DeepSeek R1 Distill 8B needs ~8.5 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 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
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
38.1 tok/s
TTFT
5080 ms
Safe context
12K
Memory
8.5 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.
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 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 60.2 tok/s | 1754 ms | 12K |
| Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 38.1 tok/s | 5080 ms | 12K |
| Agentic Coding | F | Too heavy | 24.2 tok/s | 11619 ms | 12K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 38.1 tok/s | 6004 ms | 12K |
| RAG | F | Too heavy | 24.2 tok/s | 14523 ms | 12K |
How DeepSeek R1 Distill 8B (8B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A70 |
Q3_K_S | 3 | 3.9 GB | Low | B70 |
NVFP4 | 4 | 4.5 GB | Medium | B70 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | B69 |
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 |
Copy-paste commands to run DeepSeek R1 Distill 8B on your machine.
Run
ollama run deepseek-r1:8bOpciones de mejora
RTX 3060 12GBOpción económicaElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
RTX 5060 Ti 16GBMejor relación calidad-precioElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 61%.
~$449 MSRP
RTX 4060 Ti 16GBMejora NVIDIAElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$499 MSRP
Yes, RTX 2070 Super 8GB can run DeepSeek R1 Distill 8B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 38.1 tok/s.
DeepSeek R1 Distill 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill 8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2070 Super 8GB, DeepSeek R1 Distill 8B achieves approximately 38.1 tokens per second decode speed with a time-to-first-token of 5080ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 8B on RTX 2070 Super 8GB receives a B grade with 38.1 tok/s and 12K context.
On RTX 2070 Super 8GB, DeepSeek R1 Distill 8B can safely use up to 12K tokens of context. 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.
Paste this snippet into any page to show a live fit card.
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