ms | 4K |
| Coding | F | Too heavy | 4.0 tok/s | 48822 ms | 4K |
| Agentic Coding | F | Too heavy | 3.1 tok/s | 91050 ms | 4K |
| Reasoning | F | Too heavy | 4.0 tok/s | 57699 ms | 4K |
| RAG | F | Too heavy | 3.1 tok/s | 113812 ms | 4K |
How Mistral Small 3.2 24B (24B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | F0 |
Q3_K_S | 3 | 11.8 GB | Low | F0 |
NVFP4 | 4 | 13.4 GB | Medium | F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Copy-paste commands to run Mistral Small 3.2 24B on your machine.
Run
ollama run mistral-small3.2升级选项
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.
~$899 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.
~$999 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.
~$1,899 MSRP
Yes, RX 6700 XT 12GB can run Mistral Small 3.2 24B at Q2_K quantization (Very compromised (needs ~1.5 GB host RAM)). The recommended Q4_K_M requires 19.5 GB which exceeds available memory, but at Q2_K it needs only 14.2 GB. Expected decode speed: 10.3 tok/s.
Mistral Small 3.2 24B (24B parameters) requires approximately 19.5 GB at Q4_K_M quantization. On RX 6700 XT 12GB, it fits at Q2_K using 14.2 GB.
The recommended quantization is Q4_K_M, but on RX 6700 XT 12GB the best fitting quantization is Q2_K, which uses 14.2 GB.
On RX 6700 XT 12GB, Mistral Small 3.2 24B achieves approximately 10.3 tokens per second decode speed with a time-to-first-token of 18877ms using Q2_K quantization.
For coding workloads, Mistral Small 3.2 24B on RX 6700 XT 12GB receives a F grade with 4.0 tok/s and 4K context.
On RX 6700 XT 12GB, Mistral Small 3.2 24B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mistral-small-3.2-24b-on-rx-6700-xt-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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