DeepSeek R1 Distill 32B needs ~22.5 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q3_K_S quantization, expect ~18 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
6.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
11.2 tok/s
TTFT
17334 ms
Safe context
4K
Memory
26.3 GB / 20.0 GB
Offload
20%
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 1.7 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 | 13.1 tok/s | 8039 ms | 4K |
| Coding | F | Too heavy | 11.2 tok/s | 17334 ms | 4K |
| Agentic Coding | F | Too heavy | 8.3 tok/s | 33738 ms | 4K |
| Reasoning | F | Too heavy | 11.2 tok/s | 20486 ms | 4K |
| RAG | F | Too heavy | 8.3 tok/s | 42172 ms | 4K |
How DeepSeek R1 Distill 32B (32B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 12.5 GB | Low | A76 |
Q3_K_S | 3 | 15.7 GB | Low | F0 |
NVFP4 | 4 | 17.9 GB | Medium | F0 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Copy-paste commands to run DeepSeek R1 Distill 32B on your machine.
Run
ollama run deepseek-r1:32bOpciones de mejora
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 104%.
~$999 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,899 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$2,249 MSRP
NVIDIA A100 40GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$10,000 MSRP
Yes, RX 7900 XT 20GB can run DeepSeek R1 Distill 32B at Q3_K_S quantization (Very compromised (needs ~1.7 GB host RAM)). The recommended Q4_K_M requires 26.3 GB which exceeds available memory, but at Q3_K_S it needs only 22.5 GB. Expected decode speed: 18.0 tok/s.
DeepSeek R1 Distill 32B (32B parameters) requires approximately 26.3 GB at Q4_K_M quantization. On RX 7900 XT 20GB, it fits at Q3_K_S using 22.5 GB.
The recommended quantization is Q4_K_M, but on RX 7900 XT 20GB the best fitting quantization is Q3_K_S, which uses 22.5 GB.
On RX 7900 XT 20GB, DeepSeek R1 Distill 32B achieves approximately 18.0 tokens per second decode speed with a time-to-first-token of 10744ms using Q3_K_S quantization.
For coding workloads, DeepSeek R1 Distill 32B on RX 7900 XT 20GB receives a F grade with 11.2 tok/s and 4K context.
On RX 7900 XT 20GB, DeepSeek R1 Distill 32B can safely use up to 6K tokens of context at Q3_K_S quantization. The model's official context limit is 33K, 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.
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<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-32b-on-rx-7900-xt-20gb" 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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