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DeepSeek R1 Distill 32B needs ~26.7 GB VRAM. NVIDIA A10 24GB has 24.0 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 GB host RAM)
Decode
15.5 tok/s
TTFT
12503 ms
Safe context
5K
Memory
26.7 GB / 24.0 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 | A | Runs with offload | 16.8 tok/s | 6278 ms | 5K |
| Coding | B | Very compromised | 14.3 tok/s | 13503 ms | 5K |
| Agentic Coding | F | Too heavy | 10.8 tok/s | 26174 ms | 5K |
| Reasoning | B | Very compromised | 14.3 tok/s | 15958 ms | 5K |
| RAG | F | Too heavy | 10.8 tok/s | 32718 ms | 5K |
How DeepSeek R1 Distill 32B (32B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | A76 |
Q3_K_S | 3 | 15.7 GB | Low | A75 |
NVFP4Best for your GPU |
Copy-paste commands to run DeepSeek R1 Distill 32B on your machine.
Run
ollama run deepseek-r1:32bUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 177%.
~$1,999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 168%.
~$2,499 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 65%.
~$4,000 MSRP
| 4 |
17.9 GB |
| Medium |
| A75 |
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 |
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.
