Elimina 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 99%.
~$329 MSRP
DeepSeek R1 Distill 8B needs ~8.5 GB VRAM. RX 6600 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~19 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
18.5 tok/s
TTFT
10486 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.
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 | 28.2 tok/s | 3745 ms | 12K |
| Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 18.5 tok/s | 10486 ms | 12K |
| Agentic Coding | F | Too heavy | 12.0 tok/s | 23538 ms | 12K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 18.5 tok/s | 12393 ms | 12K |
| RAG | F | Too heavy | 12.0 tok/s | 29423 ms | 12K |
How DeepSeek R1 Distill 8B (8B params) fits at each quantization level on RX 6600 XT 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
Elimina 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 99%.
~$329 MSRP
Elimina 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 140%.
~$349 MSRP
Elimina 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 209%.
~$449 MSRP
Yes, RX 6600 XT 8GB can run DeepSeek R1 Distill 8B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 18.5 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 RX 6600 XT 8GB, DeepSeek R1 Distill 8B achieves approximately 18.5 tokens per second decode speed with a time-to-first-token of 10486ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 8B on RX 6600 XT 8GB receives a B grade with 18.5 tok/s and 12K context.
On RX 6600 XT 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.
<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-8b-on-rx-6600-xt-8gb" 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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